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2018


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Method and Apparatus for Estimating Body Shape

Black, M. J., Balan, A., Weiss, A., Sigal, L., Loper, M., St Clair, T.

June 2018, U.S.~Patent 10,002,460 (misc)

Abstract
A system and method of estimating the body shape of an individual from input data such as images or range maps. The body may appear in one or more poses captured at different times and a consistent body shape is computed for all poses. The body may appear in minimal tight-fitting clothing or in normal clothing wherein the described method produces an estimate of the body shape under the clothing. Clothed or bare regions of the body are detected via image classification and the fitting method is adapted to treat each region differently. Body shapes are represented parametrically and are matched to other bodies based on shape similarity and other features. Standard measurements are extracted using parametric or non-parametric functions of body shape. The system components support many applications in body scanning, advertising, social networking, collaborative filtering and Internet clothing shopping.

Google Patents Project Page [BibTex]

2018

Google Patents Project Page [BibTex]


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Co-Registration – Simultaneous Alignment and Modeling of Articulated 3D Shapes

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

Febuary 2018, U.S.~Patent 9,898,848 (misc)

Abstract
Present application refers to a method, a model generation unit and a computer program (product) for generating trained models (M) of moving persons, based on physically measured person scan data (S). The approach is based on a common template (T) for the respective person and on the measured person scan data (S) in different shapes and different poses. Scan data are measured with a 3D laser scanner. A generic personal model is used for co-registering a set of person scan data (S) aligning the template (T) to the set of person scans (S) while simultaneously training the generic personal model to become a trained person model (M) by constraining the generic person model to be scan-specific, person-specific and pose-specific and providing the trained model (M), based on the co registering of the measured object scan data (S).

text [BibTex]

2011


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Benchmark datasets for pose estimation and tracking

Andriluka, M., Sigal, L., Black, M. J.

In Visual Analysis of Humans: Looking at People, pages: 253-274, (Editors: Moesland and Hilton and Kr"uger and Sigal), Springer-Verlag, London, 2011 (incollection)

publisher's site Project Page [BibTex]

2011

publisher's site Project Page [BibTex]


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Fields of experts

Roth, S., Black, M. J.

In Markov Random Fields for Vision and Image Processing, pages: 297-310, (Editors: Blake, A. and Kohli, P. and Rother, C.), MIT Press, 2011 (incollection)

Abstract
Fields of Experts are high-order Markov random field (MRF) models with potential functions that extend over large pixel neighborhoods. The clique potentials are modeled as a Product of Experts using nonlinear functions of many linear filter responses. In contrast to previous MRF approaches, all parameters, including the linear filters themselves, are learned from training data. A Field of Experts (FoE) provides a generic, expressive image prior that can capture the statistics of natural scenes, and can be used for a variety of machine vision tasks. The capabilities of FoEs are demonstrated with two example applications, image denoising and image inpainting, which are implemented using a simple, approximate inference scheme. While the FoE model is trained on a generic image database and is not tuned toward a specific application, the results compete with specialized techniques.

publisher site [BibTex]

publisher site [BibTex]


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Steerable random fields for image restoration and inpainting

Roth, S., Black, M. J.

In Markov Random Fields for Vision and Image Processing, pages: 377-387, (Editors: Blake, A. and Kohli, P. and Rother, C.), MIT Press, 2011 (incollection)

Abstract
This chapter introduces the concept of a Steerable Random Field (SRF). In contrast to traditional Markov random field (MRF) models in low-level vision, the random field potentials of a SRF are defined in terms of filter responses that are steered to the local image structure. This steering uses the structure tensor to obtain derivative responses that are either aligned with, or orthogonal to, the predominant local image structure. Analysis of the statistics of these steered filter responses in natural images leads to the model proposed here. Clique potentials are defined over steered filter responses using a Gaussian scale mixture model and are learned from training data. The SRF model connects random fields with anisotropic regularization and provides a statistical motivation for the latter. Steering the random field to the local image structure improves image denoising and inpainting performance compared with traditional pairwise MRFs.

publisher site [BibTex]

publisher site [BibTex]


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Model-Based Pose Estimation

Pons-Moll, G., Rosenhahn, B.

In Visual Analysis of Humans: Looking at People, pages: 139-170, 9, (Editors: T. Moeslund, A. Hilton, V. Krueger, L. Sigal), Springer, 2011 (inbook)

book page pdf [BibTex]

book page pdf [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]


no image
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]

1998


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Looking at people in action - An overview

Yacoob, Y., Davis, L. S., Black, M., Gavrila, D., Horprasert, T., Morimoto, C.

In Computer Vision for Human–Machine Interaction, (Editors: R. Cipolla and A. Pentland), Cambridge University Press, 1998 (incollection)

publisher site google books [BibTex]

1998

publisher site google books [BibTex]