29 results
(View BibTeX file of all listed publications)
2017
Decentralized Simultaneous Multi-target Exploration using a Connected Network of Multiple Robots
Nestmeyer, T., Robuffo Giordano, P., Bülthoff, H. H., Franchi, A.
In pages: 989-1011, Autonomous Robots, 2017 (incollection)
2014
Learning People Detectors for Tracking in Crowded Scenes.
Tang, S., Andriluka, M., Milan, A., Schindler, K., Roth, S., Schiele, B.
2014, Scene Understanding Workshop (SUNw, CVPR workshop) (unpublished)
Simulated Annealing
In Encyclopedia of Computer Vision, pages: 737-741, 0, (Editors: Ikeuchi, K. ), Springer Verlag, 2014, to appear (inbook)
2013
A Study of X-Ray Image Perception for Pneumoconiosis Detection
IIIT-Hyderabad, Hyderabad, India, January 2013 (mastersthesis)
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.
Modeling Shapes with Higher-Order Graphs: Theory and Applications
Wang, C., Zeng, Y., Samaras, D., Paragios, N.
In Shape Perception in Human and Computer Vision: An Interdisciplinary Perspective, (Editors: Zygmunt Pizlo and Sven Dickinson), Springer, 2013 (incollection)
Class-Specific Hough Forests for Object Detection
Gall, J., Lempitsky, V.
In Decision Forests for Computer Vision and Medical Image Analysis, pages: 143-157, 11, (Editors: Criminisi, A. and Shotton, J.), Springer, 2013 (incollection)
Image Gradient Based Level Set Methods in 2D and 3D
Xianhua Xie, Si Yong Yeo, Majid Mirmehdi, Igor Sazonov, Perumal Nithiarasu
In Deformation Models: Tracking, Animation and Applications, pages: 101-120, 0, (Editors: Manuel González Hidalgo and Arnau Mir Torres and Javier Varona Gómez), Springer, 2013 (inbook)
This chapter presents an image gradient based approach to perform 2D and 3D deformable model segmentation using level set. The 2D method uses an external force field that is based on magnetostatics and hypothesized magnetic interactions between the active contour and object boundaries. The major contribution of the method is that the interaction of its forces can greatly improve the active contour in capturing complex geometries and dealing with difficult initializations, weak edges and broken boundaries. This method is then generalized to 3D by reformulating its external force based on geometrical interactions between the relative geometries of the deformable model and the object boundary characterized by image gradient. The evolution of the deformable model is solved using the level set method so that topological changes are handled automatically. The relative geometrical configurations between the deformable model and the object boundaries contribute to a dynamic vector force field that changes accordingly as the deformable model evolves. The geometrically induced dynamic interaction force has been shown to greatly improve the deformable model performance in acquiring complex geometries and highly concave boundaries, and it gives the deformable model a high invariancy in initialization configurations. The voxel interactions across the whole image domain provide a global view of the object boundary representation, giving the external force a long attraction range. The bidirectionality of the external force field allows the new deformable model to deal with arbitrary cross-boundary initializations, and facilitates the handling of weak edges and broken boundaries.
2012
An Analysis of Successful Approaches to Human Pose Estimation
An Analysis of Successful Approaches to Human Pose Estimation, University of Augsburg, University of Augsburg, May 2012 (mastersthesis)
The field of Human Pose Estimation is developing fast and lately leaped forward
with the release of the Kinect system. That system reaches a very good perfor-
mance for pose estimation using 3D scene information, however pose estimation
from 2D color images is not solved reliably yet. There is a vast amount of pub-
lications trying to reach this aim, but no compilation of important methods and
solution strategies. The aim of this thesis is to fill this gap: it gives an introductory
overview over important techniques by analyzing four current (2012) publications
in detail. They are chosen such, that during their analysis many frequently used
techniques for Human Pose Estimation can be explained. The thesis includes two
introductory chapters with a definition of Human Pose Estimation and exploration
of the main difficulties, as well as a detailed explanation of frequently used methods.
A final chapter presents some ideas on how parts of the analyzed approaches can
be recombined and shows some open questions that can be tackled in future work.
The thesis is therefore a good entry point to the field of Human Pose Estimation
and enables the reader to get an impression of the current state-of-the-art.
Exploiting pedestrian interaction via global optimization and social behaviors
Leal-Taixé, L., Pons-Moll, G., Rosenhahn, B.
In Theoretic Foundations of Computer Vision: Outdoor and Large-Scale Real-World Scene Analysis, Springer, April 2012 (incollection)
Data-driven Manifolds for Outdoor Motion Capture
Pons-Moll, G., Leal-Taix’e, L., Gall, J., Rosenhahn, B.
In Outdoor and Large-Scale Real-World Scene Analysis, 7474, pages: 305-328, LNCS, (Editors: Dellaert, Frank and Frahm, Jan-Michael and Pollefeys, Marc and Rosenhahn, Bodo and Leal-Taix’e, Laura), Springer, 2012 (incollection)
Scan-Based Flow Modelling in Human Upper Airways
Perumal Nithiarasu, Igor Sazonov, Si Yong Yeo
In Patient-Specific Modeling in Tomorrow’s Medicine, pages: 241 - 280, 0, (Editors: Amit Gefen), Springer, 2012 (inbook)
An Introduction to Random Forests for Multi-class Object Detection
Gall, J., Razavi, N., van Gool, L.
In Outdoor and Large-Scale Real-World Scene Analysis, 7474, pages: 243-263, LNCS, (Editors: Dellaert, Frank and Frahm, Jan-Michael and Pollefeys, Marc and Rosenhahn, Bodo and Leal-Taix’e, Laura), Springer, 2012 (incollection)
Home 3D body scans from noisy image and range data
Weiss, A., Hirshberg, D., Black, M. J.
In Consumer Depth Cameras for Computer Vision: Research Topics and Applications, pages: 99-118, 6, (Editors: Andrea Fossati and Juergen Gall and Helmut Grabner and Xiaofeng Ren and Kurt Konolige), Springer-Verlag, 2012 (incollection)
2011
Steerable random fields for image restoration and inpainting
Roth, S., Black, M. J.
In Markov Random Fields for Vision and Image Processing, pages: 377-387, (Editors: Blake, A. and Kohli, P. and Rother, C.), MIT Press, 2011 (incollection)
This chapter introduces the concept of a Steerable Random Field (SRF). In contrast to traditional Markov random field (MRF) models in low-level vision, the random field potentials of a SRF are defined in terms of filter responses that are steered to the local image structure. This steering uses the structure tensor to obtain derivative responses that are either aligned with, or orthogonal to, the predominant local image structure. Analysis of the statistics of these steered filter responses in natural images leads to the model proposed here. Clique potentials are defined over steered filter responses using a Gaussian scale mixture model and are learned from training data. The SRF model connects random fields with anisotropic regularization and provides a statistical motivation for the latter. Steering the random field to the local image structure improves image denoising and inpainting performance compared with traditional pairwise MRFs.
Benchmark datasets for pose estimation and tracking
Andriluka, M., Sigal, L., Black, M. J.
In Visual Analysis of Humans: Looking at People, pages: 253-274, (Editors: Moesland and Hilton and Kr"uger and Sigal), Springer-Verlag, London, 2011 (incollection)
Fields of experts
Roth, S., Black, M. J.
In Markov Random Fields for Vision and Image Processing, pages: 297-310, (Editors: Blake, A. and Kohli, P. and Rother, C.), MIT Press, 2011 (incollection)
Fields of Experts are high-order Markov random field (MRF) models with potential functions that extend over large pixel neighborhoods. The clique potentials are modeled as a Product of Experts using nonlinear functions of many linear filter responses. In contrast to previous MRF approaches, all parameters, including the linear filters themselves, are learned from training data. A Field of Experts (FoE) provides a generic, expressive image prior that can capture the statistics of natural scenes, and can be used for a variety of machine vision tasks. The capabilities of FoEs are demonstrated with two example applications, image denoising and image inpainting, which are implemented using a simple, approximate inference scheme. While the FoE model is trained on a generic image database and is not tuned toward a specific application, the results compete with specialized techniques.
Model-Based Pose Estimation
Pons-Moll, G., Rosenhahn, B.
In Visual Analysis of Humans: Looking at People, pages: 139-170, 9, (Editors: T. Moeslund, A. Hilton, V. Krueger, L. Sigal), Springer, 2011 (inbook)
2009
An introduction to Kernel Learning Algorithms
In Kernel Methods for Remote Sensing Data Analysis, pages: 25-48, 2, (Editors: Gustavo Camps-Valls and Lorenzo Bruzzone), Wiley, New York, NY, USA, 2009 (inbook)
Kernel learning algorithms are currently becoming a standard tool in the area of machine learning and pattern recognition.
In this chapter we review the fundamental theory of kernel learning. As the basic building block we introduce the kernel function,
which provides an elegant and general way to compare possibly very complex objects. We then review the concept
of a reproducing kernel Hilbert space and state the representer theorem. Finally we give an overview of the most
prominent algorithms, which are support vector classification and regression, Gaussian Processes and kernel principal analysis.
With multiple kernel learning and structured output prediction we also introduce some more recent advancements in the field.
Visual Object Discovery
Sinha, P., Balas, B., Ostrovsky, Y., Wulff, J.
In Object Categorization: Computer and Human Vision Perspectives, pages: 301-323, (Editors: S. J. Dickinson, A. Leonardis, B. Schiele, M.J. Tarr), Cambridge University Press, 2009 (inbook)
2007
Probabilistically modeling and decoding neural population activity in motor cortex
Black, M. J., Donoghue, J. P.
In Toward Brain-Computer Interfacing, pages: 147-159, (Editors: Dornhege, G. and del R. Millan, J. and Hinterberger, T. and McFarland, D. and Muller, K.-R.), MIT Press, London, 2007 (incollection)
2006
Products of “Edge-perts”
Gehler, P., Welling, M.
In Advances in Neural Information Processing Systems 18, pages: 419-426, (Editors: Weiss, Y. and Sch"olkopf, B. and Platt, J.), MIT Press, Cambridge, MA, 2006 (incollection)
2004
Development of neural motor prostheses for humans
Donoghue, J., Nurmikko, A., Friehs, G., Black, M.
In Advances in Clinical Neurophysiology, (Editors: Hallett, M. and Phillips, L.H. and Schomer, D.L. and Massey, J.M.), Supplements to Clinical Neurophysiology Vol. 57, 2004 (incollection)
2002
Bayesian Inference of Visual Motion Boundaries
Fleet, D. J., Black, M. J., Nestares, O.
In Exploring Artificial Intelligence in the New Millennium, pages: 139-174, (Editors: Lakemeyer, G. and Nebel, B.), Morgan Kaufmann Pub., July 2002 (incollection)
This chapter addresses an open problem in visual motion analysis, the estimation of image motion in the vicinity of occlusion boundaries. With a Bayesian formulation, local image motion is explained in terms of multiple, competing, nonlinear models, including models for smooth (translational) motion and for motion boundaries. The generative model for motion boundaries explicitly encodes the orientation of the boundary, the velocities on either side, the motion of the occluding edge over time, and the appearance/disappearance of pixels at the boundary. We formulate the posterior probability distribution over the models and model parameters, conditioned on the image sequence. Approximate inference is achieved with a combination of tools: A Bayesian filter provides for online computation; factored sampling allows us to represent multimodal non-Gaussian distributions and to propagate beliefs with nonlinear dynamics from one time to the next; and mixture models are used to simplify the computation of joint prediction distributions in the Bayesian filter. To efficiently represent such a high-dimensional space, we also initialize samples using the responses of a low-level motion-discontinuity detector. The basic formulation and computational model provide a general probabilistic framework for motion estimation with multiple, nonlinear models.
1999
Artscience Sciencart
Black, M. J., Levy, D., PamelaZ,
In Art and Innovation: The Xerox PARC Artist-in-Residence Program, pages: 244-300, (Editors: Harris, C.), MIT-Press, 1999 (incollection)
One of the effects of the PARC Artist In Residence (PAIR) program has been to expose the strong connections between scientists and artists. Both do what they do because they need to do it. They are often called upon to justify their work in order to be allowed to continue to do it. They need to justify it to funders, to sponsoring institutions, corporations, the government, the public. They publish papers, teach workshops, and write grants touting the educational or health benefits of what they do. All of these things are to some extent valid, but the fact of the matter is: artists and scientists do their work because they are driven to do it. They need to explore and create.
This chapter attempts to give a flavor of one multi-way "PAIRing" between performance artist PamelaZ and two PARC researchers, Michael Black and David Levy. The three of us paired up because we found each other interesting. We chose each other. While most artists in the program are paired with a single researcher Pamela jokingly calls herself a bigamist for choosing two PAIR "husbands" with different backgrounds and interests.
There are no "rules" to the PAIR program; no one told us what to do with our time. Despite this we all had a sense that we needed to produce something tangible during Pamela's year-long residency. In fact, Pamela kept extending her residency because she did not feel as though we had actually made anything concrete. The interesting thing was that all along we were having great conversations, some of which Pamela recorded. What we did not see at the time was that it was these conversations between artists and scientists that are at the heart of the PAIR program and that these conversations were changing the way we thought about our own work and the relationships between science and art.
To give these conversations their due, and to allow the reader into our PAIR interactions, we include two of our many conversations in this chapter.
1998
Looking at people in action - An overview
Yacoob, Y., Davis, L. S., Black, M., Gavrila, D., Horprasert, T., Morimoto, C.
In Computer Vision for Human–Machine Interaction, (Editors: R. Cipolla and A. Pentland), Cambridge University Press, 1998 (incollection)
Using computers to watch human activity has proven to be a research area having not only a large number of potentially important applications (in surveillance, communications, health, etc.) but also one the had led to a variety of new, fundamental problems in image processing and computer vision. In this chapter we review research that has been conducted at the University of Maryland during the past five years on various topics involving analysis of human activity.
1997
Recognizing human motion using parameterized models of optical flow
Black, M. J., Yacoob, Y., Ju, X. S.
In Motion-Based Recognition, pages: 245-269, (Editors: Mubarak Shah and Ramesh Jain,), Kluwer Academic Publishers, Boston, MA, 1997 (incollection)
The tracking and recognition of human motion is a challenging problem
with diverse applications in virtual reality, medicine, teleoperations, animation, and human-computer interaction to name a few. The study of
human motion has a long history with the use of images for analyzing animate motion beginning with the improvements in photography and the
development of motion-pictures in the late nineteenth century. Scientists
and artists such as Marey [12] and Muybridge [26] were early explorers of
human and animal motion in images and image sequences. Today, commercial motion-capture systems can be used to accurately record the 3D
movements of an instrumented person, but the motion analysis and motion
recognition of an arbitrary person in a video sequence remains an unsolved
problem. In this chapter we describe the representation and recognition
of human motion using parameterized models of optical flow. A person's
limbs, face, and facial features are represented as patches whose motion
in an image sequence can be modeled by low-order polynomials. A robust
optical flow estimation technique is used to recover the motion of these
patches and the recovered motion parameters provide a rich, yet concise,
description of the human motion which can be used to recognize human
activities, gestures, and facial expressions.
1993
Mixture models for optical flow computation
Jepson, A., Black, M.
In Partitioning Data Sets, DIMACS Workshop, pages: 271-286, (Editors: Ingemar Cox, Pierre Hansen, and Bela Julesz), AMS Pub, Providence, RI., April 1993 (incollection)