Results 1 - 10 of 110

Recovering 3D Human Pose from Monocular Images

by Ankur Agarwal, Bill Triggs
"... We describe a learning based method for recovering 3D human body pose from single images and monocular image sequences. Our approach requires neither an explicit body model nor prior labelling of body parts in the image. Instead, it recovers pose by direct nonlinear regression against shape descrip ..."
Abstract - Cited by 261 (0 self) - Add to MetaCart
We describe a learning based method for recovering 3D human body pose from single images and monocular image sequences. Our approach requires neither an explicit body model nor prior labelling of body parts in the image. Instead, it recovers pose by direct nonlinear regression against shape

Learning Coupled Prior Shape and Appearance Models for Segmentation

by Xiaolei Huang, Zhiguo Li, Dimitris Metaxas - In Proc. of the 7th Annual Int’l Conf. on Medical Image Computing and Computer Assisted Intervention , 2004
"... Abstract. We present a novel framework for learning a joint shape and appearance model from a large set of un-labelled training examples in arbitrary positions and orientations. The shape and intensity spaces are unified by implicitly representing shapes as “images ” in the space of distance transfo ..."
Abstract - Cited by 11 (3 self) - Add to MetaCart
Abstract. We present a novel framework for learning a joint shape and appearance model from a large set of un-labelled training examples in arbitrary positions and orientations. The shape and intensity spaces are unified by implicitly representing shapes as “images ” in the space of distance

STACS: A new active contour scheme for cardiac MR image segmentation

by Charnchai Pluempitiwiriyawej, José M. F. Moura, Yi-jen Lin Wu, Chien Ho - IEEE Trans. Med. Imag , 2005
"... Abstract—The paper presents a novel stochastic active contour scheme (STACS) for automatic image segmentation designed to overcome some of the unique challenges in cardiac MR images such as problems with low contrast, papillary muscles, and turbulent blood flow. STACS minimizes an energy functional ..."
Abstract - Cited by 57 (7 self) - Add to MetaCart
and region-based information; 3) ability to segment the heart from the chest wall and the undesired papillary muscles due to inclusion of heart shape priors. Application of STACS to a set of 48 real cardiac MR images shows that it can successfully segment the heart from its surroundings such as the chest

3D Human Pose and Shape Estimation from Multi-view Imagery

by Atul Kanaujia, Niels Haering, Graham Taylor, Chris Bregler
"... In this study we present robust solution for estimating 3D pose and shape of human targets from multiple, synchronized video streams. The objective is to automatically estimate physical attributes of the targets that would allow us to analyze its behavior non-intrusively. Proposed system estimates t ..."
Abstract - Cited by 3 (0 self) - Add to MetaCart
(top-down) method that also estimates the optimal skeleton of the target using anthropometric prior models learned from the CAESAR dataset. Statistical shape models are also learned from the CAESAR dataset and are used to model both global and local shape variability of human body parts. We also

A shape-based approach to robust image segmentation

by Samuel Dambreville, Yogesh Rathi, Allen Tannenbaum - IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 4141 of LNCS , 2006
"... Segmentation involves separating an object from the background. In this work, we propose a novel segmentation method combining image information with prior shape knowledge, within the level-set framework. Following the work of Leventon et al., we revisit the use of principal component analysis (PCA) ..."
Abstract - Cited by 36 (8 self) - Add to MetaCart
) to introduce prior knowledge about shapes in a more robust manner. To this end, we utilize Kernel PCA and show that this method of learning shapes outperforms linear PCA, by allowing only shapes that are close enough to the training data. In the proposed segmentation algorithm, shape knowledge and image

3D Human Pose from Silhouettes by Relevance Vector Regression

by Ankur Agarwal, Bill Triggs - In CVPR , 2004
"... We describe a learning based method for recovering 3D human body pose from single images and monocular image sequences. Our approach requires neither an explicit body model nor prior labelling of body parts in the image. Instead, it recovers pose by direct nonlinear regression against shape descript ..."
Abstract - Cited by 199 (8 self) - Add to MetaCart
We describe a learning based method for recovering 3D human body pose from single images and monocular image sequences. Our approach requires neither an explicit body model nor prior labelling of body parts in the image. Instead, it recovers pose by direct nonlinear regression against shape

Learning for Multi-View 3D Tracking in the Context of Particle Filters

by Juergen Gall, Bodo Rosenhahn, Thomas Brox, Hans-peter Seidel
"... Abstract. In this paper we present an approach to use prior knowledge in the particle filter framework for 3D tracking, i.e. estimating the state parameters such as joint angles of a 3D object. The probability of the object’s states, including correlations between the state parameters, is learned a ..."
Abstract - Cited by 8 (4 self) - Add to MetaCart
knowledge and the robustness of our approach to image distortions. Finally, we compare the results of our multi-view tracking system quantitatively to the outcome of an industrial marker based tracking system. 1

3-D active appearance models: segmentation of cardiac MR and ultrasound images

by Steven C. Mitchell, Johan G. Bosch, Boudewijn P. F. Lelieveldt, Rob J. Van Der Geest, Johan H. C. Reiber, Milan Sonka - IEEE Transactions on Medical Imaging
"... Abstract—A model-based method for three-dimensional image segmentation was developed and its performance assessed in segmentation of volumetric cardiac magnetic resonance (MR) images and echocardiographic temporal image sequences. Com-prehensive design of a three-dimensional (3-D) active appearance ..."
Abstract - Cited by 68 (7 self) - Add to MetaCart
model (AAM) is reported for the first time as an involved extension of the AAM framework introduced by Cootes et al. The model’s behavior is learned from manually traced segmentation examples during an automated training stage. Information about shape and image appearance of the cardiac structures

Learning to Segment Dense Cell Nuclei with Shape Prior

by Xinghua Lou, Ullrich Koethe, Jochen Wittbrodt, Fred A. Hamprecht
"... We study the problem of segmenting multiple cell nucle-i from GFP or Hoechst stained microscope images with a shape prior. This problem is encountered ubiquitously in cell biology and developmental biology. Our work is mo-tivated by the observation that segmentations with loose boundary or shrinking ..."
Abstract - Cited by 7 (4 self) - Add to MetaCart
We study the problem of segmenting multiple cell nucle-i from GFP or Hoechst stained microscope images with a shape prior. This problem is encountered ubiquitously in cell biology and developmental biology. Our work is mo-tivated by the observation that segmentations with loose boundary

Estimating patient-specific shape prior for medical image segmentation

by Wuxia Zhang, Pingkun Yan, Xuelong Li - In International Symposium on Biomedical Imaging , 2011
"... Image segmentation is one of the key problems in medical image analysis. This paper presents a new statistical shape model for automatic image segmentation. In contrast to the previous model based segmentation methods, where shape priors are estimated from a general population-based shape model, our ..."
Abstract - Cited by 7 (0 self) - Add to MetaCart
method has been demonstrated by the experiments on segmenting the prostate from MR images. Index Terms — image segmentation, shape modeling, manifold learning 1.
Results 1 - 10 of 110