Publications | Perceiving Systems - Max Planck Institute for Intelligent Systems

66 results (View BibTeX file of all listed publications)

2018

Customized Multi-Person Tracker

Ma, L., Tang, S., Black, M. J., Van Gool, L.

In Computer Vision – ACCV 2018, Springer International Publishing, December 2018 (inproceedings)

2018

Ma, L., Tang, S., Black, M. J., Van Gool, L. Customized Multi-Person Tracker In Computer Vision – ACCV 2018, Springer International Publishing, December 2018 (inproceedings)

PDF Project Page [BibTex]

On the Integration of Optical Flow and Action Recognition

Sevilla-Lara, L., Liao, Y., Güney, F., Jampani, V., Geiger, A., Black, M. J.

In German Conference on Pattern Recognition (GCPR), LNCS 11269, pages: 281-297, Springer, Cham, October 2018 (inproceedings)

Abstract

Most of the top performing action recognition methods use optical flow as a "black box" input. Here we take a deeper look at the combination of flow and action recognition, and investigate why optical flow is helpful, what makes a flow method good for action recognition, and how we can make it better. In particular, we investigate the impact of different flow algorithms and input transformations to better understand how these affect a state-of-the-art action recognition method. Furthermore, we fine tune two neural-network flow methods end-to-end on the most widely used action recognition dataset (UCF101). Based on these experiments, we make the following five observations: 1) optical flow is useful for action recognition because it is invariant to appearance, 2) optical flow methods are optimized to minimize end-point-error (EPE), but the EPE of current methods is not well correlated with action recognition performance, 3) for the flow methods tested, accuracy at boundaries and at small displacements is most correlated with action recognition performance, 4) training optical flow to minimize classification error instead of minimizing EPE improves recognition performance, and 5) optical flow learned for the task of action recognition differs from traditional optical flow especially inside the human body and at the boundary of the body. These observations may encourage optical flow researchers to look beyond EPE as a goal and guide action recognition researchers to seek better motion cues, leading to a tighter integration of the optical flow and action recognition communities.

arXiv DOI [BibTex]

Sevilla-Lara, L., Liao, Y., Güney, F., Jampani, V., Geiger, A., Black, M. J. On the Integration of Optical Flow and Action Recognition In German Conference on Pattern Recognition (GCPR), LNCS 11269, pages: 281-297, Springer, Cham, October 2018 (inproceedings)

arXiv DOI [BibTex]

Temporal Interpolation as an Unsupervised Pretraining Task for Optical Flow Estimation

Wulff, J., Black, M. J.

In German Conference on Pattern Recognition (GCPR), LNCS 11269, pages: 567-582, Springer, Cham, October 2018 (inproceedings)

Abstract

The difficulty of annotating training data is a major obstacle to using CNNs for low-level tasks in video. Synthetic data often does not generalize to real videos, while unsupervised methods require heuristic n losses. Proxy tasks can overcome these issues, and start by training a network for a task for which annotation is easier or which can be trained unsupervised. The trained network is then fine-tuned for the original task using small amounts of ground truth data. Here, we investigate frame interpolation as a proxy task for optical flow. Using real movies, we train a CNN unsupervised for temporal interpolation. Such a network implicitly estimates motion, but cannot handle untextured regions. By fine-tuning on small amounts of ground truth flow, the network can learn to fill in homogeneous regions and compute full optical flow fields. Using this unsupervised pre-training, our network outperforms similar architectures that were trained supervised using synthetic optical flow.

pdf arXiv DOI Project Page [BibTex]

Wulff, J., Black, M. J. Temporal Interpolation as an Unsupervised Pretraining Task for Optical Flow Estimation In German Conference on Pattern Recognition (GCPR), LNCS 11269, pages: 567-582, Springer, Cham, October 2018 (inproceedings)

pdf arXiv DOI Project Page [BibTex]

Human Motion Parsing by Hierarchical Dynamic Clustering

Zhang, Y., Tang, S., Sun, H., Neumann, H.

In Proceedings of the British Machine Vision Conference (BMVC), pages: 269, BMVA Press, September 2018 (inproceedings)

Abstract

Parsing continuous human motion into meaningful segments plays an essential role in various applications. In this work, we propose a hierarchical dynamic clustering framework to derive action clusters from a sequence of local features in an unsuper- vised bottom-up manner. We systematically investigate the modules in this framework and particularly propose diverse temporal pooling schemes, in order to realize accurate temporal action localization. We demonstrate our method on two motion parsing tasks: temporal action segmentation and abnormal behavior detection. The experimental results indicate that the proposed framework is significantly more effective than the other related state-of-the-art methods on several datasets.

pdf Project Page [BibTex]

Zhang, Y., Tang, S., Sun, H., Neumann, H. Human Motion Parsing by Hierarchical Dynamic Clustering In Proceedings of the British Machine Vision Conference (BMVC), pages: 269, BMVA Press, September 2018 (inproceedings)

pdf Project Page [BibTex]

Generating 3D Faces using Convolutional Mesh Autoencoders

Ranjan, A., Bolkart, T., Sanyal, S., Black, M. J.

In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 11207, pages: 725-741, Springer, Cham, September 2018 (inproceedings)

Abstract

Learned 3D representations of human faces are useful for computer vision problems such as 3D face tracking and reconstruction from images, as well as graphics applications such as character generation and animation. Traditional models learn a latent representation of a face using linear subspaces or higher-order tensor generalizations. Due to this linearity, they can not capture extreme deformations and non-linear expressions. To address this, we introduce a versatile model that learns a non-linear representation of a face using spectral convolutions on a mesh surface. We introduce mesh sampling operations that enable a hierarchical mesh representation that captures non-linear variations in shape and expression at multiple scales within the model. In a variational setting, our model samples diverse realistic 3D faces from a multivariate Gaussian distribution. Our training data consists of 20,466 meshes of extreme expressions captured over 12 different subjects. Despite limited training data, our trained model outperforms state-of-the-art face models with 50% lower reconstruction error, while using 75% fewer parameters. We also show that, replacing the expression space of an existing state-of-the-art face model with our autoencoder, achieves a lower reconstruction error. Our data, model and code are available at http://coma.is.tue.mpg.de/.

Code (tensorflow) Code (pytorch) Project Page paper supplementary DOI Project Page Project Page [BibTex]

Ranjan, A., Bolkart, T., Sanyal, S., Black, M. J. Generating 3D Faces using Convolutional Mesh Autoencoders In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 11207, pages: 725-741, Springer, Cham, September 2018 (inproceedings)

Code (tensorflow) Code (pytorch) Project Page paper supplementary DOI Project Page Project Page [BibTex]

Part-Aligned Bilinear Representations for Person Re-identification

Suh, Y., Wang, J., Tang, S., Mei, T., Lee, K. M.

In European Conference on Computer Vision (ECCV), 11218, pages: 418-437, Springer, Cham, September 2018 (inproceedings)

Abstract

Comparing the appearance of corresponding body parts is essential for person re-identification. However, body parts are frequently misaligned be- tween detected boxes, due to the detection errors and the pose/viewpoint changes. In this paper, we propose a network that learns a part-aligned representation for person re-identification. Our model consists of a two-stream network, which gen- erates appearance and body part feature maps respectively, and a bilinear-pooling layer that fuses two feature maps to an image descriptor. We show that it results in a compact descriptor, where the inner product between two image descriptors is equivalent to an aggregation of the local appearance similarities of the cor- responding body parts, and thereby significantly reduces the part misalignment problem. Our approach is advantageous over other pose-guided representations by learning part descriptors optimal for person re-identification. Training the net- work does not require any part annotation on the person re-identification dataset. Instead, we simply initialize the part sub-stream using a pre-trained sub-network of an existing pose estimation network and train the whole network to minimize the re-identification loss. We validate the effectiveness of our approach by demon- strating its superiority over the state-of-the-art methods on the standard bench- mark datasets including Market-1501, CUHK03, CUHK01 and DukeMTMC, and standard video dataset MARS.

pdf supplementary DOI Project Page [BibTex]

Suh, Y., Wang, J., Tang, S., Mei, T., Lee, K. M. Part-Aligned Bilinear Representations for Person Re-identification In European Conference on Computer Vision (ECCV), 11218, pages: 418-437, Springer, Cham, September 2018 (inproceedings)

pdf supplementary DOI Project Page [BibTex]

Learning Human Optical Flow

Ranjan, A., Romero, J., Black, M. J.

In 29th British Machine Vision Conference, September 2018 (inproceedings)

Abstract

The optical flow of humans is well known to be useful for the analysis of human action. Given this, we devise an optical flow algorithm specifically for human motion and show that it is superior to generic flow methods. Designing a method by hand is impractical, so we develop a new training database of image sequences with ground truth optical flow. For this we use a 3D model of the human body and motion capture data to synthesize realistic flow fields. We then train a convolutional neural network to estimate human flow fields from pairs of images. Since many applications in human motion analysis depend on speed, and we anticipate mobile applications, we base our method on SpyNet with several modifications. We demonstrate that our trained network is more accurate than a wide range of top methods on held-out test data and that it generalizes well to real image sequences. When combined with a person detector/tracker, the approach provides a full solution to the problem of 2D human flow estimation. Both the code and the dataset are available for research.

video code pdf link (url) Project Page Project Page [BibTex]

Ranjan, A., Romero, J., Black, M. J. Learning Human Optical Flow In 29th British Machine Vision Conference, September 2018 (inproceedings)

video code pdf link (url) Project Page Project Page [BibTex]

Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation

(Best Student Paper Award)

Omran, M., Lassner, C., Pons-Moll, G., Gehler, P. V., Schiele, B.

In 3DV, September 2018 (inproceedings)

Abstract

Direct prediction of 3D body pose and shape remains a challenge even for highly parameterized deep learning models. Mapping from the 2D image space to the prediction space is difficult: perspective ambiguities make the loss function noisy and training data is scarce. In this paper, we propose a novel approach (Neural Body Fitting (NBF)). It integrates a statistical body model within a CNN, leveraging reliable bottom-up semantic body part segmentation and robust top-down body model constraints. NBF is fully differentiable and can be trained using 2D and 3D annotations. In detailed experiments, we analyze how the components of our model affect performance, especially the use of part segmentations as an explicit intermediate representation, and present a robust, efficiently trainable framework for 3D human pose estimation from 2D images with competitive results on standard benchmarks. Code is available at https://github.com/mohomran/neural_body_fitting

arXiv code Project Page [BibTex]

Omran, M., Lassner, C., Pons-Moll, G., Gehler, P. V., Schiele, B. Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation In 3DV, September 2018 (inproceedings)

arXiv code Project Page [BibTex]

Unsupervised Learning of Multi-Frame Optical Flow with Occlusions

Janai, J., Güney, F., Ranjan, A., Black, M. J., Geiger, A.

In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 11220, pages: 713-731, Springer, Cham, September 2018 (inproceedings)

pdf suppmat Video Project Page DOI Project Page [BibTex]

Janai, J., Güney, F., Ranjan, A., Black, M. J., Geiger, A. Unsupervised Learning of Multi-Frame Optical Flow with Occlusions In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 11220, pages: 713-731, Springer, Cham, September 2018 (inproceedings)

pdf suppmat Video Project Page DOI Project Page [BibTex]

Learning an Infant Body Model from RGB-D Data for Accurate Full Body Motion Analysis

Hesse, N., Pujades, S., Romero, J., Black, M. J., Bodensteiner, C., Arens, M., Hofmann, U. G., Tacke, U., Hadders-Algra, M., Weinberger, R., Muller-Felber, W., Schroeder, A. S.

In Int. Conf. on Medical Image Computing and Computer Assisted Intervention (MICCAI), September 2018 (inproceedings)

Abstract

Infant motion analysis enables early detection of neurodevelopmental disorders like cerebral palsy (CP). Diagnosis, however, is challenging, requiring expert human judgement. An automated solution would be beneficial but requires the accurate capture of 3D full-body movements. To that end, we develop a non-intrusive, low-cost, lightweight acquisition system that captures the shape and motion of infants. Going beyond work on modeling adult body shape, we learn a 3D Skinned Multi-Infant Linear body model (SMIL) from noisy, low-quality, and incomplete RGB-D data. We demonstrate the capture of shape and motion with 37 infants in a clinical environment. Quantitative experiments show that SMIL faithfully represents the data and properly factorizes the shape and pose of the infants. With a case study based on general movement assessment (GMA), we demonstrate that SMIL captures enough information to allow medical assessment. SMIL provides a new tool and a step towards a fully automatic system for GMA.

pdf Project page video extended arXiv version DOI Project Page [BibTex]

Hesse, N., Pujades, S., Romero, J., Black, M. J., Bodensteiner, C., Arens, M., Hofmann, U. G., Tacke, U., Hadders-Algra, M., Weinberger, R., Muller-Felber, W., Schroeder, A. S. Learning an Infant Body Model from RGB-D Data for Accurate Full Body Motion Analysis In Int. Conf. on Medical Image Computing and Computer Assisted Intervention (MICCAI), September 2018 (inproceedings)

pdf Project page video extended arXiv version DOI Project Page [BibTex]

Deep Directional Statistics: Pose Estimation with Uncertainty Quantification

Prokudin, S., Gehler, P., Nowozin, S.

European Conference on Computer Vision (ECCV), September 2018 (conference)

Abstract

Modern deep learning systems successfully solve many perception tasks such as object pose estimation when the input image is of high quality. However, in challenging imaging conditions such as on low resolution images or when the image is corrupted by imaging artifacts, current systems degrade considerably in accuracy. While a loss in performance is unavoidable we would like our models to quantify their uncertainty in order to achieve robustness against images of varying quality. Probabilistic deep learning models combine the expressive power of deep learning with uncertainty quantification. In this paper, we propose a novel probabilistic deep learning model for the task of angular regression. Our model uses von Mises distributions to predict a distribution over object pose angle. Whereas a single von Mises distribution is making strong assumptions about the shape of the distribution, we extend the basic model to predict a mixture of von Mises distributions. We show how to learn a mixture model using a finite and infinite number of mixture components. Our model allow for likelihood-based training and efficient inference at test time. We demonstrate on a number of challenging pose estimation datasets that our model produces calibrated probability predictions and competitive or superior point estimates compared to the current state-of-the-art.

code pdf [BibTex]

Prokudin, S., Gehler, P., Nowozin, S. Deep Directional Statistics: Pose Estimation with Uncertainty Quantification European Conference on Computer Vision (ECCV), September 2018 (conference)

code pdf [BibTex]

Recovering Accurate 3D Human Pose in The Wild Using IMUs and a Moving Camera

Marcard, T. V., Henschel, R., Black, M. J., Rosenhahn, B., Pons-Moll, G.

In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 11214, pages: 614-631, Springer, Cham, September 2018 (inproceedings)

Abstract

In this work, we propose a method that combines a single hand-held camera and a set of Inertial Measurement Units (IMUs) attached at the body limbs to estimate accurate 3D poses in the wild. This poses many new challenges: the moving camera, heading drift, cluttered background, occlusions and many people visible in the video. We associate 2D pose detections in each image to the corresponding IMU-equipped persons by solving a novel graph based optimization problem that forces 3D to 2D coherency within a frame and across long range frames. Given associations, we jointly optimize the pose of a statistical body model, the camera pose and heading drift using a continuous optimization framework. We validated our method on the TotalCapture dataset, which provides video and IMU synchronized with ground truth. We obtain an accuracy of 26mm, which makes it accurate enough to serve as a benchmark for image-based 3D pose estimation in the wild. Using our method, we recorded 3D Poses in the Wild (3DPW ), a new dataset consisting of more than 51; 000 frames with accurate 3D pose in challenging sequences, including walking in the city, going up-stairs, having coffee or taking the bus. We make the reconstructed 3D poses, video, IMU and 3D models available for research purposes at http://virtualhumans.mpi-inf.mpg.de/3DPW.

pdf SupMat data project DOI Project Page [BibTex]

Marcard, T. V., Henschel, R., Black, M. J., Rosenhahn, B., Pons-Moll, G. Recovering Accurate 3D Human Pose in The Wild Using IMUs and a Moving Camera In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 11214, pages: 614-631, Springer, Cham, September 2018 (inproceedings)

pdf SupMat data project DOI Project Page [BibTex]

Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios

Tallamraju, R., Rajappa, S., Black, M. J., Karlapalem, K., Ahmad, A.

2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pages: 1-8, IEEE, August 2018 (conference)

Abstract

In this work, we consider the problem of decentralized multi-robot target tracking and obstacle avoidance in dynamic environments. Each robot executes a local motion planning algorithm which is based on model predictive control (MPC). The planner is designed as a quadratic program, subject to constraints on robot dynamics and obstacle avoidance. Repulsive potential field functions are employed to avoid obstacles. The novelty of our approach lies in embedding these non-linear potential field functions as constraints within a convex optimization framework. Our method convexifies nonconvex constraints and dependencies, by replacing them as pre-computed external input forces in robot dynamics. The proposed algorithm additionally incorporates different methods to avoid field local minima problems associated with using potential field functions in planning. The motion planner does not enforce predefined trajectories or any formation geometry on the robots and is a comprehensive solution for cooperative obstacle avoidance in the context of multi-robot target tracking. We perform simulation studies for different scenarios to showcase the convergence and efficacy of the proposed algorithm.

Published Version link (url) DOI [BibTex]

Tallamraju, R., Rajappa, S., Black, M. J., Karlapalem, K., Ahmad, A. Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pages: 1-8, IEEE, August 2018 (conference)

Published Version link (url) DOI [BibTex]

End-to-end Recovery of Human Shape and Pose

Kanazawa, A., Black, M. J., Jacobs, D. W., Malik, J.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, 2018 (inproceedings)

Abstract

We describe Human Mesh Recovery (HMR), an end-to-end framework for reconstructing a full 3D mesh of a human body from a single RGB image. In contrast to most current methods that compute 2D or 3D joint locations, we produce a richer and more useful mesh representation that is parameterized by shape and 3D joint angles. The main objective is to minimize the reprojection loss of keypoints, which allows our model to be trained using in-the-wild images that only have ground truth 2D annotations. However, the reprojection loss alone is highly underconstrained. In this work we address this problem by introducing an adversary trained to tell whether human body shape and pose parameters are real or not using a large database of 3D human meshes. We show that HMR can be trained with and without using any paired 2D-to-3D supervision. We do not rely on intermediate 2D keypoint detections and infer 3D pose and shape parameters directly from image pixels. Our model runs in real-time given a bounding box containing the person. We demonstrate our approach on various images in-the-wild and out-perform previous optimization-based methods that output 3D meshes and show competitive results on tasks such as 3D joint location estimation and part segmentation.

pdf code project video Project Page [BibTex]

Kanazawa, A., Black, M. J., Jacobs, D. W., Malik, J. End-to-end Recovery of Human Shape and Pose In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, 2018 (inproceedings)

pdf code project video Project Page [BibTex]

Lions and Tigers and Bears: Capturing Non-Rigid, 3D, Articulated Shape from Images

Zuffi, S., Kanazawa, A., Black, M. J.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, 2018 (inproceedings)

Abstract

Animals are widespread in nature and the analysis of their shape and motion is important in many fields and industries. Modeling 3D animal shape, however, is difficult because the 3D scanning methods used to capture human shape are not applicable to wild animals or natural settings. Consequently, we propose a method to capture the detailed 3D shape of animals from images alone. The articulated and deformable nature of animals makes this problem extremely challenging, particularly in unconstrained environments with moving and uncalibrated cameras. To make this possible, we use a strong prior model of articulated animal shape that we fit to the image data. We then deform the animal shape in a canonical reference pose such that it matches image evidence when articulated and projected into multiple images. Our method extracts significantly more 3D shape detail than previous methods and is able to model new species, including the shape of an extinct animal, using only a few video frames. Additionally, the projected 3D shapes are accurate enough to facilitate the extraction of a realistic texture map from multiple frames.

pdf code/data 3D models Project Page [BibTex]

Zuffi, S., Kanazawa, A., Black, M. J. Lions and Tigers and Bears: Capturing Non-Rigid, 3D, Articulated Shape from Images In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, 2018 (inproceedings)

pdf code/data 3D models Project Page [BibTex]

PoTion: Pose MoTion Representation for Action Recognition

Choutas, V., Weinzaepfel, P., Revaud, J., Schmid, C.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, 2018 (inproceedings)

Abstract

Most state-of-the-art methods for action recognition rely on a two-stream architecture that processes appearance and motion independently. In this paper, we claim that consider- ing them jointly offers rich information for action recogni- tion. We introduce a novel representation that gracefully en- codes the movement of some semantic keypoints. We use the human joints as these keypoints and term our Pose moTion representation PoTion. Specifically, we first run a state- of-the-art human pose estimator [4] and extract heatmaps for the human joints in each frame. We obtain our PoTion representation by temporally aggregating these probability maps. This is achieved by ‘colorizing’ each of them de- pending on the relative time of the frames in the video clip and summing them. This fixed-size representation for an en- tire video clip is suitable to classify actions using a shallow convolutional neural network. Our experimental evaluation shows that PoTion outper- forms other state-of-the-art pose representations [6, 48]. Furthermore, it is complementary to standard appearance and motion streams. When combining PoTion with the recent two-stream I3D approach [5], we obtain state-of- the-art performance on the JHMDB, HMDB and UCF101 datasets.

PDF [BibTex]

Choutas, V., Weinzaepfel, P., Revaud, J., Schmid, C. PoTion: Pose MoTion Representation for Action Recognition In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, 2018 (inproceedings)

PDF [BibTex]

2012

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

Jampani, V., Ujjwal, , Sivaswamy, J., Vaidya, V. Assessment of Computational Visual Attention Models on Medical Images Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing, pages: 80:1-80:8, ACM, Mumbai, India, December 2012 (conference)

url pdf poster link (url) [BibTex]

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]

Freifeld, O., Black, M. J. Lie Bodies: A Manifold Representation of 3D Human Shape 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)

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

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]

Hirshberg, D., Loper, M., Rachlin, E., Black, M. Coregistration: Simultaneous alignment and modeling of articulated 3D shape 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)

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

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]

Wulff, J., Butler, D. J., Stanley, G. B., Black, M. J. Lessons and insights from creating a synthetic optical flow benchmark 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]

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]

Pepik, B., Gehler, P., Stark, M., Schiele, B. 3D2PM – 3D Deformable Part Models 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]

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]

Butler, D. J., Wulff, J., Stanley, G. B., Black, M. J. A naturalistic open source movie for optical flow evaluation 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)

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

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]

Restrepo, M. I., Mayer, B. A., Ulusoy, A. O., Mundy, J. L. Characterization of 3-D Volumetric Probabilistic Scenes for Object Recognition In Selected Topics in Signal Processing, IEEE Journal of, 6(5):522-537, September 2012 (inproceedings)

pdf DOI [BibTex]

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]

Foster, J. D., Nuyujukian, P., Freifeld, O., Ryu, S., Black, M. J., Shenoy, K. V. A framework for relating neural activity to freely moving behavior 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]

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]

Dann, C., Gehler, P., Roth, S., Nowozin, S. Pottics – The Potts Topic Model for Semantic Image Segmentation 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]

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]

Bogo, F., Samory, M., Belloni Fortina, A., Piaserico, S., Peserico, E. Psoriasis segmentation through chromatic regions and Geometric Active Contours 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]

PCA-enhanced stochastic optimization methods

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

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

pdf [BibTex]

Kuznetsova, A., Pons-Moll, G., Rosenhahn, B. PCA-enhanced stochastic optimization methods In German Conference on Pattern Recognition (GCPR), August 2012 (inproceedings)

pdf [BibTex]

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]

Hennig, P., Kiefel, M. Quasi-Newton Methods: A New Direction 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)

website+code pdf link (url) [BibTex]

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]

Gehler, P., Lehmann, A. Learning Search Based Inference for Object Detection 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]

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]

Sevilla-Lara, L., Learned-Miller, E. Distribution Fields for Tracking In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA, June 2012 (inproceedings)

pdf Matlab code [BibTex]

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]

Zuffi, S., Freifeld, O., Black, M. J. From pictorial structures to deformable structures In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 3546-3553, IEEE, June 2012 (inproceedings)

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

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]

Pepik, B., Stark, M., Gehler, P., Schiele, B. Teaching 3D Geometry to Deformable Part Models 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]

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]

Leal-Taixé, L., Pons-Moll, G., Rosenhahn, B. Branch-and-price global optimization for multi-view multi-object tracking In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2012 (inproceedings)

project page paper poster [BibTex]

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]

Kong, N., Tai, Y., Shin, S. Y. A physically-based approach to reflection separation In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 9-16, June 2012 (inproceedings)

Publisher site [BibTex]

High Resolution Surface Reconstruction from Multi-view Aerial Imagery

Calakli, F., Ulusoy, A. O., Restrepo, M. I., Taubin, G., Mundy, J. L.

In 3D Imaging Modeling Processing Visualization Transmission (3DIMPVT), pages: 25-32, IEEE, 2012 (inproceedings)

Abstract

This paper presents a novel framework for surface reconstruction from multi-view aerial imagery of large scale urban scenes, which combines probabilistic volumetric modeling with smooth signed distance surface estimation, to produce very detailed and accurate surfaces. Using a continuous probabilistic volumetric model which allows for explicit representation of ambiguities caused by moving objects, reflective surfaces, areas of constant appearance, and self-occlusions, the algorithm learns the geometry and appearance of a scene from a calibrated image sequence. An online implementation of Bayesian learning precess in GPUs significantly reduces the time required to process a large number of images. The probabilistic volumetric model of occupancy is subsequently used to estimate a smooth approximation of the signed distance function to the surface. This step, which reduces to the solution of a sparse linear system, is very efficient and scalable to large data sets. The proposed algorithm is shown to produce high quality surfaces in challenging aerial scenes where previous methods make large errors in surface localization. The general applicability of the algorithm beyond aerial imagery is confirmed against the Middlebury benchmark.

Video pdf link (url) DOI [BibTex]

Calakli, F., Ulusoy, A. O., Restrepo, M. I., Taubin, G., Mundy, J. L. High Resolution Surface Reconstruction from Multi-view Aerial Imagery In 3D Imaging Modeling Processing Visualization Transmission (3DIMPVT), pages: 25-32, IEEE, 2012 (inproceedings)

Video pdf link (url) DOI [BibTex]

Detection and Tracking of Occluded People

(Best Paper Award)

Tang, S., Andriluka, M., Schiele, B.

In British Machine Vision Conference (BMVC), 2012, BMVC Best Paper Award (inproceedings)

PDF [BibTex]

Tang, S., Andriluka, M., Schiele, B. Detection and Tracking of Occluded People In British Machine Vision Conference (BMVC), 2012, BMVC Best Paper Award (inproceedings)

PDF [BibTex]

3D Cardiac Segmentation with Pose-Invariant Higher-Order MRFs

Xiang, B., Wang, C., Deux, J., Rahmouni, A., Paragios, N.

In IEEE International Symposium on Biomedical Imaging (ISBI), 2012 (inproceedings)

[BibTex]

Xiang, B., Wang, C., Deux, J., Rahmouni, A., Paragios, N. 3D Cardiac Segmentation with Pose-Invariant Higher-Order MRFs In IEEE International Symposium on Biomedical Imaging (ISBI), 2012 (inproceedings)

[BibTex]

Real-time Facial Feature Detection using Conditional Regression Forests

Dantone, M., Gall, J., Fanelli, G., van Gool, L.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 2578-2585, IEEE, Providence, RI, USA, 2012 (inproceedings)

code pdf Project Page [BibTex]

Dantone, M., Gall, J., Fanelli, G., van Gool, L. Real-time Facial Feature Detection using Conditional Regression Forests In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 2578-2585, IEEE, Providence, RI, USA, 2012 (inproceedings)

code pdf Project Page [BibTex]

Latent Hough Transform for Object Detection

Razavi, N., Gall, J., Kohli, P., van Gool, L.

In European Conference on Computer Vision (ECCV), 7574, pages: 312-325, LNCS, Springer, 2012 (inproceedings)

pdf Project Page [BibTex]

Razavi, N., Gall, J., Kohli, P., van Gool, L. Latent Hough Transform for Object Detection In European Conference on Computer Vision (ECCV), 7574, pages: 312-325, LNCS, Springer, 2012 (inproceedings)

pdf Project Page [BibTex]

Destination Flow for Crowd Simulation

Pellegrini, S., Gall, J., Sigal, L., van Gool, L.

In Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams, 7585, pages: 162-171, LNCS, Springer, 2012 (inproceedings)

pdf Project Page [BibTex]

Pellegrini, S., Gall, J., Sigal, L., van Gool, L. Destination Flow for Crowd Simulation In Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams, 7585, pages: 162-171, LNCS, Springer, 2012 (inproceedings)

pdf Project Page [BibTex]

From Deformations to Parts: Motion-based Segmentation of 3D Objects

Ghosh, S., Sudderth, E., Loper, M., Black, M.

In Advances in Neural Information Processing Systems 25 (NIPS), pages: 2006-2014, (Editors: P. Bartlett and F.C.N. Pereira and C.J.C. Burges and L. Bottou and K.Q. Weinberger), MIT Press, 2012 (inproceedings)

Abstract

We develop a method for discovering the parts of an articulated object from aligned meshes of the object in various three-dimensional poses. We adapt the distance dependent Chinese restaurant process (ddCRP) to allow nonparametric discovery of a potentially unbounded number of parts, while simultaneously guaranteeing a spatially connected segmentation. To allow analysis of datasets in which object instances have varying 3D shapes, we model part variability across poses via affine transformations. By placing a matrix normal-inverse-Wishart prior on these affine transformations, we develop a ddCRP Gibbs sampler which tractably marginalizes over transformation uncertainty. Analyzing a dataset of humans captured in dozens of poses, we infer parts which provide quantitatively better deformation predictions than conventional clustering methods.

pdf supplemental code poster link (url) Project Page [BibTex]

Ghosh, S., Sudderth, E., Loper, M., Black, M. From Deformations to Parts: Motion-based Segmentation of 3D Objects In Advances in Neural Information Processing Systems 25 (NIPS), pages: 2006-2014, (Editors: P. Bartlett and F.C.N. Pereira and C.J.C. Burges and L. Bottou and K.Q. Weinberger), MIT Press, 2012 (inproceedings)

pdf supplemental code poster link (url) Project Page [BibTex]

Segmentation of Vessel Geometries from Medical Images Using GPF Deformable Model

Si Yong Yeo, Xianghua Xie, Igor Sazonov, Perumal Nithiarasu

In International Conference on Pattern Recognition Applications and Methods, 2012 (inproceedings)

Abstract

We present a method for the reconstruction of vascular geometries from medical images. Image denoising is performed using vessel enhancing diffusion, which can smooth out image noise and enhance vessel structures. The Canny edge detection technique which produces object edges with single pixel width is used for accurate detection of the lumen boundaries. The image gradients are then used to compute the geometric potential field which gives a global representation of the geometric configuration. The deformable model uses a regional constraint to suppress calcified regions for accurate segmentation of the vessel geometries. The proposed framework show high accuracy when applied to the segmentation of the carotid arteries from CT images.

[BibTex]

Si Yong Yeo, Xianghua Xie, Igor Sazonov, Perumal Nithiarasu Segmentation of Vessel Geometries from Medical Images Using GPF Deformable Model In International Conference on Pattern Recognition Applications and Methods, 2012 (inproceedings)

[BibTex]

SuperFloxels: A Mid-Level Representation for Video Sequences

Ravichandran, A., Wang, C., Raptis, M., Soatto, S.

In Analysis and Retrieval of Tracked Events and Motion in Imagery Streams Workshop (ARTEMIS) (in conjunction with ECCV 2012), 2012 (inproceedings)

pdf [BibTex]

Ravichandran, A., Wang, C., Raptis, M., Soatto, S. SuperFloxels: A Mid-Level Representation for Video Sequences In Analysis and Retrieval of Tracked Events and Motion in Imagery Streams Workshop (ARTEMIS) (in conjunction with ECCV 2012), 2012 (inproceedings)

pdf [BibTex]

Implicit Active Contours for N-Dimensional Biomedical Image Segmentation

Si Yong Yeo

In IEEE International Conference on Systems, Man, and Cybernetics, pages: 2855 - 2860, 2012 (inproceedings)

Abstract

The segmentation of shapes from biomedical images has a wide range of uses such as image based modelling and bioimage analysis. In this paper, an active contour model is proposed for the segmentation of N-dimensional biomedical images. The proposed model uses a curvature smoothing flow and an image attraction force derived from the interactions between the geometries of the active contour model and the image objects. The active contour model is formulated using the level set method so as to handle topological changes automatically. The magnitude and orientation of the image attraction force is based on the relative geometric configurations between the active contour model and the image object boundaries. The vector force field is therefore dynamic, and the active contour model can propagate through narrow structures to segment complex shapes efficiently. The proposed model utilizes pixel interactions across the image domain, which gives a coherent representation of the image object shapes. This allows the active contour model to be robust to image noise and weak object edges. The proposed model is compared against widely used active contour models in the segmentation of anatomical shapes from biomedical images. It is shown that the proposed model has several advantages over existing techniques and can be used for the segmentation of biomedical images efficiently.

[BibTex]

Si Yong Yeo Implicit Active Contours for N-Dimensional Biomedical Image Segmentation In IEEE International Conference on Systems, Man, and Cybernetics, pages: 2855 - 2860, 2012 (inproceedings)

[BibTex]

Interactive Object Detection

Yao, A., Gall, J., Leistner, C., van Gool, L.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 3242-3249, IEEE, Providence, RI, USA, 2012 (inproceedings)

video pdf Project Page [BibTex]

Yao, A., Gall, J., Leistner, C., van Gool, L. Interactive Object Detection In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 3242-3249, IEEE, Providence, RI, USA, 2012 (inproceedings)

video pdf Project Page [BibTex]

Real Time 3D Head Pose Estimation: Recent Achievements and Future Challenges

Fanelli, G., Gall, J., van Gool, L.

In 5th International Symposium on Communications, Control and Signal Processing (ISCCSP), 2012 (inproceedings)

data and code pdf Project Page [BibTex]

Fanelli, G., Gall, J., van Gool, L. Real Time 3D Head Pose Estimation: Recent Achievements and Future Challenges In 5th International Symposium on Communications, Control and Signal Processing (ISCCSP), 2012 (inproceedings)

data and code pdf Project Page [BibTex]

Motion Capture of Hands in Action using Discriminative Salient Points

Ballan, L., Taneja, A., Gall, J., van Gool, L., Pollefeys, M.

In European Conference on Computer Vision (ECCV), 7577, pages: 640-653, LNCS, Springer, 2012 (inproceedings)

data video pdf supplementary Project Page [BibTex]

Ballan, L., Taneja, A., Gall, J., van Gool, L., Pollefeys, M. Motion Capture of Hands in Action using Discriminative Salient Points In European Conference on Computer Vision (ECCV), 7577, pages: 640-653, LNCS, Springer, 2012 (inproceedings)

data video pdf supplementary Project Page [BibTex]

Sparsity Potentials for Detecting Objects with the Hough Transform

Razavi, N., Alvar, N., Gall, J., van Gool, L.

In British Machine Vision Conference (BMVC), pages: 11.1-11.10, (Editors: Bowden, Richard and Collomosse, John and Mikolajczyk, Krystian), BMVA Press, 2012 (inproceedings)

pdf Project Page [BibTex]

Razavi, N., Alvar, N., Gall, J., van Gool, L. Sparsity Potentials for Detecting Objects with the Hough Transform In British Machine Vision Conference (BMVC), pages: 11.1-11.10, (Editors: Bowden, Richard and Collomosse, John and Mikolajczyk, Krystian), BMVA Press, 2012 (inproceedings)

pdf Project Page [BibTex]

Metric Learning from Poses for Temporal Clustering of Human Motion

L’opez-M’endez, A., Gall, J., Casas, J., van Gool, L.

In British Machine Vision Conference (BMVC), pages: 49.1-49.12, (Editors: Bowden, Richard and Collomosse, John and Mikolajczyk, Krystian), BMVA Press, 2012 (inproceedings)

video pdf Project Page Project Page [BibTex]

L’opez-M’endez, A., Gall, J., Casas, J., van Gool, L. Metric Learning from Poses for Temporal Clustering of Human Motion In British Machine Vision Conference (BMVC), pages: 49.1-49.12, (Editors: Bowden, Richard and Collomosse, John and Mikolajczyk, Krystian), BMVA Press, 2012 (inproceedings)

video pdf Project Page Project Page [BibTex]

Local Context Priors for Object Proposal Generation

Ristin, M., Gall, J., van Gool, L.

In Asian Conference on Computer Vision (ACCV), 7724, pages: 57-70, LNCS, Springer-Verlag, 2012 (inproceedings)

pdf DOI Project Page [BibTex]

Ristin, M., Gall, J., van Gool, L. Local Context Priors for Object Proposal Generation In Asian Conference on Computer Vision (ACCV), 7724, pages: 57-70, LNCS, Springer-Verlag, 2012 (inproceedings)

pdf DOI Project Page [BibTex]

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