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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]


Thumb xl screenshot area 2015 07 27 004304
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]


Thumb xl bmvc teaser
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]


Thumb xl deqingcvpr13b
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]


no image
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]


Thumb xl shapeinvariance bookchapter2012
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)

Publishers site [BibTex]

Publishers site [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]


Thumb xl pic cdc iccv13
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]


Thumb xl ncmrf cvpr2013
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]


Thumb xl embs1
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]


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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)

code Project Page [BibTex]

code Project Page [BibTex]


Thumb xl dfmdv1
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)

Abstract
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.

[BibTex]

[BibTex]

2012


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Assessment of Computational Visual Attention Models on Medical Images

Jampani, V., Ujjwal, , Sivaswamy, J., Vaidya, V.

Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing, pages: 80:1-80:8, ACM, Mumbai, India, December 2012 (conference)

Abstract
Visual attention plays a major role in our lives. Our very perception (which very much decides our survival) depends on it - like perceiving a predator while walking through a forest, perceiving a fast car coming from the front on a busy road or even spotting our favorite color out of the many colors. In Medical Imaging, where medical experts have to take major clinical decisions based on the examination of images of various kinds (CT, MRI etc), visual attention plays a pivotal role. It makes the medical experts fixate on any abnormal behavior exhibited in the medical image and helps in speedy diagnosis. Many previous works (see the paper for details) have exhibited this important fact and the model proposed by Nodine and Kundel highlights the important role of visual attention in medical image diagnosis. Visual attention involves two components - Bottom-Up and Top-Down.In the present work, we examine a number of established computational models of visual attention in the context of chest X-rays (infected with Pneumoconiosis) and retinal images (having hard exudates). The fundamental motivation is to try to understand the applicability of visual attention models in the context of different types of abnormalities. Our assessment of four popular visual attention models, is extensive and shows that they are able to pick up abnormal features reasonably well. We compare the models towards detecting subtle abnormalities and high-contrast lesions. Although significant scope of improvements exists especially in picking up more subtle abnormalities and getting more selective towards picking up more abnormalities and less normal structures, the presented assessment shows that visual attention indeed shows a promise for inclusion in the main field of medical image analysis

url pdf poster link (url) [BibTex]

2012

url pdf poster link (url) [BibTex]


Thumb xl paperfig
Lie Bodies: A Manifold Representation of 3D Human Shape

Freifeld, O., Black, M. J.

In European Conf. on Computer Vision (ECCV), pages: 1-14, Part I, LNCS 7572, (Editors: A. Fitzgibbon et al. (Eds.)), Springer-Verlag, October 2012 (inproceedings)

Abstract
Three-dimensional object shape is commonly represented in terms of deformations of a triangular mesh from an exemplar shape. Existing models, however, are based on a Euclidean representation of shape deformations. In contrast, we argue that shape has a manifold structure: For example, summing the shape deformations for two people does not necessarily yield a deformation corresponding to a valid human shape, nor does the Euclidean difference of these two deformations provide a meaningful measure of shape dissimilarity. Consequently, we define a novel manifold for shape representation, with emphasis on body shapes, using a new Lie group of deformations. This has several advantages. First we define triangle deformations exactly, removing non-physical deformations and redundant degrees of freedom common to previous methods. Second, the Riemannian structure of Lie Bodies enables a more meaningful definition of body shape similarity by measuring distance between bodies on the manifold of body shape deformations. Third, the group structure allows the valid composition of deformations. This is important for models that factor body shape deformations into multiple causes or represent shape as a linear combination of basis shapes. Finally, body shape variation is modeled using statistics on manifolds. Instead of modeling Euclidean shape variation with Principal Component Analysis we capture shape variation on the manifold using Principal Geodesic Analysis. Our experiments show consistent visual and quantitative advantages of Lie Bodies over traditional Euclidean models of shape deformation and our representation can be easily incorporated into existing methods.

pdf supplemental material youtube poster eigenshape video code Project Page Project Page Project Page [BibTex]

pdf supplemental material youtube poster eigenshape video code Project Page Project Page Project Page [BibTex]


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Coregistration: Simultaneous alignment and modeling of articulated 3D shape

Hirshberg, D., Loper, M., Rachlin, E., Black, M.

In European Conf. on Computer Vision (ECCV), pages: 242-255, LNCS 7577, Part IV, (Editors: A. Fitzgibbon et al. (Eds.)), Springer-Verlag, October 2012 (inproceedings)

Abstract
Three-dimensional (3D) shape models are powerful because they enable the inference of object shape from incomplete, noisy, or ambiguous 2D or 3D data. For example, realistic parameterized 3D human body models have been used to infer the shape and pose of people from images. To train such models, a corpus of 3D body scans is typically brought into registration by aligning a common 3D human-shaped template to each scan. This is an ill-posed problem that typically involves solving an optimization problem with regularization terms that penalize implausible deformations of the template. When aligning a corpus, however, we can do better than generic regularization. If we have a model of how the template can deform then alignments can be regularized by this model. Constructing a model of deformations, however, requires having a corpus that is already registered. We address this chicken-and-egg problem by approaching modeling and registration together. By minimizing a single objective function, we reliably obtain high quality registration of noisy, incomplete, laser scans, while simultaneously learning a highly realistic articulated body model. The model greatly improves robustness to noise and missing data. Since the model explains a corpus of body scans, it captures how body shape varies across people and poses.

pdf publisher site poster supplemental material (400MB) Project Page Project Page [BibTex]

pdf publisher site poster supplemental material (400MB) Project Page Project Page [BibTex]


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Lessons and insights from creating a synthetic optical flow benchmark

Wulff, J., Butler, D. J., Stanley, G. B., Black, M. J.

In ECCV Workshop on Unsolved Problems in Optical Flow and Stereo Estimation, pages: 168-177, Part II, LNCS 7584, (Editors: A. Fusiello et al. (Eds.)), Springer-Verlag, October 2012 (inproceedings)

pdf dataset poster youtube Project Page [BibTex]

pdf dataset poster youtube Project Page [BibTex]


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3D2PM – 3D Deformable Part Models

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

In Proceedings of the European Conference on Computer Vision (ECCV), pages: 356-370, Lecture Notes in Computer Science, (Editors: Fitzgibbon, Andrew W. and Lazebnik, Svetlana and Perona, Pietro and Sato, Yoichi and Schmid, Cordelia), Springer, Firenze, October 2012 (inproceedings)

pdf video poster Project Page [BibTex]

pdf video poster Project Page [BibTex]


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A naturalistic open source movie for optical flow evaluation

Butler, D. J., Wulff, J., Stanley, G. B., Black, M. J.

In European Conf. on Computer Vision (ECCV), pages: 611-625, Part IV, LNCS 7577, (Editors: A. Fitzgibbon et al. (Eds.)), Springer-Verlag, October 2012 (inproceedings)

Abstract
Ground truth optical flow is difficult to measure in real scenes with natural motion. As a result, optical flow data sets are restricted in terms of size, complexity, and diversity, making optical flow algorithms difficult to train and test on realistic data. We introduce a new optical flow data set derived from the open source 3D animated short film Sintel. This data set has important features not present in the popular Middlebury flow evaluation: long sequences, large motions, specular reflections, motion blur, defocus blur, and atmospheric effects. Because the graphics data that generated the movie is open source, we are able to render scenes under conditions of varying complexity to evaluate where existing flow algorithms fail. We evaluate several recent optical flow algorithms and find that current highly-ranked methods on the Middlebury evaluation have difficulty with this more complex data set suggesting further research on optical flow estimation is needed. To validate the use of synthetic data, we compare the image- and flow-statistics of Sintel to those of real films and videos and show that they are similar. The data set, metrics, and evaluation website are publicly available.

pdf dataset youtube talk supplemental material Project Page Project Page [BibTex]

pdf dataset youtube talk supplemental material Project Page Project Page [BibTex]


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Characterization of 3-D Volumetric Probabilistic Scenes for Object Recognition

Restrepo, M. I., Mayer, B. A., Ulusoy, A. O., Mundy, J. L.

In Selected Topics in Signal Processing, IEEE Journal of, 6(5):522-537, September 2012 (inproceedings)

Abstract
This paper presents a new volumetric representation for categorizing objects in large-scale 3-D scenes reconstructed from image sequences. This work uses a probabilistic volumetric model (PVM) that combines the ideas of background modeling and volumetric multi-view reconstruction to handle the uncertainty inherent in the problem of reconstructing 3-D structures from 2-D images. The advantages of probabilistic modeling have been demonstrated by recent application of the PVM representation to video image registration, change detection and classification of changes based on PVM context. The applications just mentioned, operate on 2-D projections of the PVM. This paper presents the first work to characterize and use the local 3-D information in the scenes. Two approaches to local feature description are proposed and compared: 1) features derived from a PCA analysis of model neighborhoods; and 2) features derived from the coefficients of a 3-D Taylor series expansion within each neighborhood. The resulting description is used in a bag-of-features approach to classify buildings, houses, cars, planes, and parking lots learned from aerial imagery collected over Providence, RI. It is shown that both feature descriptions explain the data with similar accuracy and their effectiveness for dense-feature categorization is compared for the different classes. Finally, 3-D extensions of the Harris corner detector and a Hessian-based detector are used to detect salient features. Both types of salient features are evaluated through object categorization experiments, where only features with maximal response are retained. For most saliency criteria tested, features based on the determinant of the Hessian achieved higher classification accuracy than Harris-based features.

pdf DOI [BibTex]

pdf DOI [BibTex]


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A framework for relating neural activity to freely moving behavior

Foster, J. D., Nuyujukian, P., Freifeld, O., Ryu, S., Black, M. J., Shenoy, K. V.

In 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’12), pages: 2736 -2739 , IEEE, San Diego, August 2012 (inproceedings)

pdf Project Page [BibTex]

pdf Project Page [BibTex]


Thumb xl screen shot 2012 06 25 at 1.59.41 pm
Pottics – The Potts Topic Model for Semantic Image Segmentation

Dann, C., Gehler, P., Roth, S., Nowozin, S.

In Proceedings of 34th DAGM Symposium, pages: 397-407, Lecture Notes in Computer Science, (Editors: Pinz, Axel and Pock, Thomas and Bischof, Horst and Leberl, Franz), Springer, August 2012 (inproceedings)

code pdf poster [BibTex]

code pdf poster [BibTex]


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Psoriasis segmentation through chromatic regions and Geometric Active Contours

Bogo, F., Samory, M., Belloni Fortina, A., Piaserico, S., Peserico, E.

In 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’12), pages: 5388-5391, San Diego, August 2012 (inproceedings)

pdf [BibTex]

pdf [BibTex]


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PCA-enhanced stochastic optimization methods

Kuznetsova, A., Pons-Moll, G., Rosenhahn, B.

In German Conference on Pattern Recognition (GCPR), August 2012 (inproceedings)

pdf [BibTex]

pdf [BibTex]


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Quasi-Newton Methods: A New Direction

Hennig, P., Kiefel, M.

In Proceedings of the 29th International Conference on Machine Learning, pages: 25-32, ICML ’12, (Editors: John Langford and Joelle Pineau), Omnipress, New York, NY, USA, July 2012 (inproceedings)

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

website+code pdf link (url) [BibTex]

website+code pdf link (url) [BibTex]


Thumb xl screen shot 2012 06 25 at 2.04.30 pm
Learning Search Based Inference for Object Detection

Gehler, P., Lehmann, A.

In International Conference on Machine Learning (ICML) workshop on Inferning: Interactions between Inference and Learning, Edinburgh, Scotland, UK, July 2012, short version of BMVC11 paper (http://ps.is.tue.mpg.de/publications/31/get_file) (inproceedings)

pdf [BibTex]

pdf [BibTex]


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Distribution Fields for Tracking

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

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA, June 2012 (inproceedings)

Abstract
Visual tracking of general objects often relies on the assumption that gradient descent of the alignment function will reach the global optimum. A common technique to smooth the objective function is to blur the image. However, blurring the image destroys image information, which can cause the target to be lost. To address this problem we introduce a method for building an image descriptor using distribution fields (DFs), a representation that allows smoothing the objective function without destroying information about pixel values. We present experimental evidence on the superiority of the width of the basin of attraction around the global optimum of DFs over other descriptors. DFs also allow the representation of uncertainty about the tracked object. This helps in disregarding outliers during tracking (like occlusions or small misalignments) without modeling them explicitly. Finally, this provides a convenient way to aggregate the observations of the object through time and maintain an updated model. We present a simple tracking algorithm that uses DFs and obtains state-of-the-art results on standard benchmarks.

pdf Matlab code [BibTex]

pdf Matlab code [BibTex]


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From pictorial structures to deformable structures

Zuffi, S., Freifeld, O., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 3546-3553, IEEE, June 2012 (inproceedings)

Abstract
Pictorial Structures (PS) define a probabilistic model of 2D articulated objects in images. Typical PS models assume an object can be represented by a set of rigid parts connected with pairwise constraints that define the prior probability of part configurations. These models are widely used to represent non-rigid articulated objects such as humans and animals despite the fact that such objects have parts that deform non-rigidly. Here we define a new Deformable Structures (DS) model that is a natural extension of previous PS models and that captures the non-rigid shape deformation of the parts. Each part in a DS model is represented by a low-dimensional shape deformation space and pairwise potentials between parts capture how the shape varies with pose and the shape of neighboring parts. A key advantage of such a model is that it more accurately models object boundaries. This enables image likelihood models that are more discriminative than previous PS likelihoods. This likelihood is learned using training imagery annotated using a DS “puppet.” We focus on a human DS model learned from 2D projections of a realistic 3D human body model and use it to infer human poses in images using a form of non-parametric belief propagation.

pdf sup mat code poster Project Page Project Page Project Page Project Page [BibTex]

pdf sup mat code poster Project Page Project Page Project Page Project Page [BibTex]


Thumb xl screen shot 2012 03 22 at 17.51.07
Teaching 3D Geometry to Deformable Part Models

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

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 3362 -3369, IEEE, Providence, RI, USA, June 2012, oral presentation (inproceedings)

pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]


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Branch-and-price global optimization for multi-view multi-object tracking

Leal-Taixé, L., Pons-Moll, G., Rosenhahn, B.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2012 (inproceedings)

project page paper poster [BibTex]

project page paper poster [BibTex]


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A physically-based approach to reflection separation

Kong, N., Tai, Y., Shin, S. Y.

In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 9-16, June 2012 (inproceedings)

Abstract
We propose a physically-based approach to separate reflection using multiple polarized images with a background scene captured behind glass. The input consists of three polarized images, each captured from the same view point but with a different polarizer angle separated by 45 degrees. The output is the high-quality separation of the reflection and background layers from each of the input images. A main technical challenge for this problem is that the mixing coefficient for the reflection and background layers depends on the angle of incidence and the orientation of the plane of incidence, which are spatially-varying over the pixels of an image. Exploiting physical properties of polarization for a double-surfaced glass medium, we propose an algorithm which automatically finds the optimal separation of the reflection and background layers. Thorough experiments, we demonstrate that our approach can generate superior results to those of previous methods.

Publisher site [BibTex]

Publisher site [BibTex]


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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)

pdf [BibTex]

pdf [BibTex]