Events & Talks

Perceiving Systems Talk Vladlen Koltun 17-02-2015 Reconstructing Complete 3D Models from Single Images We present an approach to creating 3D models of objects depicted in Web images, even when each object may only be shown in a single image. Our approach uses a comparatively small collection of existing 3D models to guide the reconstruction process. These existing shapes are used to derive information about shape structure. Our guiding idea is to jointly analyze the images and the available 3D models. Joint analysis of all images along with the available shapes regularizes the formulated optimization problems, stabilizes estimation of camera parameters and construction of dense pixel-level c...

Perceiving Systems IS Colloquium Michael Goesele 16-02-2015 Reflecting in and on the Gradient Domain Image-based rendering has been introduced in the 1990s as an alternative approach to photorealistic rendering. Its key idea is to novel renderings by re-projecting pixels from nearby views. The basic approach works well for many scenes but breaks down if the scene contains “non-standard” elements such as reflective surfaces. In this talk, I will first show how we can extend image-based rendering to handle scenes with reflections. I will then discuss a novel gradient-based technique for image-based rendering that can intrinsically handle scenes with reflections.</pre>

Talk Katherine Kuchenbecker 26-01-2015 Haptics: The Technology of Touch When you touch objects in your surroundings, you can discern each item’ s physical properties from the rich array of haptic cues you experience, including both the tactile sensations arising in your skin and the kinesthetic cues originating in your muscles and joints. Although physical interaction with the world is at the core of human experience, few computer and machine interfaces provide the operator with high-fidelity touch feedback, limiting their usability . Similarly , autonomous robots rarely take advantag... Jane Walters

Autonomous Motion IS Colloquium Marco Claudio Campi 19-01-2015 Introduction to the Scenario Approach The scenario approach is a broad methodology to deal with decision-making in an uncertain environment. By resorting to observations, or by sampling uncertainty from a given model, one obtains an optimization problem (the scenario problem), whose solution bears precise probabilistic guarantees in relation to new, unseen, situations. The scenario approach opens up new avenues to address data-based problems in learning, identification, finance, and other fields. Sebastian Trimpe

Perceiving Systems Symposium 10-12-2014 - 13-12-2014 Scenes from Videos Workshop This invitation-only workshop will bring together experts in the field to focus on the problem of estimating Scenes from Video. In so doing, we hope to draw several lines of research together to address the problem of extracting physical and semantic information from video.

Event 23-11-2014 - 25-11-2014 Computer Vision Workshop As a follow up meeting on the RNLS (Research Network on Learning Systems) at the ETH Zürich in June 2014, we will organise the Computer Vision Breakout Meeting on the MPI IS Campus in Tuebingen. This meeting is to bring together researchers from both institutions, MPI IS and ETH Zürich, to exchange ideas, knowledge and experiences and learn from each other.

Perceiving Systems Talk Wenzel Jakob 28-10-2014 Capturing and simulating the interaction of light with the world around us Driven by the increasing demand for photorealistic computer-generated images, graphics is currently undergoing a substantial transformation to physics-based approaches which accurately reproduce the interaction of light and matter. Progress on both sides of this transformation -- physical models and simulation techniques -- has been steady but mostly independent from another. When combined, the resulting methods are in many cases impracticably slow and require unrealistic workarounds to process even simple everyday scenes. My research lies at the interface of these two research fields; my g...

Perceiving Systems IS Colloquium Konrad Schindler 15-10-2014 Images everywhere - computer vision with vehicle-mounted, airborne and tourist cameras I will present selected research projects of the Photogrammetry and Remote Sensing Group at ETH, including (i) 3D scene flow estimation for stereo video captured from a car; (ii) extraction of road networks from aerial images; and (iii) 3D reconstruction from large, unstructured (e.g. crowd-sourced) image collections.<br />

Perceiving Systems Talk Leonid Sigal 15-09-2014 Weak-supervision for Objects Detection and Image/Video Set Summarization <p> The growing scale of image and video datasets in vision makes labeling and annotation of such datasets, for training of recognition models, difficult and time consuming. Further, richer models often require richer labelings of the data, that are typically even more difficult to obtain. In this talk I will focus on two models that make use of different forms of supervision for two different vision tasks.  <br /> <br /> In the first part of this talk I will focus on object detection. The appearance of an object changes profoundly with pose, camera view and interactions of the ...

Perceiving Systems Talk Jonathan Taylor 04-09-2014 Hands and Dolphins: Modelling Non-Rigid Shape with Subdivision Surfaces Abstract: I will present a general framework for modelling and recovering 3D shape and pose using subdivision surfaces. To demonstrate this frameworks generality, I will show how to recover both a personalized rigged hand model from a sequence of depth images and a blend shape model of dolphin pose from a collection of 2D dolphin images. The core requirement is the formulation of a generative model in which the control vertices of a smooth subdivision surface are parameterized (e.g. with joint angles or blend weights) by a differentiable deformation function. The energy function that fal...

Perceiving Systems Talk Abhilash Srikantha 29-08-2014 Discovering Object Classes from Activities <p> In order to avoid an expensive manual labeling process or to learn object classes autonomously without human intervention, object discovery techniques have been proposed that extract visual similar objects from weakly labelled videos. However, the problem of discovering small or medium sized objects is largely unexplored. We observe that videos with activities involving human-object interactions can serve as weakly labelled data for such cases. Since neither object appearance nor motion is distinct enough to discover objects in these videos, we propose a framework that samples from a s...

Empirical Inference IS Colloquium Joaquin Quiñonero Candela 11-08-2014 Examples of Machine Learning and Data Science at Facebook Facebook serves close to a billion people every day, who are only able to consume a small subset of the information available to them. In this talk I will give some examples of how machine learning is used to personalize people’s Facebook experience. I will also present some data science experiments with fairly counter-intuitive results.

Perceiving Systems Talk Lourdes Agapito 22-07-2014 Reconstructing the Pascal VOC Dataset and Non-Rigid Structure from Motion: Two sides of the same problem? <p> In this talk I will discuss two related problems in 3D reconstruction: (i) recovering the 3D shape of a temporally varying non-rigid 3D surface given a single video sequence and (ii) reconstructing different instances of the same object class category given a large collection of images from that category. In both cases we extract dense 3D shape information by analysing shape variation -- in one case of the same object instance over time and in the other across different instances of objects that belong to the same class.</p> <p> First I will discuss the problem of dense capture of 3D ...

Empirical Inference IS Colloquium Manfred Opper 15-07-2014 Approximate inference for stochastic differential equations Stochastic differential equations (SDEs) arise naturally as descriptions of continuous time dynamical systems. My talk addresses the problem of inferring the dynamical state and parameters of such systems from observations taken at discrete times. I will discuss the application of approximate inference methods such as the variational method and expectation propagation and show how higher dimensional systems can be treated by a mean field approximation. In the second part of my talk I will discuss the nonparametric estimation of the drift (i.e. the deterministic part of the ‘force’ whi... Philipp Hennig Michel Besserve

Perceiving Systems IS Colloquium Christian Theobalt 14-07-2014 4D reconstruction in complex scenes, inverse rendering, advanced video editing Even though many challenges remain unsolved, in recent years computer graphics algorithms to render photo-realistic imagery have seen tremendous progress. An important prerequisite for high-quality renderings is the availability of good models of the scenes to be rendered, namely models of shape, motion and appearance. Unfortunately, the technology to create such models has not kept pace with the technology to render the imagery. In fact, we observe a content creation bottleneck, as it often takes man months of tedious manual work by a animation artists to craft models of moving virtual sce... Gerard Pons-Moll

Empirical Inference IS Colloquium Holger Rauhut 23-06-2014 Compressive Sensing and Beyond The recent theory of compressive sensing predicts that (approximately) sparse vectors can be recovered from vastly incomplete linear measurements using efficient algorithms. This principle has a large number of potential applications in signal and image processing, machine learning and more. Optimal measurement matrices in this context known so far are based on randomness. Recovery algorithms include convex optimization approaches (l1-minimization) as well as greedy methods. Gaussian and Bernoulli random matrices are provably optimal in the sense that the smallest possible number of... Michel Besserve

Event 16-06-2014 - 20-06-2014 Summer School: Research Network on Learning Systems (at ETH Zurich) The inaugural summer school of the joint ETH/MPI Research Network on Learning Systems will feature tutorial lectures and practicals on various topics related to Learning Systems. In particular, researchers from the Max Planck Institute for Intelligent Systems and ETH Zurich will present introductory and advanced topics in areas such as Statistical Machine Learning, Robotics / Control and Computer Vision.

Perceiving Systems Talk Brian Corner 11-06-2014 Getting the People Right: A Physical Anthropologist's View of Digital Human Modeling for Virtual Environments <p> A goal in virtual reality is for the user to experience a synthetic environment as if it were real. Engagement with virtual actors is a big part of the sensory context, thus getting the people "right" is critical for success. Size, shape, gender, ethnicity, clothing, color, texture, movement, among other attributes must be layered and nuanced to provide an accurate encounter between an actor and a user. In this talk, I discuss the development of digital human models and how they may be improved to obtain the high realism for successful engagement in a virtual world.</p>

Perceiving Systems Talk Christian Häne 10-06-2014 Convex Methods for Dense Semantic 3D Reconstruction Volumetric 3D modeling has attracted a lot of attention in the past. In this talk I will explain how the standard volumetric formulation can be extended to include semantic information by using a convex multi-label formulation. One of the strengths of our formulation is that it allows us to directly account for the expected surface orientations. I will focus on two applications. Firstly, I will introduce a method that allows for joint volumetric reconstruction and class segmentation. This is achieved by taking into account the expected orientations of object classes such as ground and build...

Empirical Inference Symposium 04-06-2014 - 06-06-2014 IEEE Conference on Pattern Recognition in Neuroimaging, Tübingen 2014 Multivariate analysis of neuroimaging data has gained ground very rapidly in the community over the past few years, leading to impressive results in cognitive and clinical neuroscience. Pattern recognition and machine learning conferences regularly feature a neuroimaging workshop, while neuroscientific meetings dedicate sessions to new approaches to neural data analysis. Thus, a rich two-way flow has been established between disciplines. It is the goal of the PRNI workshop series to continue facilitating exchange of ideas between scientific communities, with a particular interest in new app... Moritz Grosse-Wentrup

Empirical Inference IS Colloquium Christian Lubich 26-05-2014 Low-rank dynamics This talk reviews differential equations on manifolds of matrices or tensors of low rank. They serve to approximate, in a low-rank format, large time-dependent matrices and tensors that are either given explicitly via their increments or are unknown solutions of differential equations. Furthermore, low-rank differential equations are used in novel algorithms for eigenvalue optimisation, for instance in robust-stability problems. Philipp Hennig

Max Planck Lecture Professor A. L. Greer 22-05-2014 The Glassy State properties and applications exploiting non-crystallinity: golf, frozen frogs, memory Glasses, lacking the order of crystals, are in many ways still regarded as poorly understood. Yet glasses, lacking the complications of different crystallographic symmetries, also show some remarkable correlations of diverse properties.

Autonomous Motion IS Colloquium Prof. Moritz Diehl 19-05-2014 Embedded Optimization for Nonlinear Model Predictive Control This talk shows how embedded optimization - i.e. autonomous optimization algorithms receiving data, solving problems, and sending answers continuously - are able to address challenging control problems. When nonlinear differential equation models are used to predict and optimize future system behaviour, one speaks of Nonlinear Model Predictive Control (NMPC).The talk presents experimental applications of NMPC to time and energy optimal control of mechatronic systems and discusses some of the algorithmic tricks that make NMPC optimization rates up to 1 MHz possible. Finally, we present on pa... Sebastian Trimpe

Perceiving Systems IS Colloquium Christoph Lampert 12-05-2014 Towards Lifelong Learning for Visual Scene Understanding <p> The goal of lifelong visual learning is to develop techniques that continuously and autonomously learn from visual data, potentially for years or decades. During this time the system should build an ever-improving base of generic visual information, and use it as background knowledge and context for solving specific computer vision tasks. In my talk, I will highlight two recent results from our group on the road towards lifelong visual scene understanding: the derivation of theoretical guarantees for lifelong learning systems and the development of practical methods for object categori... Gerard Pons-Moll

Perceiving Systems Talk Nikolaus Troje 06-05-2014 Depth ambiguity and perceptual biases in biological motion perception <p> Point-light walkers and stick figures rendered orthographically and without self-occlusion do not contain any information as to their depth. For instance, a frontoparallel projection could depict a walker from the front or from the back. Nevertheless, observers show a strong bias towards seeing the walker as facing the viewer. A related stimulus, the silhouette of a human figure, does not seem to show such a bias. We develop these observations into a tool to study the cause of the facing the viewer bias observed for biological motion displays.</p> <p> I will give a short overview ab...

Perceiving Systems IS Colloquium Thomas Brox 05-05-2014 Video Segmentation Compared to static image segmentation, video segmentation is still in its infancy. Various research groups have different tasks in mind when they talk of video segmentation. For some it is motion segmentation, some think of an over-segmentation with thousands of regions per video, and others understand video segmentation as contour tracking. I will go through what I think are reasonable video segmentation subtasks and will touch the issue of benchmarking. I will also discuss the difference between image and video segmentation. Due to the availability of motion and the redundancy of successi... Gerard Pons-Moll

Perceiving Systems Talk Cordelia Schmid 30-04-2014 Large displacement optical flow & flow-based action recognition <p> In the first part of our talk, we present an approach for large displacement optical flow. Optical flow computation is a key component in many computer vision systems designed for tasks such as action<br /> detection or activity  recognition. Inspired by the large displacement optical flow of Brox and  Malik, our approach  DeepFlow  combines a novel matching algorithm with a variational approach . Our matching algorithm builds upon a multi-stage architecture interleaving convolutions and max-pooling.  DeepFlow efficiently handles large displacements&nbsp...

Perceiving Systems IS Colloquium Jiri Matas 28-04-2014 WaldBoost: Combining Sequential Analysis with Machine Learning for Solving Time-constrained Vision Problems Computer vision problems often involve optimization of two quantities, one of which is time. Such problems can be formulated as time-constrained optimization or performance-constrained search for the fastest algorithm. We show that it is possible to obtain quasi-optimal time-constrained solutions to some vision problems by applying Wald's theory of sequential decision-making. Wald assumes independence of observation, which is rarely true in computer vision. We address the problem by combining Wald's sequential probability ratio test and AdaBoost. The solution, called the WaldBoost... Gerard Pons-Moll

Perceiving Systems Talk Daniel Scharstein 10-04-2014 Scalable Surface-Based Stereo Matching Stereo matching -- establishing correspondences between images taken from nearby viewpoints -- is one of the oldest problems in computer vision.  While impressive progress has been made over the last two decades, most current stereo methods do not scale to the high-resolution images taken by today's cameras since they require searching the full space of all possible disparity hypotheses over all pixels. <p> In this talk I will describe a new scalable stereo method that only evaluates a small portion of the search space.  The method first generates plane hypotheses from mat...

Empirical Inference IS Colloquium Simo Särkkä 07-04-2014 State-space representation of gaussian processes for regression and efficient inference in latent force model Gaussian process regression is a non-parametric Bayesian machine learning paradigm, where instead of estimating parameters of fixed-form functions, we model the whole unknown functions as Gaussian processes. Gaussian processes are also commonly used for representing uncertainties in models of dynamic systems in many applications such as tracking, navigation, and automatic control systems. The latter models are often formulated as state-space models, where the use of non-linear Kalman filter type of methods is common. The aim of this talk is to discuss connections of Kalman filtering method... Philipp Hennig

Empirical Inference IS Colloquium Rainer Dahlhaus 31-03-2014 Online Spot Volatility-Estimation and Decomposition with Nonlinear Market Microstructure Noise Models (joint work with Jan. C. Neddermeyer) A technique for online estimation of spot volatility for high-frequency data is developed. The algorithm works directly on the transaction data and updates the volatility estimate immediately after the occurrence of a new transaction. Furthermore, a nonlinear market microstructure noise model is proposed that reproduces several stylized facts of high frequency data. A computationally efficient particle filter is used that allows for the approximation of the unknown efficient prices and, in combination with a recursive EM algorithm, for the estimation ... Michel Besserve

Empirical Inference IS Colloquium Peter Battaglia 28-03-2014 Simulation in physical scene understanding Our ability to understand a scene is central to how we interact with our environment and with each other. Classic research on visual scene perception has focused on how people "know what is where by looking", but this talk will explore people's ability to infer the "hows" and "whys" of their world, and in particular, how they form a physical understanding of a scene. From a glance we can know so much: not only what objects are where, but whether they are movable, fragile, slimy, or hot; whether they were made by hand, by machine, or by nature; whether they are broken and how they could be r... Michel Besserve

Event 27-03-2014 Girls Day Claudia Daefler

Perceiving Systems Talk Stan Sclaroff 27-03-2014 Video-based Analysis of Humans and Their Behavior This talk will give an overview of some of the research in the Image and Video Computing Group at Boston University related to image- and video-based analysis of humans and their behavior, including: tracking humans, localizing and classifying actions in space-time, exploiting contextual cues in action classification, estimating human pose from images, analyzing the communicative behavior of children in video, and sign language recognition and retrieval. <p> Collaborators in this work include (in alphabetical order): Vassilis Athitsos, Qinxun Bai, Margrit Betke, R. Gokberk Cinbis, Kun He,...

Perceiving Systems IS Colloquium Edmond Boyer 20-03-2014 Multi-View Perception of Dynamic Scenes <div style="margin: 0px;"> The INRIA MORPHEO research team is working on the perception of moving shapes using multiple camera systems. Such systems allows to recover dense information on shapes and their motions using visual cues. This opens avenues for research investigations on how to model, understand and animate real dynamic shapes using several videos. In this talk I will more particularly focus on recent activities in the team on two fundamental components of the multi-view perception of dynamic scenes that are: (i) the recovery of time-consistent shape models or shape tracki... Gerard Pons-Moll

Perceiving Systems Talk Prof. Yoshinari Kameda 12-03-2014 Producing free viewpoint 3D video from a real soccer game and its user interface for the virtual camera control <p> This talk presents our 3D video production method by which a user can watch a  real game from any free viewpoint. Players in the game are captured by 10 cameras and they are reproduced three dimensionally by billboard based representation in real time. Upon producing the 3D video, we have also worked on good user interface that can enable people move the camera intuitively. As the speaker is also working on wide variety of computer vision to augmented reality, selected recent works will be also introduced briefly.<br /> <br /> Dr. Yoshinari K...

Perceiving Systems Talk Christof Hoppe 11-02-2014 Interactive and Task-driven Multi-view 3D Reconstruction 3D reconstruction from 2D still-images (Structure-from-Motion) has reached maturity and together with new image acquisition devices like Micro Aerial Vehicles (MAV), new interesting application scenarios arise. However, acquiring an image set which is suited for a complete and accurate reconstruction is even for expert users a non-trivial task. To overcome this problem, we propose two different methods. In the first part of the talk, we will present a SfM method that performs sparse reconstruction of 10Mpx still-images and a surface extraction from sparse and noisy 3D point clouds in real-t...

Perceiving Systems IS Colloquium Bernt Schiele 10-02-2014 Towards Visual Scene Understanding - Articulated Pose Estimation and Video Description <p class="p1"> This talk will highlight recent progress on two fronts. First, we will talk about a novel image-conditioned person model that allows for effective articulated pose estimation in realistic scenarios. Second, we describe our work towards activity recognition and the ability to describe video content with natural language. </p> <p class="p2"> Both efforts are part of a longer-term agenda towards visual scene understanding. While visual scene understanding has long been advocated as the "holy grail" of computer vision, we believe it is time to address this cha...

Perceiving Systems IS Colloquium Pascal Fua 13-01-2014 Identity Preserving Multi-People Tracking through Linear Programming <p> In this talk, I will show that, given probabilities of presence of people at various locations in individual time frames, finding the most likely set of trajectories amounts to solving a linear program that depends on very few parameters.<br /> This can be done without requiring appearance information and in real-time, by using the K-Shortest Paths algorithm (KSP). However, this can result in unwarranted identity switches in complex scenes. In such cases, sparse image information can be used within the Linear Programming framework to keep track of people's identities, even when...

Perceiving Systems Talk Alessandra Tosi 18-11-2013 Local metric approach in Gaussian Processes Latent Variables Models <p> Manifold learning techniques attempt to map a high-dimensional space onto a lower-dimensional one. From a mathematical point of view, a manifold is a topological Hausdorff space that is locally Euclidean. From Machine Learning point of view, we can interpret this embedded manifold as the underlying support of the data distribution. When dealing with high dimensional data sets, nonlinear dimensionality reduction methods can provide more faithful data representation than linear ones. However, the local geometrical distortion induced by the nonlinear mapping leads to a loss of informatio...

Perceiving Systems Talk Sven Dickinson 11-11-2013 Perceptual Grouping using Superpixels <p class="p1"> Perceptual grouping played a prominent role in support of early object recognition systems, which typically took an input image and a database of shape models and identified which of the models was visible in the image.  When the database was large, local features were not sufficiently distinctive to prune down the space of models to a manageable number that could be verified.  However, when causally related shape features were grouped, using intermediate-level shape priors, e.g., cotermination, symmetry, and compactness, they formed effective shape indices and all...

Perceiving Systems Talk Padmanabhan Anandan 14-10-2013 Inventing the Future at Microsoft Research India: Our first 8 years <p> T.b.a.</p>

Perceiving Systems Talk Pierre-Yves Laffont 11-10-2013 Exploring and editing the appearance of outdoor scenes <div> The appearance of outdoor scenes changes dramatically with lighting and weather conditions, time of day, and season. Specific conditions, such as the "golden hours" characterized by warm light, can be hard to capture because many scene properties are transient -- they change over time. Despite significant advances in image editing software, common image manipulation tasks such as lighting editing require significant expertise to achieve plausible results.</div> <div>  </div> <div> In this talk, we first explore the appearance of outdoor scenes with an approach base...

Max Planck Lecture Prof. Dr. Paul Chaikin 10-10-2013 Some Small Steps toward Artificial Life No one has successfully defined life but the properties we often associate with living things are motility, metabolism and self-replication. According to the Nobel Laureate Richard Feynman: "What I can't create, I don't understand". We thought we'd give it a shot – understanding life – and in the process we've made two different systems, one that exhibits both autonomous motility and metabolism and another which is the first artificial system which can replicate arbitrarily designed motifs.

Perceiving Systems Talk Neill Campbell 01-10-2013 Inference in highly-connected CRFs This talk presents recent work from CVPR that looks at inference for pairwise CRF models in the highly (or fully) connected case rather than simply a sparse set of neighbours used ubiquitously in many computer vision tasks. Recent work has shown that fully-connected CRFs, where each node is connected to every other node, can be solved very efficiently under the restriction that the pairwise term is a Gaussian kernel over a Euclidean feature space. The method presented generalises this model to allow arbitrary, non-parametric models (which can be learnt from training data and condi...

Perceiving Systems Talk Bei Xiao 23-09-2013 Human perception of material properties in the real world <p> Humans are very good at recognizing objects as well as the materials that they are made of. We can easily tell cheese from butter, silk from linen and snow from ice just by looking. Understanding material perception is important for many real-world applications. For instance, a robot cooking in the kitchen will benefit from the knowledge of material perception when deciding if food is cooked or raw. In this talk, I will present studies that are motivated by two important applications of material perception: online shopping and computer graphics (CG) rendering. First, I will discuss the...

Perceiving Systems Talk Victor Adrian Prisacariu 23-09-2013 Shape Knowledge in Segmentation and Tracking <p> In this talk I will detail methods for simultaneous 2D/3D segmentation, tracking and reconstruction which incorporate high level shape information. I base my work on the assumption that the space of possible 2D object shapes can be either generated by projecting down known rigid 3D shapes or learned from 2D shape examples. I minimise the discrimination between statistical foreground and background appearance models with respect to the parameters governing the shape generative process (the 6 degree-of-freedom 3D pose of the 3D shape or the parameters of the learned space). The foregroun...

Perceiving Systems Talk Alexander Schwing 12-09-2013 Efficient Inference and Learning for Structured Parameterizations/Models Sensors acquire an increasing amount of diverse information posing two challenges. Firstly, how can we efficiently deal with such a big amount of data and secondly, how can we benefit from this diversity? In this talk I will first present an approach to deal with large graphical models. The presented method distributes and parallelizes the computation and memory requirements while preserving convergence and optimality guarantees of existing inference and learning algorithms. I will demonstrate the effectiveness of the approach on stereo reconstruction from high-resolution imagery. In the se...

Perceiving Systems Talk Jamie Shotton 10-09-2013 Depth, You, and the World <p> Consumer level depth cameras such as Kinect have changed the landscape of 3D computer vision.  In this talk we will discuss two approaches that both learn to directly infer correspondences between observed depth image pixels and 3D model points.  These correspondences can then be used to drive an optimization of a generative model to explain the data.  The first approach, the "Vitruvian Manifold", aims to fit an articulated 3D human model to a depth camera image, and extends our original Body Part Recognition algorithm used in Kinect.  It applies a per-pi...

Perceiving Systems Talk Sanja Fidler 09-09-2013 2D and 3D object detection by exploiting segmentation and contextual information <div> Object detection is one of the main challenges of computer vision. In the standard setting, we are given an image and the goal is to place bounding boxes around the objects and recognize their classes. In robotics, estimating additional information such as accurate viewpoint or detailed segmentation is important for planning and interaction. In this talk, I'll approach detection in three scenarios: purely 2D, 3D from 2D and 3D from 3D and show how different types of information can be used to significantly boost the current state-of-the-art in detection.</div> <div>  </div>

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