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2013


Thumb xl iccv2013 siyu
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, December 2013 (inproceedings)

PDF DOI [BibTex]

2013

PDF DOI [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, 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.

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.

pdf [BibTex]

pdf [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.

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, 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.

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.

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]


Thumb xl embs2013
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.

pdf Project Page [BibTex]

pdf Project Page [BibTex]


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Multi-Robot Cooperative Object Tracking Based on Particle Filters

Ahmad, A., Lima, P.

In Robotics and Autonomous Systems, 61(10):1084-1093, 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.

link (url) DOI [BibTex]

link (url) DOI [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.

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)

pdf [BibTex]

pdf [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, June 2013 (inproceedings)

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.

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.

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.

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.

pdf Supplemental Material Project Page Project Page [BibTex]

pdf Supplemental Material Project Page Project Page [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, 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.

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, 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.

DOI [BibTex]

DOI [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, 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.

DOI [BibTex]

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)

pdf [BibTex]

pdf [BibTex]


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Estimating Human Pose with Flowing Puppets

Zuffi, S., Romero, J., Schmid, C., Black, M. J.

In IEEE International Conference on Computer Vision (ICCV), pages: 3312-3319, 2013 (inproceedings)

Abstract
We address the problem of upper-body human pose estimation in uncontrolled monocular video sequences, without manual initialization. Most current methods focus on isolated video frames and often fail to correctly localize arms and hands. Inferring pose over a video sequence is advantageous because poses of people in adjacent frames exhibit properties of smooth variation due to the nature of human and camera motion. To exploit this, previous methods have used prior knowledge about distinctive actions or generic temporal priors combined with static image likelihoods to track people in motion. Here we take a different approach based on a simple observation: Information about how a person moves from frame to frame is present in the optical flow field. We develop an approach for tracking articulated motions that "links" articulated shape models of people in adjacent frames trough the dense optical flow. Key to this approach is a 2D shape model of the body that we use to compute how the body moves over time. The resulting "flowing puppets" provide a way of integrating image evidence across frames to improve pose inference. We apply our method on a challenging dataset of TV video sequences and show state-of-the-art performance.

pdf code data DOI Project Page Project Page Project Page [BibTex]

pdf code data DOI Project Page Project Page Project Page [BibTex]


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Right Ventricle Segmentation by Temporal Information Constrained Gradient Vector Flow

X. Yang, S. Y. Yeo, Y. Su, C. Lim, M. Wan, L. Zhong, R. S. Tan

In IEEE International Conference on Systems, Man, and Cybernetics, 2013 (inproceedings)

Abstract
Evaluation of right ventricular (RV) structure and function is of importance in the management of most cardiac disorders. But the segmentation of RV has always been consid- ered challenging due to low contrast of the myocardium with surrounding and high shape variability of the RV. In this paper, we present a 2D + T active contour model for segmentation and tracking of RV endocardium on cardiac magnetic resonance (MR) images. To take into account the temporal information between adjacent frames, we propose to integrate the time-dependent constraints into the energy functional of the classical gradient vector flow (GVF). As a result, the prior motion knowledge of RV is introduced in the deformation process through the time-dependent constraints in the proposed GVF-T model. A weighting parameter is introduced to adjust the weight of the temporal information against the image data itself. The additional external edge forces retrieved from the temporal constraints may be useful for the RV segmentation, such that lead to a better segmentation performance. The effectiveness of the proposed approach is supported by experimental results on synthetic and cardiac MR images.

[BibTex]

[BibTex]


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A Comparison of Directional Distances for Hand Pose Estimation

Tzionas, D., Gall, J.

In German Conference on Pattern Recognition (GCPR), 8142, pages: 131-141, Lecture Notes in Computer Science, (Editors: Weickert, Joachim and Hein, Matthias and Schiele, Bernt), Springer, 2013 (inproceedings)

Abstract
Benchmarking methods for 3d hand tracking is still an open problem due to the difficulty of acquiring ground truth data. We introduce a new dataset and benchmarking protocol that is insensitive to the accumulative error of other protocols. To this end, we create testing frame pairs of increasing difficulty and measure the pose estimation error separately for each of them. This approach gives new insights and allows to accurately study the performance of each feature or method without employing a full tracking pipeline. Following this protocol, we evaluate various directional distances in the context of silhouette-based 3d hand tracking, expressed as special cases of a generalized Chamfer distance form. An appropriate parameter setup is proposed for each of them, and a comparative study reveals the best performing method in this context.

pdf Supplementary Project Page link (url) DOI Project Page [BibTex]

pdf Supplementary Project Page link (url) DOI Project Page [BibTex]


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Dynamic Probabilistic Volumetric Models

Ulusoy, A. O., Biris, O., Mundy, J. L.

In ICCV, pages: 505-512, 2013 (inproceedings)

Abstract
This paper presents a probabilistic volumetric framework for image based modeling of general dynamic 3-d scenes. The framework is targeted towards high quality modeling of complex scenes evolving over thousands of frames. Extensive storage and computational resources are required in processing large scale space-time (4-d) data. Existing methods typically store separate 3-d models at each time step and do not address such limitations. A novel 4-d representation is proposed that adaptively subdivides in space and time to explain the appearance of 3-d dynamic surfaces. This representation is shown to achieve compression of 4-d data and provide efficient spatio-temporal processing. The advances of the proposed framework is demonstrated on standard datasets using free-viewpoint video and 3-d tracking applications.

video pdf DOI [BibTex]

video pdf DOI [BibTex]


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Model Reconstruction of Patient-Specific Cardiac Mesh from Segmented Contour Lines

C. W. Lim, Y. Su, S. Y. Yeo, G. M. Ng, V. T. Nguyen, L. Zhong, R. S. Tan, K. K. Poh, P. Chai,

In Asia Pacific Congress on Computational Mechanics, 2013 (inproceedings)

Abstract
We propose an automatic algorithm for the reconstruction of a set of patient-specific dynamic cardiac mesh model with 1-to-1 mesh correspondence over the whole cardiac cycle. This work focus on both the reconstruction technique of the initial 3D model of the heart and also the consistent mapping of the vertex positions throughout all the 3D meshes. This process is technically more challenging due to the wide interval spacing between MRI images as compared to CT images, making overlapping blood vessels much harder to discern. We propose a tree-based connectivity data structure to perform a filtering process to eliminate weak connections between contours on adjacent slices. The reconstructed 3D model from the first time step is used as a base template model, and deformed to fit the segmented contours in the next time step. Our algorithm has been tested on an actual acquisition of cardiac MRI images over one cardiac cycle.

[BibTex]

[BibTex]


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A Generic Deformation Model for Dense Non-Rigid Surface Registration: a Higher-Order MRF-based Approach

Zeng, Y., Wang, C., Gu, X., Samaras, D., Paragios, N.

In IEEE International Conference on Computer Vision (ICCV), pages: 3360~3367, 2013 (inproceedings)

pdf [BibTex]

pdf [BibTex]


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Nonlinearly Constrained MRFs: Exploring the Intrinsic Dimensions of Higher-Order Cliques

Zeng, Y., Wang, C., Soatto, S., Yau, S.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (inproceedings)

pdf [BibTex]

pdf [BibTex]


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Reconstructing patient-specific cardiac models from contours via Delaunay triangulation and graph-cuts

Min Wan, Calvin Lim, Junmei Zhang, Yi Su, Si Yong Yeo, Desheng Wang, Ru San Tan, Liang Zhong

In International Conference of the IEEE Engineering in Medicine and Biology Society, pages: 2976-9, 2013 (inproceedings)

[BibTex]

[BibTex]


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Regional comparison of left ventricle systolic wall stress reveals intraregional uniformity in healthy subjects

Soo Kng Teo, Si Yong Yeo, May Ling Tan, Chi Wan Lim, Liang Zhong, Ru San Tan, Yi Su

In Computing in Cardiology Conference, pages: 575 - 578, 2013 (inproceedings)

Abstract
This study aimed to assess the feasibility of using the regional uniformity of the left ventricle (LV) wall stress (WS) to diagnose patients with myocardial infarction. We present a novel method using a similarity map that measures the degree of uniformity in nominal systolic WS across pairs of segments within the same patient. The values of the nominal WS are computed at each vertex point from a 1-to-1 corresponding mesh pair of the LV at the end-diastole (ED) and end-systole (ES) phases. The 3D geometries of the LV at ED and ES are reconstructed from border-delineated MRI images and the 1-to-1 mesh generated using a strain-energy minimization approach. The LV is then partitioned into 16 segments based on published clinical standard and the nominal WS histogram distribution for each of the segment was computed. A similarity index is then computed for each pair of histogram distributions to generate a 16-by-16 similarity map. Based on our initial study involving 12 MI patients and 9 controls, we observed uniformity for intra- regional comparisons in the controls compared against the patients. Our results suggest that the regional uniformity of the nominal systolic WS in the form of a similarity map can potentially be used as a discriminant between MI patients and normal controls.

[BibTex]

[BibTex]

2002


Thumb xl bildschirmfoto 2013 01 15 um 09.54.19
Inferring hand motion from multi-cell recordings in motor cortex using a Kalman filter

Wu, W., Black, M. J., Gao, Y., Bienenstock, E., Serruya, M., Donoghue, J. P.

In SAB’02-Workshop on Motor Control in Humans and Robots: On the Interplay of Real Brains and Artificial Devices, pages: 66-73, Edinburgh, Scotland (UK), August 2002 (inproceedings)

pdf [BibTex]

2002

pdf [BibTex]


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Inferring hand motion from multi-cell recordings in motor cortex using a Kalman filter

Wu, W., Black M., Gao, Y., Bienenstock, E., Serruya, M., Donoghue, J.

Program No. 357.5. 2002 Abstract Viewer/Itinerary Planner, Society for Neuroscience, Washington, DC, 2002, Online (conference)

abstract [BibTex]

abstract [BibTex]


Thumb xl bildschirmfoto 2012 12 11 um 09.50.58
Automatic detection and tracking of human motion with a view-based representation

Fablet, R., Black, M. J.

In European Conf. on Computer Vision, ECCV 2002, 1, pages: 476-491, LNCS 2353, (Editors: A. Heyden and G. Sparr and M. Nielsen and P. Johansen), Springer-Verlag , 2002 (inproceedings)

Abstract
This paper proposes a solution for the automatic detection and tracking of human motion in image sequences. Due to the complexity of the human body and its motion, automatic detection of 3D human motion remains an open, and important, problem. Existing approaches for automatic detection and tracking focus on 2D cues and typically exploit object appearance (color distribution, shape) or knowledge of a static background. In contrast, we exploit 2D optical flow information which provides rich descriptive cues, while being independent of object and background appearance. To represent the optical flow patterns of people from arbitrary viewpoints, we develop a novel representation of human motion using low-dimensional spatio-temporal models that are learned using motion capture data of human subjects. In addition to human motion (the foreground) we probabilistically model the motion of generic scenes (the background); these statistical models are defined as Gibbsian fields specified from the first-order derivatives of motion observations. Detection and tracking are posed in a principled Bayesian framework which involves the computation of a posterior probability distribution over the model parameters (i.e., the location and the type of the human motion) given a sequence of optical flow observations. Particle filtering is used to represent and predict this non-Gaussian posterior distribution over time. The model parameters of samples from this distribution are related to the pose parameters of a 3D articulated model (e.g. the approximate joint angles and movement direction). Thus the approach proves suitable for initializing more complex probabilistic models of human motion. As shown by experiments on real image sequences, our method is able to detect and track people under different viewpoints with complex backgrounds.

pdf [BibTex]

pdf [BibTex]


Thumb xl bildschirmfoto 2012 12 11 um 10.06.33
A layered motion representation with occlusion and compact spatial support

Fleet, D. J., Jepson, A., Black, M. J.

In European Conf. on Computer Vision, ECCV 2002, 1, pages: 692-706, LNCS 2353, (Editors: A. Heyden and G. Sparr and M. Nielsen and P. Johansen), Springer-Verlag , 2002 (inproceedings)

Abstract
We describe a 2.5D layered representation for visual motion analysis. The representation provides a global interpretation of image motion in terms of several spatially localized foreground regions along with a background region. Each of these regions comprises a parametric shape model and a parametric motion model. The representation also contains depth ordering so visibility and occlusion are rightly included in the estimation of the model parameters. Finally, because the number of objects, their positions, shapes and sizes, and their relative depths are all unknown, initial models are drawn from a proposal distribution, and then compared using a penalized likelihood criterion. This allows us to automatically initialize new models, and to compare different depth orderings.

pdf [BibTex]

pdf [BibTex]


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Implicit probabilistic models of human motion for synthesis and tracking

Sidenbladh, H., Black, M. J., Sigal, L.

In European Conf. on Computer Vision, 1, pages: 784-800, 2002 (inproceedings)

Abstract
This paper addresses the problem of probabilistically modeling 3D human motion for synthesis and tracking. Given the high dimensional nature of human motion, learning an explicit probabilistic model from available training data is currently impractical. Instead we exploit methods from texture synthesis that treat images as representing an implicit empirical distribution. These methods replace the problem of representing the probability of a texture pattern with that of searching the training data for similar instances of that pattern. We extend this idea to temporal data representing 3D human motion with a large database of example motions. To make the method useful in practice, we must address the problem of efficient search in a large training set; efficiency is particularly important for tracking. Towards that end, we learn a low dimensional linear model of human motion that is used to structure the example motion database into a binary tree. An approximate probabilistic tree search method exploits the coefficients of this low-dimensional representation and runs in sub-linear time. This probabilistic tree search returns a particular sample human motion with probability approximating the true distribution of human motions in the database. This sampling method is suitable for use with particle filtering techniques and is applied to articulated 3D tracking of humans within a Bayesian framework. Successful tracking results are presented, along with examples of synthesizing human motion using the model.

pdf [BibTex]

pdf [BibTex]


Thumb xl bildschirmfoto 2012 12 11 um 10.29.56
Robust parameterized component analysis: Theory and applications to 2D facial modeling

De la Torre, F., Black, M. J.

In European Conf. on Computer Vision, ECCV 2002, 4, pages: 653-669, LNCS 2353, Springer-Verlag, 2002 (inproceedings)

pdf [BibTex]

pdf [BibTex]


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Probabilistic inference of hand motion from neural activity in motor cortex

Gao, Y., Black, M. J., Bienenstock, E., Shoham, S., Donoghue, J.

In Advances in Neural Information Processing Systems 14, pages: 221-228, MIT Press, 2002 (inproceedings)

pdf [BibTex]

pdf [BibTex]

1998


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The Digital Office: Overview

Black, M., Berard, F., Jepson, A., Newman, W., Saund, E., Socher, G., Taylor, M.

In AAAI Spring Symposium on Intelligent Environments, pages: 1-6, Stanford, March 1998 (inproceedings)

pdf [BibTex]

1998

pdf [BibTex]


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A framework for modeling appearance change in image sequences

Black, M. J., Fleet, D. J., Yacoob, Y.

In Sixth International Conf. on Computer Vision, ICCV’98, pages: 660-667, Mumbai, India, January 1998 (inproceedings)

Abstract
Image "appearance" may change over time due to a variety of causes such as 1) object or camera motion; 2) generic photometric events including variations in illumination (e.g. shadows) and specular reflections; and 3) "iconic changes" which are specific to the objects being viewed and include complex occlusion events and changes in the material properties of the objects. We propose a general framework for representing and recovering these "appearance changes" in an image sequence as a "mixture" of different causes. The approach generalizes previous work on optical flow to provide a richer description of image events and more reliable estimates of image motion.

pdf video [BibTex]

pdf video [BibTex]


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Parameterized modeling and recognition of activities

Yacoob, Y., Black, M. J.

In Sixth International Conf. on Computer Vision, ICCV’98, pages: 120-127, Mumbai, India, January 1998 (inproceedings)

Abstract
A framework for modeling and recognition of temporal activities is proposed. The modeling of sets of exemplar activities is achieved by parameterizing their representation in the form of principal components. Recognition of spatio-temporal variants of modeled activities is achieved by parameterizing the search in the space of admissible transformations that the activities can undergo. Experiments on recognition of articulated and deformable object motion from image motion parameters are presented.

pdf [BibTex]

pdf [BibTex]


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Motion feature detection using steerable flow fields

Fleet, D. J., Black, M. J., Jepson, A. D.

In IEEE Conf. on Computer Vision and Pattern Recognition, CVPR-98, pages: 274-281, IEEE, Santa Barbara, CA, 1998 (inproceedings)

Abstract
The estimation and detection of occlusion boundaries and moving bars are important and challenging problems in image sequence analysis. Here, we model such motion features as linear combinations of steerable basis flow fields. These models constrain the interpretation of image motion, and are used in the same way as translational or affine motion models. We estimate the subspace coefficients of the motion feature models directly from spatiotemporal image derivatives using a robust regression method. From the subspace coefficients we detect the presence of a motion feature and solve for the orientation of the feature and the relative velocities of the surfaces. Our method does not require the prior computation of optical flow and recovers accurate estimates of orientation and velocity.

pdf [BibTex]

pdf [BibTex]


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Visual surveillance of human activity

L. Davis, S. F., Harwood, D., Yacoob, Y., Hariatoglu, I., Black, M.

In Asian Conference on Computer Vision, ACCV, 1998 (inproceedings)

pdf [BibTex]

pdf [BibTex]


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A Probabilistic framework for matching temporal trajectories: Condensation-based recognition of gestures and expressions

Black, M. J., Jepson, A. D.

In European Conf. on Computer Vision, ECCV-98, pages: 909-924, Freiburg, Germany, 1998 (inproceedings)

pdf [BibTex]

pdf [BibTex]


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Recognizing temporal trajectories using the Condensation algorithm

Black, M. J., Jepson, A. D.

In Int. Conf. on Automatic Face and Gesture Recognition, pages: 16-21, Nara, Japan, 1998 (inproceedings)

pdf [BibTex]

pdf [BibTex]

1994


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Estimating multiple independent motions in segmented images using parametric models with local deformations

Black, M. J., Jepson, A.

In Workshop on Non-rigid and Articulate Motion, pages: 220-227, Austin, Texas, November 1994 (inproceedings)

pdf abstract [BibTex]

1994

pdf abstract [BibTex]


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Time to contact from active tracking of motion boundaries

Ju, X., Black, M. J.

In Intelligent Robots and Computer Vision XIII: 3D Vision, Product Inspection, and Active Vision, pages: 26-37, Proc. SPIE 2354, Boston, Massachusetts, November 1994 (inproceedings)

pdf abstract [BibTex]

pdf abstract [BibTex]


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The outlier process: Unifying line processes and robust statistics

Black, M., Rangarajan, A.

In IEEE Conf. on Computer Vision and Pattern Recognition, CVPR’94, pages: 15-22, Seattle, WA, June 1994 (inproceedings)

pdf abstract [BibTex]

pdf abstract [BibTex]


Thumb xl bildschirmfoto 2013 01 14 um 11.42.57
Recursive non-linear estimation of discontinuous flow fields

Black, M.

In Proc. Third European Conf. on Computer Vision, ECCV’94,, pages: 138-145, LNCS 800, Springer Verlag, Sweden, May 1994 (inproceedings)

pdf abstract [BibTex]

pdf abstract [BibTex]

1991


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Dynamic motion estimation and feature extraction over long image sequences

Black, M. J., Anandan, P.

In Proc. IJCAI Workshop on Dynamic Scene Understanding, Sydney, Australia, August 1991 (inproceedings)

[BibTex]

1991

[BibTex]


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Robust dynamic motion estimation over time

(IEEE Computer Society Outstanding Paper Award)

Black, M. J., Anandan, P.

In Proc. Computer Vision and Pattern Recognition, CVPR-91,, pages: 296-302, Maui, Hawaii, June 1991 (inproceedings)

Abstract
This paper presents a novel approach to incrementally estimating visual motion over a sequence of images. We start by formulating constraints on image motion to account for the possibility of multiple motions. This is achieved by exploiting the notions of weak continuity and robust statistics in the formulation of the minimization problem. The resulting objective function is non-convex. Traditional stochastic relaxation techniques for minimizing such functions prove inappropriate for the task. We present a highly parallel incremental stochastic minimization algorithm which has a number of advantages over previous approaches. The incremental nature of the scheme makes it truly dynamic and permits the detection of occlusion and disocclusion boundaries.

pdf video abstract [BibTex]

pdf video abstract [BibTex]