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2013


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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]

2013

Publishers site [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]


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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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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]

2012

pdf [BibTex]


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Data-driven Manifolds for Outdoor Motion Capture

Pons-Moll, G., Leal-Taix’e, L., Gall, J., Rosenhahn, B.

In Outdoor and Large-Scale Real-World Scene Analysis, 7474, pages: 305-328, LNCS, (Editors: Dellaert, Frank and Frahm, Jan-Michael and Pollefeys, Marc and Rosenhahn, Bodo and Leal-Taix’e, Laura), Springer, 2012 (incollection)

video publisher's site pdf Project Page [BibTex]

video publisher's site pdf Project Page [BibTex]


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Scan-Based Flow Modelling in Human Upper Airways

Perumal Nithiarasu, Igor Sazonov, Si Yong Yeo

In Patient-Specific Modeling in Tomorrow’s Medicine, pages: 241 - 280, 0, (Editors: Amit Gefen), Springer, 2012 (inbook)

[BibTex]

[BibTex]


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An Introduction to Random Forests for Multi-class Object Detection

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

In Outdoor and Large-Scale Real-World Scene Analysis, 7474, pages: 243-263, LNCS, (Editors: Dellaert, Frank and Frahm, Jan-Michael and Pollefeys, Marc and Rosenhahn, Bodo and Leal-Taix’e, Laura), Springer, 2012 (incollection)

code code for Hough forest publisher's site pdf Project Page [BibTex]

code code for Hough forest publisher's site pdf Project Page [BibTex]


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Home 3D body scans from noisy image and range data

Weiss, A., Hirshberg, D., Black, M. J.

In Consumer Depth Cameras for Computer Vision: Research Topics and Applications, pages: 99-118, 6, (Editors: Andrea Fossati and Juergen Gall and Helmut Grabner and Xiaofeng Ren and Kurt Konolige), Springer-Verlag, 2012 (incollection)

Project Page [BibTex]

Project Page [BibTex]

2009


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An introduction to Kernel Learning Algorithms

Gehler, P., Schölkopf, B.

In Kernel Methods for Remote Sensing Data Analysis, pages: 25-48, 2, (Editors: Gustavo Camps-Valls and Lorenzo Bruzzone), Wiley, New York, NY, USA, 2009 (inbook)

Abstract
Kernel learning algorithms are currently becoming a standard tool in the area of machine learning and pattern recognition. In this chapter we review the fundamental theory of kernel learning. As the basic building block we introduce the kernel function, which provides an elegant and general way to compare possibly very complex objects. We then review the concept of a reproducing kernel Hilbert space and state the representer theorem. Finally we give an overview of the most prominent algorithms, which are support vector classification and regression, Gaussian Processes and kernel principal analysis. With multiple kernel learning and structured output prediction we also introduce some more recent advancements in the field.

link (url) DOI [BibTex]

2009

link (url) DOI [BibTex]


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Visual Object Discovery

Sinha, P., Balas, B., Ostrovsky, Y., Wulff, J.

In Object Categorization: Computer and Human Vision Perspectives, pages: 301-323, (Editors: S. J. Dickinson, A. Leonardis, B. Schiele, M.J. Tarr), Cambridge University Press, 2009 (inbook)

link (url) [BibTex]

link (url) [BibTex]

2002


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Bayesian Inference of Visual Motion Boundaries

Fleet, D. J., Black, M. J., Nestares, O.

In Exploring Artificial Intelligence in the New Millennium, pages: 139-174, (Editors: Lakemeyer, G. and Nebel, B.), Morgan Kaufmann Pub., July 2002 (incollection)

Abstract
This chapter addresses an open problem in visual motion analysis, the estimation of image motion in the vicinity of occlusion boundaries. With a Bayesian formulation, local image motion is explained in terms of multiple, competing, nonlinear models, including models for smooth (translational) motion and for motion boundaries. The generative model for motion boundaries explicitly encodes the orientation of the boundary, the velocities on either side, the motion of the occluding edge over time, and the appearance/disappearance of pixels at the boundary. We formulate the posterior probability distribution over the models and model parameters, conditioned on the image sequence. Approximate inference is achieved with a combination of tools: A Bayesian filter provides for online computation; factored sampling allows us to represent multimodal non-Gaussian distributions and to propagate beliefs with nonlinear dynamics from one time to the next; and mixture models are used to simplify the computation of joint prediction distributions in the Bayesian filter. To efficiently represent such a high-dimensional space, we also initialize samples using the responses of a low-level motion-discontinuity detector. The basic formulation and computational model provide a general probabilistic framework for motion estimation with multiple, nonlinear models.

pdf [BibTex]

2002

pdf [BibTex]