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2012


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Visual Orientation and Directional Selectivity Through Thalamic Synchrony

Stanley, G., Jin, J., Wang, Y., Desbordes, G., Wang, Q., Black, M., Alonso, J.

Journal of Neuroscience, 32(26):9073-9088, June 2012 (article)

Abstract
Thalamic neurons respond to visual scenes by generating synchronous spike trains on the timescale of 10–20 ms that are very effective at driving cortical targets. Here we demonstrate that this synchronous activity contains unexpectedly rich information about fundamental properties of visual stimuli. We report that the occurrence of synchronous firing of cat thalamic cells with highly overlapping receptive fields is strongly sensitive to the orientation and the direction of motion of the visual stimulus. We show that this stimulus selectivity is robust, remaining relatively unchanged under different contrasts and temporal frequencies (stimulus velocities). A computational analysis based on an integrate-and-fire model of the direct thalamic input to a layer 4 cortical cell reveals a strong correlation between the degree of thalamic synchrony and the nonlinear relationship between cortical membrane potential and the resultant firing rate. Together, these findings suggest a novel population code in the synchronous firing of neurons in the early visual pathway that could serve as the substrate for establishing cortical representations of the visual scene.

preprint publisher's site Project Page [BibTex]

2012

preprint publisher's site Project Page [BibTex]


Thumb xl screenshot area 2015 07 27 011047
An Analysis of Successful Approaches to Human Pose Estimation

Lassner, C.

An Analysis of Successful Approaches to Human Pose Estimation, University of Augsburg, University of Augsburg, May 2012 (mastersthesis)

Abstract
The field of Human Pose Estimation is developing fast and lately leaped forward with the release of the Kinect system. That system reaches a very good perfor- mance for pose estimation using 3D scene information, however pose estimation from 2D color images is not solved reliably yet. There is a vast amount of pub- lications trying to reach this aim, but no compilation of important methods and solution strategies. The aim of this thesis is to fill this gap: it gives an introductory overview over important techniques by analyzing four current (2012) publications in detail. They are chosen such, that during their analysis many frequently used techniques for Human Pose Estimation can be explained. The thesis includes two introductory chapters with a definition of Human Pose Estimation and exploration of the main difficulties, as well as a detailed explanation of frequently used methods. A final chapter presents some ideas on how parts of the analyzed approaches can be recombined and shows some open questions that can be tackled in future work. The thesis is therefore a good entry point to the field of Human Pose Estimation and enables the reader to get an impression of the current state-of-the-art.

pdf [BibTex]

pdf [BibTex]


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Bilinear Spatiotemporal Basis Models

Akhter, I., Simon, T., Khan, S., Matthews, I., Sheikh, Y.

ACM Transactions on Graphics (TOG), 31(2):17, ACM, April 2012 (article)

Abstract
A variety of dynamic objects, such as faces, bodies, and cloth, are represented in computer graphics as a collection of moving spatial landmarks. Spatiotemporal data is inherent in a number of graphics applications including animation, simulation, and object and camera tracking. The principal modes of variation in the spatial geometry of objects are typically modeled using dimensionality reduction techniques, while concurrently, trajectory representations like splines and autoregressive models are widely used to exploit the temporal regularity of deformation. In this article, we present the bilinear spatiotemporal basis as a model that simultaneously exploits spatial and temporal regularity while maintaining the ability to generalize well to new sequences. This factorization allows the use of analytical, predefined functions to represent temporal variation (e.g., B-Splines or the Discrete Cosine Transform) resulting in efficient model representation and estimation. The model can be interpreted as representing the data as a linear combination of spatiotemporal sequences consisting of shape modes oscillating over time at key frequencies. We apply the bilinear model to natural spatiotemporal phenomena, including face, body, and cloth motion data, and compare it in terms of compaction, generalization ability, predictive precision, and efficiency to existing models. We demonstrate the application of the model to a number of graphics tasks including labeling, gap-filling, denoising, and motion touch-up.

pdf project page link (url) [BibTex]

pdf project page link (url) [BibTex]


Thumb xl 3dimpvt2012
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]

Video pdf link (url) DOI [BibTex]


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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]

PDF [BibTex]


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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]

[BibTex]


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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]

code pdf Project Page [BibTex]


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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]

pdf Project Page [BibTex]


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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]

pdf Project Page [BibTex]


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A metric for comparing the anthropomorphic motion capability of artificial hands

Feix, T., Romero, J., Ek, C. H., Schmiedmayer, H., Kragic, D.

IEEE RAS Transactions on Robotics, TRO, pages: 974-980, 2012 (article)

Publisher site Human Grasping Database Project [BibTex]

Publisher site Human Grasping Database Project [BibTex]


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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]

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


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The Ankyrin 3 (ANK3) Bipolar Disorder Gene Regulates Psychiatric-related Behaviors that are Modulated by Lithium and Stress

Leussis, M., Berry-Scott, E., Saito, M., Jhuang, H., Haan, G., Alkan, O., Luce, C., Madison, J., Sklar, P., Serre, T., Root, D., Petryshen, T.

Biological Psychiatry , 2012 (article)

Prepublication Article Abstract [BibTex]

Prepublication Article Abstract [BibTex]


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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]

[BibTex]


Thumb xl superfloxel
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]

pdf [BibTex]


Thumb xl smcfv1
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]

[BibTex]


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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]

video pdf Project Page [BibTex]


Thumb xl headpose
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]

data and code pdf Project Page [BibTex]


Thumb xl hands
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]

data video pdf supplementary Project Page [BibTex]


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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]

pdf Project Page [BibTex]


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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]

video pdf Project Page Project Page [BibTex]


Thumb xl objectproposal
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]

pdf DOI Project Page [BibTex]


Thumb xl cvprlayers12crop
Layered segmentation and optical flow estimation over time

Sun, D., Sudderth, E., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 1768-1775, IEEE, 2012 (inproceedings)

Abstract
Layered models provide a compelling approach for estimating image motion and segmenting moving scenes. Previous methods, however, have failed to capture the structure of complex scenes, provide precise object boundaries, effectively estimate the number of layers in a scene, or robustly determine the depth order of the layers. Furthermore, previous methods have focused on optical flow between pairs of frames rather than longer sequences. We show that image sequences with more frames are needed to resolve ambiguities in depth ordering at occlusion boundaries; temporal layer constancy makes this feasible. Our generative model of image sequences is rich but difficult to optimize with traditional gradient descent methods. We propose a novel discrete approximation of the continuous objective in terms of a sequence of depth-ordered MRFs and extend graph-cut optimization methods with new “moves” that make joint layer segmentation and motion estimation feasible. Our optimizer, which mixes discrete and continuous optimization, automatically determines the number of layers and reasons about their depth ordering. We demonstrate the value of layered models, our optimization strategy, and the use of more than two frames on both the Middlebury optical flow benchmark and the MIT layer segmentation benchmark.

pdf sup mat poster Project Page Project Page [BibTex]

pdf sup mat poster Project Page Project Page [BibTex]


Thumb xl imavis2012
Natural Metrics and Least-Committed Priors for Articulated Tracking

Soren Hauberg, Stefan Sommer, Kim S. Pedersen

Image and Vision Computing, 30(6-7):453-461, Elsevier, 2012 (article)

Publishers site Code PDF [BibTex]

Publishers site Code PDF [BibTex]


Thumb xl bookcdc4cv
Consumer Depth Cameras for Computer Vision - Research Topics and Applications

Fossati, A., Gall, J., Grabner, H., Ren, X., Konolige, K.

Advances in Computer Vision and Pattern Recognition, Springer, 2012 (book)

workshop publisher's site [BibTex]

workshop publisher's site [BibTex]


Thumb xl amdo2012v2
Spatial Measures between Human Poses for Classification and Understanding

Soren Hauberg, Kim S. Pedersen

In Articulated Motion and Deformable Objects, 7378, pages: 26-36, LNCS, (Editors: Perales, Francisco J. and Fisher, Robert B. and Moeslund, Thomas B.), Springer Berlin Heidelberg, 2012 (inproceedings)

Publishers site Project Page [BibTex]

Publishers site Project Page [BibTex]


Thumb xl nips teaser
A Geometric Take on Metric Learning

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

In Advances in Neural Information Processing Systems (NIPS) 25, pages: 2033-2041, (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
Multi-metric learning techniques learn local metric tensors in different parts of a feature space. With such an approach, even simple classifiers can be competitive with the state-of-the-art because the distance measure locally adapts to the structure of the data. The learned distance measure is, however, non-metric, which has prevented multi-metric learning from generalizing to tasks such as dimensionality reduction and regression in a principled way. We prove that, with appropriate changes, multi-metric learning corresponds to learning the structure of a Riemannian manifold. We then show that this structure gives us a principled way to perform dimensionality reduction and regression according to the learned metrics. Algorithmically, we provide the first practical algorithm for computing geodesics according to the learned metrics, as well as algorithms for computing exponential and logarithmic maps on the Riemannian manifold. Together, these tools let many Euclidean algorithms take advantage of multi-metric learning. We illustrate the approach on regression and dimensionality reduction tasks that involve predicting measurements of the human body from shape data.

PDF Youtube Suppl. material Poster Project Page [BibTex]

PDF Youtube Suppl. material Poster Project Page [BibTex]