Haptic technologies in both kinesthetic and tactile aspects benefit a brand-new opportunity to recent human-machine interactive applications. In this talk, I, who believe in that one of the essential role of a researcher is pioneering new insights and knowledge, will present my previous research topics about haptic technologies and human-machine interactive applications in two branches: laser-based mid-air haptics and sensorimotor skill learning. For the former branch, I will introduce our approach named indirect laser radiation and its application. Indirect laser radiation utilizes a laser and a light-absorbing elastic medium to evoke a tapping-like tactile sensation. For the latter, I will introduce our data-driven approach for both modeling and learning of sensorimotor skills (especially, driving) with kinesthetic assistance and artificial neural networks; I call it human-like haptic assistance. To unify two different branches of my earlier studies for exploring the feasibility of the sensory channel named "touch", I will present a general research paradigm for human-machine interactive applications to which current haptic technologies can aim in future.
Organizers: Katherine J. Kuchenbecker
Needle insertion is the most essential skill in medical care; training has to be imparted not only for physicians but also for nurses and paramedics. In most needle insertion procedures, haptic feedback from the needle is the main stimulus that novices are to be trained in. For better patient safety, the classical methods of training the haptic skills have to be replaced with simulators based on new robotic and graphics technologies. The main objective of this work is to develop analytical models of needle insertion (a special case of epidural anesthesia) including the biomechanical and psychophysical concepts that simulate the needle-tissue interaction forces in linear heterogeneous tissues and to validate the model with a series of experiments. The biomechanical and perception models were validated with experiments in two stages: with and without the human intervention. The second stage is the validation using the Turing test with two different experiments: 1) to observe the perceptual difference between the simulated and the physical phantom model, and 2) to verify the effectiveness of perceptual filter between the unfiltered and filtered model response. The results showed that the model could replicate the physical phantom tissues with good accuracy. This can be further extended to a non-linear heterogeneous model. The proposed needle/tissue interaction force models can be used more often in improving realism, performance and enabling future applications in needle simulators in heterogeneous tissue. Needle insertion training simulator was developed with the simulated models using Omni Phantom and clinical trials are conducted for the face validity and construct validity. The face validity results showed that the degree of realism of virtual environments and instruments had the overall lowest mean score and ease of usage and training in hand – eye coordination had the highest mean score. The construct validity results showed that the simulator was able to successfully differentiate force and psychomotor signatures of anesthesiologists with experiences less than 5 years and more than 5 years. For the performance index of the trainees, a novel measure, Just Controllable Difference (JCD) was proposed and a preliminary study on JCD measure is explored using two experiments for the novice. A preliminary study on the use of clinical training simulations, especially needle insertion procedure in virtual environments is emphasized on two objectives: Firstly, measures of force JND with the three fingers and secondly, comparison of these measures in Non-Immersive Virtual Reality (NIVR) to that of the Immersive Virtual Reality (IVR) using psychophysical study with the Force Matching task, Constant Stimuli method, and Isometric Force Probing stimuli. The results showed a better force JND in the IVR compared to that of the NIVR. Also, a simple state observer model was proposed to explain the improvement of force JND in the IVR. This study would quantitatively reinforce the use of the IVR for the design of various medical simulators.
Organizers: Katherine J. Kuchenbecker
Functional polymers can be easily tailored for their interaction with living organismes. In our Group, we have worked during the last 15 years in the development of this kind of polymeric materials with different funcionalities, high biocompatibility and in different forms. In this talk, we will describe the synthesis of thermosensitive thin films that can be used to prevent biofilm formation in medical devices, the preparation of biodegradable polymers specially designed for vectors for gene transfection and a new familliy of zwitterionic polymers that are able to cross intestine mucouse for oral delivery applications. The relationship between structure-functionality- applications will be discussed for every example.
Organizers: Metin Sitti
Since Hubel and Wiesel's seminal findings in the primary visual cortex (V1) more than 50 years ago, progress in vision science has been very limited along previous frameworks and schools of thoughts on understanding vision. Have we been asking the right questions? I will show observations motivating the new path. First, a drastic information bottleneck forces the brain to process only a tiny fraction of the massive visual input information; this selection is called the attentional selection, how to select this tiny fraction is critical. Second, a large body of evidence has been accumulating to suggest that the primary visual cortex (V1) is where this selection starts, suggesting that the visual cortical areas along the visual pathway beyond V1 must be investigated in light of this selection in V1. Placing attentional selection as the center stage, a new path to understanding vision is proposed (articulated in my book "Understanding vision: theory, models, and data", Oxford University Press 2014). I will show a first example of using this new path, which aims to ask new questions and make fresh progresses. I will relate our insights to artificial vision systems to discuss issues like top-down feedbacks in hierachical processing, analysis-by-synthesis, and image understanding.
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 correspondences, and leads to reasonable reproduction of object appearance in the absence of traditional multi-view cues. Joint work with Qixing Huang and Hai Wang.
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.
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 advantage of touch perception and thus struggle to match the manipulation capabilities of humans. This talk will describe several research projects from Professor Kuchenbecker's laboratory , including data-driven haptic texture rendering, vibrotactile feedback of tool vibrations for robotic surgery , and robotic learning of haptic adjectives
Organizers: Jane Walters
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.
Organizers: Sebastian Trimpe
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 goal is to break down the barriers between simulation techniques and the underlying physical models, and to use the resulting insights to develop realistic methods that remain efficient over a wide range of inputs.
I will cover three areas of recent work: the first involves volumetric modeling approaches to create realistic images of woven and knitted cloth. Next, I will discuss reflectance models for glitter/sparkle effects and arbitrarily layered materials that are specially designed to allow for efficient simulations. In the last part of the talk, I will give an overview of Manifold Exploration, a Markov Chain Monte Carlo technique that is able to reason about the geometric structure of light paths in high dimensional configuration spaces defined by the underlying physical models, and which uses this information to compute images more efficiently.
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.
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.
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 object with other objects in the scene. This makes it challenging to learn detectors based on an object-level labels (e.g., “car”). We postulate that having a richer set of labelings (at different levels of granularity) for an object, including finer-grained sub-categories, consistent in appearance and view, and higher-order composites – contextual groupings of objects consistent in their spatial layout and appearance, can significantly alleviate these problems. However, obtaining such a rich set of annotations, including annotation of an exponentially growing set of object groupings, is infeasible. To this end, we propose a weakly-supervised framework for object detection where we discover subcategories and the composites automatically with only traditional object-level category labels as input.
In the second part of the talk I will focus on the framework for large scale image set and video summarization. Starting from the intuition that the characteristics of the two media types are different but complementary, we develop a fast and easily-parallelizable approach for creating not only video summaries but also novel structural summaries of events in the form of the storyline graphs. The storyline graphs can illustrate various events or activities associated with the topic in the form of a branching directed network. The video summarization is achieved by diversity ranking on the similarity graphs between images and video frame, thereby treating consumer image as essentially a form of weak-supervision. The reconstruction of storyline graphs on the other hand is formulated as inference of the sparse time-varying directed graphs from a set of photo streams with assistance of consumer videos.
Time permitting I will also talk about a few other recent project highlights.
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 falls out of measuring the deviation between the surface and the observed data is also differentiable and can be minimized through standard, albeit tricky, gradient based non-linear optimization from a reasonable initial guess. The latter can often be obtained using machine learning methods when manual intervention is undesirable. Satisfyingly, the "tricks" involved in the former are elegant and widen the applicability of these methods.
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 space of algorithms and their parameters to extract sequences of object proposals. Furthermore, we model similarity of objects based on appearance and functionality, which is derived from human and object motion. We show that functionality is an important cue for discovering objects from activities and demonstrate the generality of the model on three challenging RGB-D and RGB datasets.
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.