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2013


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Learning People Detectors for Tracking in Crowded Scenes

Tang, S., Andriluka, M., Milan, A., Schindler, K., Roth, S., Schiele, B.

In 2013 IEEE International Conference on Computer Vision, pages: 1049-1056, IEEE, IEEE International Conference on Computer Vision, December 2013 (inproceedings)

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PDF DOI [BibTex]

2013


PDF DOI [BibTex]


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Branch&Rank for Efficient Object Detection

Lehmann, A., Gehler, P., VanGool, L.

International Journal of Computer Vision, Springer, December 2013 (article)

Abstract
Ranking hypothesis sets is a powerful concept for efficient object detection. In this work, we propose a branch&rank scheme that detects objects with often less than 100 ranking operations. This efficiency enables the use of strong and also costly classifiers like non-linear SVMs with RBF-TeX kernels. We thereby relieve an inherent limitation of branch&bound methods as bounds are often not tight enough to be effective in practice. Our approach features three key components: a ranking function that operates on sets of hypotheses and a grouping of these into different tasks. Detection efficiency results from adaptively sub-dividing the object search space into decreasingly smaller sets. This is inherited from branch&bound, while the ranking function supersedes a tight bound which is often unavailable (except for rather limited function classes). The grouping makes the system effective: it separates image classification from object recognition, yet combines them in a single formulation, phrased as a structured SVM problem. A novel aspect of branch&rank is that a better ranking function is expected to decrease the number of classifier calls during detection. We use the VOC’07 dataset to demonstrate the algorithmic properties of branch&rank.

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pdf link (url) [BibTex]

pdf link (url) [BibTex]


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Strong Appearance and Expressive Spatial Models for Human Pose Estimation

Pishchulin, L., Andriluka, M., Gehler, P., Schiele, B.

In International Conference on Computer Vision (ICCV), pages: 3487 - 3494 , IEEE, Computer Vision (ICCV), IEEE International Conference on , December 2013 (inproceedings)

Abstract
Typical approaches to articulated pose estimation combine spatial modelling of the human body with appearance modelling of body parts. This paper aims to push the state-of-the-art in articulated pose estimation in two ways. First we explore various types of appearance representations aiming to substantially improve the body part hypotheses. And second, we draw on and combine several recently proposed powerful ideas such as more flexible spatial models as well as image-conditioned spatial models. In a series of experiments we draw several important conclusions: (1) we show that the proposed appearance representations are complementary; (2) we demonstrate that even a basic tree-structure spatial human body model achieves state-of-the-art performance when augmented with the proper appearance representation; and (3) we show that the combination of the best performing appearance model with a flexible image-conditioned spatial model achieves the best result, significantly improving over the state of the art, on the "Leeds Sports Poses'' and "Parse'' benchmarks.

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pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]


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Methods and Applications for Distance Based ANN Training

Lassner, C., Lienhart, R.

In IEEE International Conference on Machine Learning and Applications (ICMLA), December 2013 (inproceedings)

Abstract
Feature learning has the aim to take away the hassle of hand-designing features for machine learning tasks. Since the feature design process is tedious and requires a lot of experience, an automated solution is of great interest. However, an important problem in this field is that usually no objective values are available to fit a feature learning function to. Artificial Neural Networks are a sufficiently flexible tool for function approximation to be able to avoid this problem. We show how the error function of an ANN can be modified such that it works solely with objective distances instead of objective values. We derive the adjusted rules for backpropagation through networks with arbitrary depths and include practical considera- tions that must be taken into account to apply difference based learning successfully. On all three benchmark datasets we use, linear SVMs trained on automatically learned ANN features outperform RBF kernel SVMs trained on the raw data. This can be achieved in a feature space with up to only a tenth of dimensions of the number of original data dimensions. We conclude our work with two experiments on distance based ANN training in two further fields: data visualization and outlier detection.

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pdf [BibTex]

pdf [BibTex]


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Extracting Postural Synergies for Robotic Grasping

Romero, J., Feix, T., Ek, C., Kjellstrom, H., Kragic, D.

Robotics, IEEE Transactions on, 29(6):1342-1352, December 2013 (article)

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[BibTex]

[BibTex]


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Understanding High-Level Semantics by Modeling Traffic Patterns

Zhang, H., Geiger, A., Urtasun, R.

In International Conference on Computer Vision, pages: 3056-3063, Sydney, Australia, December 2013 (inproceedings)

Abstract
In this paper, we are interested in understanding the semantics of outdoor scenes in the context of autonomous driving. Towards this goal, we propose a generative model of 3D urban scenes which is able to reason not only about the geometry and objects present in the scene, but also about the high-level semantics in the form of traffic patterns. We found that a small number of patterns is sufficient to model the vast majority of traffic scenes and show how these patterns can be learned. As evidenced by our experiments, this high-level reasoning significantly improves the overall scene estimation as well as the vehicle-to-lane association when compared to state-of-the-art approaches. All data and code will be made available upon publication.

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pdf [BibTex]

pdf [BibTex]


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A Non-parametric Bayesian Network Prior of Human Pose

Lehrmann, A. M., Gehler, P., Nowozin, S.

In Proceedings IEEE Conf. on Computer Vision (ICCV), pages: 1281-1288, IEEE International Conference on Computer Vision, December 2013 (inproceedings)

Abstract
Having a sensible prior of human pose is a vital ingredient for many computer vision applications, including tracking and pose estimation. While the application of global non-parametric approaches and parametric models has led to some success, finding the right balance in terms of flexibility and tractability, as well as estimating model parameters from data has turned out to be challenging. In this work, we introduce a sparse Bayesian network model of human pose that is non-parametric with respect to the estimation of both its graph structure and its local distributions. We describe an efficient sampling scheme for our model and show its tractability for the computation of exact log-likelihoods. We empirically validate our approach on the Human 3.6M dataset and demonstrate superior performance to global models and parametric networks. We further illustrate our model's ability to represent and compose poses not present in the training set (compositionality) and describe a speed-accuracy trade-off that allows realtime scoring of poses.

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Project page pdf DOI Project Page [BibTex]

Project page pdf DOI Project Page [BibTex]


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Towards understanding action recognition

Jhuang, H., Gall, J., Zuffi, S., Schmid, C., Black, M. J.

In IEEE International Conference on Computer Vision (ICCV), pages: 3192-3199, IEEE, Sydney, Australia, December 2013 (inproceedings)

Abstract
Although action recognition in videos is widely studied, current methods often fail on real-world datasets. Many recent approaches improve accuracy and robustness to cope with challenging video sequences, but it is often unclear what affects the results most. This paper attempts to provide insights based on a systematic performance evaluation using thoroughly-annotated data of human actions. We annotate human Joints for the HMDB dataset (J-HMDB). This annotation can be used to derive ground truth optical flow and segmentation. We evaluate current methods using this dataset and systematically replace the output of various algorithms with ground truth. This enables us to discover what is important – for example, should we work on improving flow algorithms, estimating human bounding boxes, or enabling pose estimation? In summary, we find that highlevel pose features greatly outperform low/mid level features; in particular, pose over time is critical, but current pose estimation algorithms are not yet reliable enough to provide this information. We also find that the accuracy of a top-performing action recognition framework can be greatly increased by refining the underlying low/mid level features; this suggests it is important to improve optical flow and human detection algorithms. Our analysis and JHMDB dataset should facilitate a deeper understanding of action recognition algorithms.

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Website Errata Poster Paper Slides DOI Project Page Project Page Project Page [BibTex]

Website Errata Poster Paper Slides DOI Project Page Project Page Project Page [BibTex]


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Mixing Decoded Cursor Velocity and Position from an Offline Kalman Filter Improves Cursor Control in People with Tetraplegia

Homer, M., Harrison, M., Black, M. J., Perge, J., Cash, S., Friehs, G., Hochberg, L.

In 6th International IEEE EMBS Conference on Neural Engineering, pages: 715-718, San Diego, November 2013 (inproceedings)

Abstract
Kalman filtering is a common method to decode neural signals from the motor cortex. In clinical research investigating the use of intracortical brain computer interfaces (iBCIs), the technique enabled people with tetraplegia to control assistive devices such as a computer or robotic arm directly from their neural activity. For reaching movements, the Kalman filter typically estimates the instantaneous endpoint velocity of the control device. Here, we analyzed attempted arm/hand movements by people with tetraplegia to control a cursor on a computer screen to reach several circular targets. A standard velocity Kalman filter is enhanced to additionally decode for the cursor’s position. We then mix decoded velocity and position to generate cursor movement commands. We analyzed data, offline, from two participants across six sessions. Root mean squared error between the actual and estimated cursor trajectory improved by 12.2 ±10.5% (pairwise t-test, p<0.05) as compared to a standard velocity Kalman filter. The findings suggest that simultaneously decoding for intended velocity and position and using them both to generate movement commands can improve the performance of iBCIs.

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pdf Project Page [BibTex]

pdf Project Page [BibTex]


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Markov Random Field Modeling, Inference & Learning in Computer Vision & Image Understanding: A Survey

Wang, C., Komodakis, N., Paragios, N.

Computer Vision and Image Understanding (CVIU), 117(11):1610-1627, November 2013 (article)

Abstract
In this paper, we present a comprehensive survey of Markov Random Fields (MRFs) in computer vision and image understanding, with respect to the modeling, the inference and the learning. While MRFs were introduced into the computer vision field about two decades ago, they started to become a ubiquitous tool for solving visual perception problems around the turn of the millennium following the emergence of efficient inference methods. During the past decade, a variety of MRF models as well as inference and learning methods have been developed for addressing numerous low, mid and high-level vision problems. While most of the literature concerns pairwise MRFs, in recent years we have also witnessed significant progress in higher-order MRFs, which substantially enhances the expressiveness of graph-based models and expands the domain of solvable problems. This survey provides a compact and informative summary of the major literature in this research topic.

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Publishers site pdf [BibTex]

Publishers site pdf [BibTex]


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Camera-specific Image Denoising

Schober, M.

Eberhard Karls Universität Tübingen, Germany, October 2013 (diplomathesis)

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PDF [BibTex]

PDF [BibTex]


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Multi-robot cooperative spherical-object tracking in 3D space based on particle filters

Ahmad, A., Lima, P.

Robotics and Autonomous Systems, 61(10):1084-1093, October 2013 (article)

Abstract
This article presents a cooperative approach for tracking a moving spherical object in 3D space by a team of mobile robots equipped with sensors, in a highly dynamic environment. The tracker’s core is a particle filter, modified to handle, within a single unified framework, the problem of complete or partial occlusion for some of the involved mobile sensors, as well as inconsistent estimates in the global frame among sensors, due to observation errors and/or self-localization uncertainty. We present results supporting our approach by applying it to a team of real soccer robots tracking a soccer ball, including comparison with ground truth.

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DOI [BibTex]

DOI [BibTex]


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Multi-Robot Cooperative Object Tracking Based on Particle Filters

Ahmad, A., Lima, P.

In 61(10):1084-1093, 5th European Conference on Mobile Robots (ECMR), October 2013 (inproceedings)

Abstract
This article presents a cooperative approach for tracking a moving object by a team of mobile robots equipped with sensors, in a highly dynamic environment. The tracker’s core is a particle filter, modified to handle, within a single unified framework, the problem of complete or partial occlusion for some of the involved mobile sensors, as well as inconsistent estimates in the global frame among sensors, due to observation errors and/or self-localization uncertainty. We present results supporting our approach by applying it to a team of real soccer robots tracking a soccer ball.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Puppet Flow

Zuffi, S., Black, M. J.

(7), Max Planck Institute for Intelligent Systems, October 2013 (techreport)

Abstract
We introduce Puppet Flow (PF), a layered model describing the optical flow of a person in a video sequence. We consider video frames composed by two layers: a foreground layer corresponding to a person, and background. We model the background as an affine flow field. The foreground layer, being a moving person, requires reasoning about the articulated nature of the human body. We thus represent the foreground layer with the Deformable Structures model (DS), a parametrized 2D part-based human body representation. We call the motion field defined through articulated motion and deformation of the DS model, a Puppet Flow. By exploiting the DS representation, Puppet Flow is a parametrized optical flow field, where parameters are the person's pose, gender and body shape.

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pdf Project Page Project Page [BibTex]

pdf Project Page Project Page [BibTex]


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D2.1.4 RoCKIn@Work - Innovation in Mobile Industrial Manipulation Competition Design, Rule Book, and Scenario Construction

Ahmad, A., Awaad, I., Amigoni, F., Berghofer, J., Bischoff, R., Bonarini, A., Dwiputra, R., Hegger, F., Hochgeschwender, N., Iocchi, L., Kraetzschmar, G., Lima, P., Matteucci, M., Nardi, D., Schneider, S.

(FP7-ICT-601012 Revision 0.7), RoCKIn - Robot Competitions Kick Innovation in Cognitive Systems and Robotics, sep 2013 (techreport)

Abstract
RoCKIn is a EU-funded project aiming to foster scientific progress and innovation in cognitive systems and robotics through the design and implementation of competitions. An additional objective of RoCKIn is to increase public awareness of the current state-of-the-art in robotics in Europe and to demonstrate the innovation potential of robotics applications for solving societal challenges and improving the competitiveness of Europe in the global markets. In order to achieve these objectives, RoCKIn develops two competitions, one for domestic service robots (RoCKIn@Home) and one for industrial robots in factories (RoCKIn-@Work). These competitions are designed around challenges that are based on easy-to-communicate and convincing user stories, which catch the interest of both the general public and the scientifc community. The latter is in particular interested in solving open scientific challenges and to thoroughly assess, compare, and evaluate the developed approaches with competing ones. To allow this to happen, the competitions are designed to meet the requirements of benchmarking procedures and good experimental methods. The integration of benchmarking technology with the competition concept is one of the main objectives of RoCKIn. This document describes the first version of the RoCKIn@Work competition, which will be held for the first time in 2014. The first chapter of the document gives a brief overview, outlining the purpose and objective of the competition, the methodological approach taken by the RoCKIn project, the user story upon which the competition is based, the structure and organization of the competition, and the commonalities and differences with the RoboCup@Work competition, which served as inspiration for RoCKIn@Work. The second chapter provides details on the user story and analyzes the scientific and technical challenges it poses. Consecutive chapters detail the competition scenario, the competition design, and the organization of the competition. The appendices contain information on a library of functionalities, which we believe are needed, or at least useful, for building competition entries, details on the scenario construction, and a detailed account of the benchmarking infrastructure needed — and provided by RoCKIn.

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[BibTex]

[BibTex]


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D2.1.1 RoCKIn@Home - A Competition for Domestic Service Robots Competition Design, Rule Book, and Scenario Construction

Ahmad, A., Awaad, I., Amigoni, F., Berghofer, J., Bischoff, R., Bonarini, A., Dwiputra, R., Hegger, F., Hochgeschwender, N., Iocchi, L., Kraetzschmar, G., Lima, P., Matteucci, M., Nardi, D., Schneider, S.

(FP7-ICT-601012 Revision 0.7), RoCKIn - Robot Competitions Kick Innovation in Cognitive Systems and Robotics, sep 2013 (techreport)

Abstract
RoCKIn is a EU-funded project aiming to foster scientific progress and innovation in cognitive systems and robotics through the design and implementation of competitions. An additional objective of RoCKIn is to increase public awareness of the current state-of-the-art in robotics in Europe and to demonstrate the innovation potential of robotics applications for solving societal challenges and improving the competitiveness of Europe in the global markets. In order to achieve these objectives, RoCKIn develops two competitions, one for domestic service robots (RoCKIn@Home) and one for industrial robots in factories (RoCKIn-@Work). These competitions are designed around challenges that are based on easy-to-communicate and convincing user stories, which catch the interest of both the general public and the scientifc community. The latter is in particular interested in solving open scientific challenges and to thoroughly assess, compare, and evaluate the developed approaches with competing ones. To allow this to happen, the competitions are designed to meet the requirements of benchmarking procedures and good experimental methods. The integration of benchmarking technology with the competition concept is one of the main objectives of RoCKIn. This document describes the first version of the RoCKIn@Home competition, which will be held for the first time in 2014. The first chapter of the document gives a brief overview, outlining the purpose and objective of the competition, the methodological approach taken by the RoCKIn project, the user story upon which the competition is based, the structure and organization of the competition, and the commonalities and differences with the RoboCup@Home competition, which served as inspiration for RoCKIn@Home. The second chapter provides details on the user story and analyzes the scientific and technical challenges it poses. Consecutive chapters detail the competition scenario, the competition design, and the organization of the competition. The appendices contain information on a library of functionalities, which we believe are needed, or at least useful, for building competition entries, details on the scenario construction, and a detailed account of the benchmarking infrastructure needed — and provided by RoCKIn.

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[BibTex]

[BibTex]


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Distribution Fields with Adaptive Kernels for Large Displacement Image Alignment

Mears, B., Sevilla-Lara, L., Learned-Miller, E.

In British Machine Vision Conference (BMVC) , BMVA Press, September 2013 (inproceedings)

Abstract
While region-based image alignment algorithms that use gradient descent can achieve sub-pixel accuracy when they converge, their convergence depends on the smoothness of the image intensity values. Image smoothness is often enforced through the use of multiscale approaches in which images are smoothed and downsampled. Yet, these approaches typically use fixed smoothing parameters which may be appropriate for some images but not for others. Even for a particular image, the optimal smoothing parameters may depend on the magnitude of the transformation. When the transformation is large, the image should be smoothed more than when the transformation is small. Further, with gradient-based approaches, the optimal smoothing parameters may change with each iteration as the algorithm proceeds towards convergence. We address convergence issues related to the choice of smoothing parameters by deriving a Gauss-Newton gradient descent algorithm based on distribution fields (DFs) and proposing a method to dynamically select smoothing parameters at each iteration. DF and DF-like representations have previously been used in the context of tracking. In this work we incorporate DFs into a full affine model for region-based alignment and simultaneously search over parameterized sets of geometric and photometric transforms. We use a probabilistic interpretation of DFs to select smoothing parameters at each step in the optimization and show that this results in improved convergence rates.

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pdf code [BibTex]

pdf code [BibTex]


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Metric Regression Forests for Human Pose Estimation

(Best Science Paper Award)

Pons-Moll, G., Taylor, J., Shotton, J., Hertzmann, A., Fitzgibbon, A.

In British Machine Vision Conference (BMVC) , BMVA Press, September 2013 (inproceedings)

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pdf [BibTex]

pdf [BibTex]


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Vision meets Robotics: The KITTI Dataset

Geiger, A., Lenz, P., Stiller, C., Urtasun, R.

International Journal of Robotics Research, 32(11):1231 - 1237 , Sage Publishing, September 2013 (article)

Abstract
We present a novel dataset captured from a VW station wagon for use in mobile robotics and autonomous driving research. In total, we recorded 6 hours of traffic scenarios at 10-100 Hz using a variety of sensor modalities such as high-resolution color and grayscale stereo cameras, a Velodyne 3D laser scanner and a high-precision GPS/IMU inertial navigation system. The scenarios are diverse, capturing real-world traffic situations and range from freeways over rural areas to inner-city scenes with many static and dynamic objects. Our data is calibrated, synchronized and timestamped, and we provide the rectified and raw image sequences. Our dataset also contains object labels in the form of 3D tracklets and we provide online benchmarks for stereo, optical flow, object detection and other tasks. This paper describes our recording platform, the data format and the utilities that we provide.

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pdf DOI [BibTex]

pdf DOI [BibTex]


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Human Pose Calculation from Optical Flow Data

Black, M., Loper, M., Romero, J., Zuffi, S.

European Patent Application EP 2843621 , August 2013 (patent)

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Google Patents [BibTex]

Google Patents [BibTex]


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Statistics on Manifolds with Applications to Modeling Shape Deformations

Freifeld, O.

Brown University, August 2013 (phdthesis)

Abstract
Statistical models of non-rigid deformable shape have wide application in many fi elds, including computer vision, computer graphics, and biometry. We show that shape deformations are well represented through nonlinear manifolds that are also matrix Lie groups. These pattern-theoretic representations lead to several advantages over other alternatives, including a principled measure of shape dissimilarity and a natural way to compose deformations. Moreover, they enable building models using statistics on manifolds. Consequently, such models are superior to those based on Euclidean representations. We demonstrate this by modeling 2D and 3D human body shape. Shape deformations are only one example of manifold-valued data. More generally, in many computer-vision and machine-learning problems, nonlinear manifold representations arise naturally and provide a powerful alternative to Euclidean representations. Statistics is traditionally concerned with data in a Euclidean space, relying on the linear structure and the distances associated with such a space; this renders it inappropriate for nonlinear spaces. Statistics can, however, be generalized to nonlinear manifolds. Moreover, by respecting the underlying geometry, the statistical models result in not only more e ffective analysis but also consistent synthesis. We go beyond previous work on statistics on manifolds by showing how, even on these curved spaces, problems related to modeling a class from scarce data can be dealt with by leveraging information from related classes residing in di fferent regions of the space. We show the usefulness of our approach with 3D shape deformations. To summarize our main contributions: 1) We de fine a new 2D articulated model -- more expressive than traditional ones -- of deformable human shape that factors body-shape, pose, and camera variations. Its high realism is obtained from training data generated from a detailed 3D model. 2) We defi ne a new manifold-based representation of 3D shape deformations that yields statistical deformable-template models that are better than the current state-of-the- art. 3) We generalize a transfer learning idea from Euclidean spaces to Riemannian manifolds. This work demonstrates the value of modeling manifold-valued data and their statistics explicitly on the manifold. Specifi cally, the methods here provide new tools for shape analysis.

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pdf Project Page [BibTex]


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D1.1 Specification of General Features of Scenarios and Robots for Benchmarking Through Competitions

Ahmad, A., Awaad, I., Amigoni, F., Berghofer, J., Bischoff, R., Bonarini, A., Dwiputra, R., Fontana, G., Hegger, F., Hochgeschwender, N., Iocchi, L., Kraetzschmar, G., Lima, P., Matteucci, M., Nardi, D., Schiaffonati, V., Schneider, S.

(FP7-ICT-601012 Revision 1.0), RoCKIn - Robot Competitions Kick Innovation in Cognitive Systems and Robotics, July 2013 (techreport)

Abstract
RoCKIn is a EU-funded project aiming to foster scientific progress and innovation in cognitive systems and robotics through the design and implementation of competitions. An additional objective of RoCKIn is to increase public awareness of the current state-of-the-art in robotics and the innovation potential of robotics applications. From these objectives several requirements for the work performed in RoCKIn can be derived: The RoCKIn competitions must start from convincing, easy-to-communicate user stories, that catch the attention of relevant stakeholders, the media, and the crowd. The user stories play the role of a mid- to long-term vision for a competition. Preferably, the user stories address economic, societal, or environmental problems. The RoCKIn competitions must pose open scientific challenges of interest to sufficiently many researchers to attract existing and new teams of robotics researchers for participation in the competition. The competitions need to promise some suitable reward, such as recognition in the scientific community, publicity for a team’s work, awards, or prize money, to justify the effort a team puts into the development of a competition entry. The competitions should be designed in such a way that they reward general, scientifically sound solutions to the challenge problems; such general solutions should score better than approaches that work only in narrowly defined contexts and are considred over-engineered. The challenges motivating the RoCKIn competitions must be broken down into suitable intermediate goals that can be reached with a limited team effort until the next competition and the project duration. The RoCKIn competitions must be well-defined and well-designed, with comprehensive rule books and instructions for the participants in order to guarantee a fair competition. The RoCKIn competitions must integrate competitions with benchmarking in order to provide comprehensive feedback for the teams about the suitability of particular functional modules, their overall architecture, and system integration. This document takes the first steps towards the RoCKIn goals. After outlining our approach, we present several user stories for further discussion within the community. The main objectives of this document are to identify and document relevant scenario features and the tasks and functionalities subject for benchmarking in the competitions.

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[BibTex]

[BibTex]


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Studying large-scale brain networks: electrical stimulation and neural-event-triggered fMRI

Logothetis, N., Eschenko, O., Murayama, Y., Augath, M., Steudel, T., Evrard, H., Besserve, M., Oeltermann, A.

Twenty-Second Annual Computational Neuroscience Meeting (CNS*2013), July 2013, journal = {BMC Neuroscience}, year = {2013}, month = {7}, volume = {14}, number = {Supplement 1}, pages = {A1}, (talk)

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Web [BibTex]

Web [BibTex]


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SocRob-MSL 2013 Team Description Paper for Middle Sized League

Messias, J., Ahmad, A., Reis, J., Serafim, M., Lima, P.

17th Annual RoboCup International Symposium 2013, July 2013 (techreport)

Abstract
This paper describes the status of the SocRob MSL robotic soccer team as required by the RoboCup 2013 qualification procedures. The team’s latest scientific and technical developments, since its last participation in RoboCup MSL, include further advances in cooperative perception; novel communication methods for distributed robotics; progressive deployment of the ROS middleware; improved localization through feature tracking and Mixture MCL; novel planning methods based on Petri nets and decision-theoretic frameworks; and hardware developments in ball-handling/kicking devices.

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link (url) [BibTex]

link (url) [BibTex]


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Visualizing dimensionality reduction of systems biology data

Lehrmann, A. M., Huber, M., Polatkan, A. C., Pritzkau, A., Nieselt, K.

Data Mining and Knowledge Discovery, 1(27):146-165, Springer, July 2013 (article)

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pdf SpRay [BibTex]

pdf SpRay [BibTex]


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Poselet conditioned pictorial structures

Pishchulin, L., Andriluka, M., Gehler, P., Schiele, B.

In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages: 588 - 595, IEEE, Portland, OR, Conference on Computer Vision and Pattern Recognition (CVRP), June 2013 (inproceedings)

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pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]


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Occlusion Patterns for Object Class Detection

Pepik, B., Stark, M., Gehler, P., Schiele, B.

In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Portland, OR, June 2013 (inproceedings)

Abstract
Despite the success of recent object class recognition systems, the long-standing problem of partial occlusion re- mains a major challenge, and a principled solution is yet to be found. In this paper we leave the beaten path of meth- ods that treat occlusion as just another source of noise – instead, we include the occluder itself into the modelling, by mining distinctive, reoccurring occlusion patterns from annotated training data. These patterns are then used as training data for dedicated detectors of varying sophistica- tion. In particular, we evaluate and compare models that range from standard object class detectors to hierarchical, part-based representations of occluder/occludee pairs. In an extensive evaluation we derive insights that can aid fur- ther developments in tackling the occlusion challenge.

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pdf Project Page [BibTex]

pdf Project Page [BibTex]


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Lost! Leveraging the Crowd for Probabilistic Visual Self-Localization

(CVPR13 Best Paper Runner-Up)

Brubaker, M. A., Geiger, A., Urtasun, R.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2013), pages: 3057-3064, IEEE, Portland, OR, June 2013 (inproceedings)

Abstract
In this paper we propose an affordable solution to self- localization, which utilizes visual odometry and road maps as the only inputs. To this end, we present a probabilis- tic model as well as an efficient approximate inference al- gorithm, which is able to utilize distributed computation to meet the real-time requirements of autonomous systems. Because of the probabilistic nature of the model we are able to cope with uncertainty due to noisy visual odometry and inherent ambiguities in the map ( e.g ., in a Manhattan world). By exploiting freely available, community devel- oped maps and visual odometry measurements, we are able to localize a vehicle up to 3m after only a few seconds of driving on maps which contain more than 2,150km of driv- able roads.

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pdf supplementary project page [BibTex]

pdf supplementary project page [BibTex]


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Human Pose Estimation using Body Parts Dependent Joint Regressors

Dantone, M., Gall, J., Leistner, C., van Gool, L.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 3041-3048, IEEE, Portland, OR, USA, June 2013 (inproceedings)

Abstract
In this work, we address the problem of estimating 2d human pose from still images. Recent methods that rely on discriminatively trained deformable parts organized in a tree model have shown to be very successful in solving this task. Within such a pictorial structure framework, we address the problem of obtaining good part templates by proposing novel, non-linear joint regressors. In particular, we employ two-layered random forests as joint regressors. The first layer acts as a discriminative, independent body part classifier. The second layer takes the estimated class distributions of the first one into account and is thereby able to predict joint locations by modeling the interdependence and co-occurrence of the parts. This results in a pose estimation framework that takes dependencies between body parts already for joint localization into account and is thus able to circumvent typical ambiguities of tree structures, such as for legs and arms. In the experiments, we demonstrate that our body parts dependent joint regressors achieve a higher joint localization accuracy than tree-based state-of-the-art methods.

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pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]


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A fully-connected layered model of foreground and background flow

Sun, D., Wulff, J., Sudderth, E., Pfister, H., Black, M.

In IEEE Conf. on Computer Vision and Pattern Recognition, (CVPR 2013), pages: 2451-2458, Portland, OR, June 2013 (inproceedings)

Abstract
Layered models allow scene segmentation and motion estimation to be formulated together and to inform one another. Traditional layered motion methods, however, employ fairly weak models of scene structure, relying on locally connected Ising/Potts models which have limited ability to capture long-range correlations in natural scenes. To address this, we formulate a fully-connected layered model that enables global reasoning about the complicated segmentations of real objects. Optimization with fully-connected graphical models is challenging, and our inference algorithm leverages recent work on efficient mean field updates for fully-connected conditional random fields. These methods can be implemented efficiently using high-dimensional Gaussian filtering. We combine these ideas with a layered flow model, and find that the long-range connections greatly improve segmentation into figure-ground layers when compared with locally connected MRF models. Experiments on several benchmark datasets show that the method can recover fine structures and large occlusion regions, with good flow accuracy and much lower computational cost than previous locally-connected layered models.

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pdf Supplemental Material Project Page Project Page [BibTex]

pdf Supplemental Material Project Page Project Page [BibTex]


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Correlation of Simultaneously Acquired Diffusion-Weighted Imaging and 2-Deoxy-[18F] fluoro-2-D-glucose Positron Emission Tomography of Pulmonary Lesions in a Dedicated Whole-Body Magnetic Resonance/Positron Emission Tomography System

Schmidt, H., Brendle, C., Schraml, C., Martirosian, P., Bezrukov, I., Hetzel, J., Müller, M., Sauter, A., Claussen, C., Pfannenberg, C., Schwenzer, N.

Investigative Radiology, 48(5):247-255, May 2013 (article)

ei

Web [BibTex]

Web [BibTex]


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Replacing Causal Faithfulness with Algorithmic Independence of Conditionals

Lemeire, J., Janzing, D.

Minds and Machines, 23(2):227-249, May 2013 (article)

Abstract
Independence of Conditionals (IC) has recently been proposed as a basic rule for causal structure learning. If a Bayesian network represents the causal structure, its Conditional Probability Distributions (CPDs) should be algorithmically independent. In this paper we compare IC with causal faithfulness (FF), stating that only those conditional independences that are implied by the causal Markov condition hold true. The latter is a basic postulate in common approaches to causal structure learning. The common spirit of FF and IC is to reject causal graphs for which the joint distribution looks ‘non-generic’. The difference lies in the notion of genericity: FF sometimes rejects models just because one of the CPDs is simple, for instance if the CPD describes a deterministic relation. IC does not behave in this undesirable way. It only rejects a model when there is a non-generic relation between different CPDs although each CPD looks generic when considered separately. Moreover, it detects relations between CPDs that cannot be captured by conditional independences. IC therefore helps in distinguishing causal graphs that induce the same conditional independences (i.e., they belong to the same Markov equivalence class). The usual justification for FF implicitly assumes a prior that is a probability density on the parameter space. IC can be justified by Solomonoff’s universal prior, assigning non-zero probability to those points in parameter space that have a finite description. In this way, it favours simple CPDs, and therefore respects Occam’s razor. Since Kolmogorov complexity is uncomputable, IC is not directly applicable in practice. We argue that it is nevertheless helpful, since it has already served as inspiration and justification for novel causal inference algorithms.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Perception-driven multi-robot formation control

Ahmad, A., Nascimento, T., Conceicao, A., Moreira, A., Lima, P.

In pages: 1851-1856, IEEE, IEEE International Conference on Robotics and Automation (ICRA), May 2013 (inproceedings)

Abstract
Maximizing the performance of cooperative perception of a tracked target by a team of mobile robots while maintaining the team's formation is the core problem addressed in this work. We propose a solution by integrating the controller and the estimator modules in a formation control loop. The controller module is a distributed non-linear model predictive controller and the estimator module is based on a particle filter for cooperative target tracking. A formal description of the integration followed by simulation and real robot results on two different teams of homogeneous robots are presented. The results highlight how our method successfully enables a team of homogeneous robots to minimize the total uncertainty of the tracked target's cooperative estimate while complying with the performance criteria such as keeping a pre-set distance between the team-mates and/or the target and obstacle avoidance.

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DOI [BibTex]

DOI [BibTex]


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Cooperative Robot Localization and Target Tracking based on Least Squares Minimization

Ahmad, A., Tipaldi, G., Lima, P., Burgard, W.

In pages: 5696-5701, IEEE, IEEE International Conference on Robotics and Automation (ICRA), May 2013 (inproceedings)

Abstract
In this paper we address the problem of cooperative localization and target tracking with a team of moving robots. We model the problem as a least squares minimization problem and show that this problem can be efficiently solved using sparse optimization methods. To achieve this, we represent the problem as a graph, where the nodes are robot and target poses at individual time-steps and the edges are their relative measurements. Static landmarks at known position are used to define a common reference frame for the robots and the targets. In this way, we mitigate the risk of using measurements and state estimates more than once, since all the relative measurements are i.i.d. and no marginalization is performed. Experiments performed using a set of real robots show higher accuracy compared to a Kalman filter.

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DOI [BibTex]

DOI [BibTex]


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Unscented Kalman Filtering on Riemannian Manifolds

Soren Hauberg, Francois Lauze, Kim S. Pedersen

Journal of Mathematical Imaging and Vision, 46(1):103-120, Springer Netherlands, May 2013 (article)

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Publishers site PDF [BibTex]

Publishers site PDF [BibTex]


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Probabilistic Models for 3D Urban Scene Understanding from Movable Platforms

Geiger, A.

Karlsruhe Institute of Technology, Karlsruhe Institute of Technology, April 2013 (phdthesis)

Abstract
Visual 3D scene understanding is an important component in autonomous driving and robot navigation. Intelligent vehicles for example often base their decisions on observations obtained from video cameras as they are cheap and easy to employ. Inner-city intersections represent an interesting but also very challenging scenario in this context: The road layout may be very complex and observations are often noisy or even missing due to heavy occlusions. While Highway navigation and autonomous driving on simple and annotated intersections have already been demonstrated successfully, understanding and navigating general inner-city crossings with little prior knowledge remains an unsolved problem. This thesis is a contribution to understanding multi-object traffic scenes from video sequences. All data is provided by a camera system which is mounted on top of the autonomous driving platform AnnieWAY. The proposed probabilistic generative model reasons jointly about the 3D scene layout as well as the 3D location and orientation of objects in the scene. In particular, the scene topology, geometry as well as traffic activities are inferred from short video sequences. The model takes advantage of monocular information in the form of vehicle tracklets, vanishing lines and semantic labels. Additionally, the benefit of stereo features such as 3D scene flow and occupancy grids is investigated. Motivated by the impressive driving capabilities of humans, no further information such as GPS, lidar, radar or map knowledge is required. Experiments conducted on 113 representative intersection sequences show that the developed approach successfully infers the correct layout in a variety of difficult scenarios. To evaluate the importance of each feature cue, experiments with different feature combinations are conducted. Additionally, the proposed method is shown to improve object detection and object orientation estimation performance.

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pdf [BibTex]

pdf [BibTex]


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Unknown-color spherical object detection and tracking

Troppan, A., Guerreiro, E., Celiberti, F., Santos, G., Ahmad, A., Lima, P.

In pages: 1-4, IEEE, 13th International Conference on Autonomous Robot Systems (Robotica), April 2013 (inproceedings)

Abstract
Detection and tracking of an unknown-color spherical object in a partially-known environment using a robot with a single camera is the core problem addressed in this article. A novel color detection mechanism, which exploits the geometrical properties of the spherical object's projection onto the image plane, precedes the object's detection process. A Kalman filter-based tracker uses the object detection in its update step and tracks the spherical object. Real robot experimental evaluation of the proposed method is presented on soccer robots detecting and tracking an unknown-color ball.

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DOI [BibTex]

DOI [BibTex]


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System and method for generating bilinear spatiotemporal basis models

Matthews, I. A. I. S. T. S. K. S. Y.

US Patent Application 13/425,369, March 2013 (patent)

Abstract
Techniques are disclosed for generating a bilinear spatiotemporal basis model. A method includes the steps of predefining a trajectory basis for the bilinear spatiotemporal basis model, receiving three-dimensional spatiotemporal data for a training sequence, estimating a shape basis for the bilinear spatiotemporal basis model using the three-dimensional spatiotemporal data, and computing coefficients for the bilinear spatiotemporal basis model using the trajectory basis and the shape basis.

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Google Patents [BibTex]


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What can neurons do for their brain? Communicate selectivity with bursts

Balduzzi, D., Tononi, G.

Theory in Biosciences , 132(1):27-39, Springer, March 2013 (article)

Abstract
Neurons deep in cortex interact with the environment extremely indirectly; the spikes they receive and produce are pre- and post-processed by millions of other neurons. This paper proposes two information-theoretic constraints guiding the production of spikes, that help ensure bursting activity deep in cortex relates meaningfully to events in the environment. First, neurons should emphasize selective responses with bursts. Second, neurons should propagate selective inputs by burst-firing in response to them. We show the constraints are necessary for bursts to dominate information-transfer within cortex, thereby providing a substrate allowing neurons to distribute credit amongst themselves. Finally, since synaptic plasticity degrades the ability of neurons to burst selectively, we argue that homeostatic regulation of synaptic weights is necessary, and that it is best performed offline during sleep.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Apprenticeship Learning with Few Examples

Boularias, A., Chaib-draa, B.

Neurocomputing, 104, pages: 83-96, March 2013 (article)

Abstract
We consider the problem of imitation learning when the examples, provided by an expert human, are scarce. Apprenticeship learning via inverse reinforcement learning provides an efficient tool for generalizing the examples, based on the assumption that the expert's policy maximizes a value function, which is a linear combination of state and action features. Most apprenticeship learning algorithms use only simple empirical averages of the features in the demonstrations as a statistics of the expert's policy. However, this method is efficient only when the number of examples is sufficiently large to cover most of the states, or the dynamics of the system is nearly deterministic. In this paper, we show that the quality of the learned policies is sensitive to the error in estimating the averages of the features when the dynamics of the system is stochastic. To reduce this error, we introduce two new approaches for bootstrapping the demonstrations by assuming that the expert is near-optimal and the dynamics of the system is known. In the first approach, the expert's examples are used to learn a reward function and to generate furthermore examples from the corresponding optimal policy. The second approach uses a transfer technique, known as graph homomorphism, in order to generalize the expert's actions to unvisited regions of the state space. Empirical results on simulated robot navigation problems show that our approach is able to learn sufficiently good policies from a significantly small number of examples.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Quasi-Newton Methods: A New Direction

Hennig, P., Kiefel, M.

Journal of Machine Learning Research, 14(1):843-865, March 2013 (article)

Abstract
Four decades after their invention, quasi-Newton methods are still state of the art in unconstrained numerical optimization. Although not usually interpreted thus, these are learning algorithms that fit a local quadratic approximation to the objective function. We show that many, including the most popular, quasi-Newton methods can be interpreted as approximations of Bayesian linear regression under varying prior assumptions. This new notion elucidates some shortcomings of classical algorithms, and lights the way to a novel nonparametric quasi-Newton method, which is able to make more efficient use of available information at computational cost similar to its predecessors.

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website+code pdf link (url) [BibTex]

website+code pdf link (url) [BibTex]


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Regional effects of magnetization dispersion on quantitative perfusion imaging for pulsed and continuous arterial spin labeling

Cavusoglu, M., Pohmann, R., Burger, H. C., Uludag, K.

Magnetic Resonance in Medicine, 69(2):524-530, Febuary 2013 (article)

Abstract
Most experiments assume a global transit delay time with blood flowing from the tagging region to the imaging slice in plug flow without any dispersion of the magnetization. However, because of cardiac pulsation, nonuniform cross-sectional flow profile, and complex vessel networks, the transit delay time is not a single value but follows a distribution. In this study, we explored the regional effects of magnetization dispersion on quantitative perfusion imaging for varying transit times within a very large interval from the direct comparison of pulsed, pseudo-continuous, and dual-coil continuous arterial spin labeling encoding schemes. Longer distances between tagging and imaging region typically used for continuous tagging schemes enhance the regional bias on the quantitative cerebral blood flow measurement causing an underestimation up to 37% when plug flow is assumed as in the standard model.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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The multivariate Watson distribution: Maximum-likelihood estimation and other aspects

Sra, S., Karp, D.

Journal of Multivariate Analysis, 114, pages: 256-269, February 2013 (article)

Abstract
This paper studies fundamental aspects of modelling data using multivariate Watson distributions. Although these distributions are natural for modelling axially symmetric data (i.e., unit vectors where View the MathML source are equivalent), for high-dimensions using them can be difficult—largely because for Watson distributions even basic tasks such as maximum-likelihood are numerically challenging. To tackle the numerical difficulties some approximations have been derived. But these are either grossly inaccurate in high-dimensions [K.V. Mardia, P. Jupp, Directional Statistics, second ed., John Wiley & Sons, 2000] or when reasonably accurate [A. Bijral, M. Breitenbach, G.Z. Grudic, Mixture of Watson distributions: a generative model for hyperspherical embeddings, in: Artificial Intelligence and Statistics, AISTATS 2007, 2007, pp. 35–42], they lack theoretical justification. We derive new approximations to the maximum-likelihood estimates; our approximations are theoretically well-defined, numerically accurate, and easy to compute. We build on our parameter estimation and discuss mixture-modelling with Watson distributions; here we uncover a hitherto unknown connection to the “diametrical clustering” algorithm of Dhillon et al. [I.S. Dhillon, E.M. Marcotte, U. Roshan, Diametrical clustering for identifying anticorrelated gene clusters, Bioinformatics 19 (13) (2003) 1612–1619].

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Simple, fast, accurate melanocytic lesion segmentation in 1D colour space

Peruch, F., Bogo, F., Bonazza, M., Bressan, M., Cappelleri, V., Peserico, E.

In VISAPP (1), pages: 191-200, Barcelona, February 2013 (inproceedings)

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pdf [BibTex]

pdf [BibTex]


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How the result of graph clustering methods depends on the construction of the graph

Maier, M., von Luxburg, U., Hein, M.

ESAIM: Probability & Statistics, 17, pages: 370-418, January 2013 (article)

Abstract
We study the scenario of graph-based clustering algorithms such as spectral clustering. Given a set of data points, one rst has to construct a graph on the data points and then apply a graph clustering algorithm to nd a suitable partition of the graph. Our main question is if and how the construction of the graph (choice of the graph, choice of parameters, choice of weights) in uences the outcome of the nal clustering result. To this end we study the convergence of cluster quality measures such as the normalized cut or the Cheeger cut on various kinds of random geometric graphs as the sample size tends to in nity. It turns out that the limit values of the same objective function are systematically di erent on di erent types of graphs. This implies that clustering results systematically depend on the graph and can be very di erent for di erent types of graph. We provide examples to illustrate the implications on spectral clustering.

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PDF DOI [BibTex]

PDF DOI [BibTex]


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Falsification and future performance

Balduzzi, D.

In Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence, 7070, pages: 65-78, Lecture Notes in Computer Science, Springer, Berlin, Germany, Solomonoff 85th Memorial Conference, January 2013 (inproceedings)

Abstract
We information-theoretically reformulate two measures of capacity from statistical learning theory: empirical VC-entropy and empirical Rademacher complexity. We show these capacity measures count the number of hypotheses about a dataset that a learning algorithm falsifies when it finds the classifier in its repertoire minimizing empirical risk. It then follows from that the future performance of predictors on unseen data is controlled in part by how many hypotheses the learner falsifies. As a corollary we show that empirical VC-entropy quantifies the message length of the true hypothesis in the optimal code of a particular probability distribution, the so-called actual repertoire.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Explicit eigenvalues of certain scaled trigonometric matrices

Sra, S.

Linear Algebra and its Applications, 438(1):173-181, January 2013 (article)

ei

DOI [BibTex]

DOI [BibTex]


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How Sensitive Is the Human Visual System to the Local Statistics of Natural Images?

Gerhard, H., Wichmann, F., Bethge, M.

PLoS Computational Biology, 9(1):e1002873, January 2013 (article)

Abstract
Several aspects of primate visual physiology have been identified as adaptations to local regularities of natural images. However, much less work has measured visual sensitivity to local natural image regularities. Most previous work focuses on global perception of large images and shows that observers are more sensitive to visual information when image properties resemble those of natural images. In this work we measure human sensitivity to local natural image regularities using stimuli generated by patch-based probabilistic natural image models that have been related to primate visual physiology. We find that human observers can learn to discriminate the statistical regularities of natural image patches from those represented by current natural image models after very few exposures and that discriminability depends on the degree of regularities captured by the model. The quick learning we observed suggests that the human visual system is biased for processing natural images, even at very fine spatial scales, and that it has a surprisingly large knowledge of the regularities in natural images, at least in comparison to the state-of-the-art statistical models of natural images.

ei

DOI [BibTex]

DOI [BibTex]


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A Study of X-Ray Image Perception for Pneumoconiosis Detection

Jampani, V.

IIIT-Hyderabad, Hyderabad, India, January 2013 (mastersthesis)

Abstract
Pneumoconiosis is an occupational lung disease caused by the inhalation of industrial dust. Despite the increasing safety measures and better work place environments, pneumoconiosis is deemed to be the most common occupational disease in the developing countries like India and China. Screening and assessment of this disease is done through radiological observation of chest x-rays. Several studies have shown the significant inter and intra reader observer variation in the diagnosis of this disease, showing the complexity of the task and importance of the expertise in diagnosis. The present study is aimed at understanding the perceptual and cognitive factors affecting the reading of chest x-rays of pneumoconiosis patients. Understanding these factors helps in developing better image acquisition systems, better training regimen for radiologists and development of better computer aided diagnostic (CAD) systems. We used an eye tracking experiment to study the various factors affecting the assessment of this diffused lung disease. Specifically, we aimed at understanding the role of expertize, contralateral symmetric (CS) information present in chest x-rays on the diagnosis and the eye movements of the observers. We also studied the inter and intra observer fixation consistency along with the role of anatomical and bottom up saliency features in attracting the gaze of observers of different expertize levels, to get better insights into the effect of bottom up and top down visual saliency on the eye movements of observers. The experiment is conducted in a room dedicated to eye tracking experiments. Participants consisting of novices (3), medical students (12), residents (4) and staff radiologists (4) were presented with good quality PA chest X-rays, and were asked to give profusion ratings for each of the 6 lung zones. Image set consisting of 17 normal full chest x-rays and 16 single lung images are shown to the participants in random order. Time of the diagnosis and the eye movements are also recorded using a remote head free eye tracker. Results indicated that Expertise and CS play important roles in the diagnosis of pneumoconiosis. Novices and medical students are slow and inefficient whereas, residents and staff are quick and efficient. A key finding of our study is that the presence of CS information alone does not help improve diagnosis as much as learning how to use the information. This learning appears to be gained from focused training and years of experience. Hence, good training for radiologists and careful observation of each lung zone may improve the quality of diagnostic results. For residents, the eye scanning strategies play an important role in using the CS information present in chest radiographs; however, in staff radiologists, peripheral vision or higher-level cognitive processes seems to play role in using the CS information. There is a reasonably good inter and intra observer fixation consistency suggesting the use of similar viewing strategies. Experience is helping the observers to develop new visual strategies based on the image content so that they can quickly and efficiently assess the disease level. First few fixations seem to be playing an important role in choosing the visual strategy, appropriate for the given image. Both inter-rib and rib regions are given equal importance by the observers. Despite reading of chest x-rays being highly task dependent, bottom up saliency is shown to have played an important role in attracting the fixations of the observers. This role of bottom up saliency seems to be more in lower expertize groups compared to that of higher expertize groups. Both bottom up and top down influence of visual fixations seems to change with time. The relative role of top down and bottom up influences of visual attention is still not completely understood and it remains the part of future work. Based on our experimental results, we have developed an extended saliency model by combining the bottom up saliency and the saliency of lung regions in a chest x-ray. This new saliency model performed significantly better than bottom-up saliency in predicting the gaze of the observers in our experiment. Even though, the model is a simple combination of bottom-up saliency maps and segmented lung masks, this demonstrates that even basic models using simple image features can predict the fixations of the observers to a good accuracy. Experimental analysis suggested that the factors affecting the reading of chest x-rays of pneumoconiosis are complex and varied. A good understanding of these factors definitely helps in the development of better radiological screening of pneumoconiosis through improved training and also through the use of improved CAD tools. The presented work is an attempt to get insights into what these factors are and how they modify the behavior of the observers.

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pdf [BibTex]

pdf [BibTex]


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A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them

Sun, D., Roth, S., Black, M. J.

(CS-10-03), Brown University, Department of Computer Science, January 2013 (techreport)

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pdf [BibTex]

pdf [BibTex]