Results 1 - 10 of 28,970

The 2005 pascal visual object classes challenge

by Mark Everingham, Andrew Zisserman, Christopher K. I. Williams, Luc Van Gool, Moray Allan, Christopher M. Bishop, Olivier Chapelle, Navneet Dalal, Thomas Deselaers, Gyuri Dorkó, Stefan Duffner, Jan Eichhorn, Jason D. R. Farquhar, Mario Fritz, Christophe Garcia, Tom Griffiths, Frederic Jurie, Daniel Keysers, Markus Koskela, Jorma Laaksonen, Diane Larlus, Bastian Leibe, Hongying Meng, Hermann Ney, Bernt Schiele, Cordelia Schmid, Edgar Seemann, John Shawe-taylor, Amos Storkey, Or Szedmak, Bill Triggs, Ilkay Ulusoy, Ville Viitaniemi, Jianguo Zhang , 2006
"... Abstract. The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). Four object classes were selected: motorbikes, bicycles, cars and peop ..."
Abstract - Cited by 649 (23 self) - Add to MetaCart
Abstract. The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). Four object classes were selected: motorbikes, bicycles, cars

Object class recognition by unsupervised scale-invariant learning

by R. Fergus, P. Perona, A. Zisserman - In CVPR , 2003
"... We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constellations of parts. A probabilistic representation is used for all aspects of the object: shape, appearance, occlusion and ..."
Abstract - Cited by 1127 (50 self) - Add to MetaCart
We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constellations of parts. A probabilistic representation is used for all aspects of the object: shape, appearance, occlusion

The PASCAL Visual Object Classes (VOC) Challenge

by M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, A. Zisserman - INTERNATIONAL JOURNAL OF COMPUTER VISION
"... ... and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection. ..."
Abstract - Cited by 629 (20 self) - Add to MetaCart
... and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection

Learning to detect unseen object classes by betweenclass attribute transfer

by Christoph H. Lampert, Hannes Nickisch, Stefan Harmeling - In CVPR , 2009
"... We study the problem of object classification when training and test classes are disjoint, i.e. no training examples of the target classes are available. This setup has hardly been studied in computer vision research, but it is the rule rather than the exception, because the world contains tens of t ..."
Abstract - Cited by 363 (5 self) - Add to MetaCart
We study the problem of object classification when training and test classes are disjoint, i.e. no training examples of the target classes are available. This setup has hardly been studied in computer vision research, but it is the rule rather than the exception, because the world contains tens

Object Class

by Rob Fergus, Yan Huang
"... •  Learning Feature Hierarchies for Vision –  For object recognition •  is talk: Basic concepts Links to existing vision approaches ..."
Abstract - Add to MetaCart
•  Learning Feature Hierarchies for Vision –  For object recognition •  is talk: Basic concepts Links to existing vision approaches

LOCUS: Learning Object Classes with Unsupervised Segmentation

by J. Winn - in ICCV , 2005
"... We address the problem of learning object class models and object segmentations from unannotated images. We introduce LOCUS (Learning Object Classes with Unsupervised Segmentation) which uses a generative probabilistic model to combine bottom-up cues of color and edge with top-down cues of shape and ..."
Abstract - Cited by 195 (8 self) - Add to MetaCart
We address the problem of learning object class models and object segmentations from unannotated images. We introduce LOCUS (Learning Object Classes with Unsupervised Segmentation) which uses a generative probabilistic model to combine bottom-up cues of color and edge with top-down cues of shape

Recognition of Planar Object Classes

by M.C. Burl, P. Perona - In Proc. IEEE Comput. Soc. Conf. Comput. Vision and Pattern Recogn , 1996
"... We present a new framework for recognizing planar object classes, which is based on local feature detectors and a probabilistic model of the spatial arrangement of the features. The allowed object deformations are represented through shape statistics, which are learned from examples. Instances of an ..."
Abstract - Cited by 80 (12 self) - Add to MetaCart
We present a new framework for recognizing planar object classes, which is based on local feature detectors and a probabilistic model of the spatial arrangement of the features. The allowed object deformations are represented through shape statistics, which are learned from examples. Instances

Associative hierarchical CRFs for object class image segmentation

by Chris Russell, Philip H. S. Torr, Pushmeet Kohli - in Proc. ICCV , 2009
"... Most methods for object class segmentation are formulated as a labelling problem over a single choice of quantisation of an image space- pixels, segments or group of segments. It is well known that each quantisation has its fair share of pros and cons; and the existence of a common optimal quantisat ..."
Abstract - Cited by 172 (25 self) - Add to MetaCart
Most methods for object class segmentation are formulated as a labelling problem over a single choice of quantisation of an image space- pixels, segments or group of segments. It is well known that each quantisation has its fair share of pros and cons; and the existence of a common optimal

Linear Object Classes and Image Synthesis From a Single Example Image

by Thomas Vetter, Tomaso Poggio - IEEE Transactions on Pattern Analysis and Machine Intelligence , 1997
"... Abstract—The need to generate new views of a 3D object from a single real image arises in several fields, including graphics and object recognition. While the traditional approach relies on the use of 3D models, we have recently introduced [1], [2], [3] simpler techniques that are applicable under r ..."
Abstract - Cited by 235 (25 self) - Add to MetaCart
restricted conditions. The approach exploits image transformations that are specific to the relevant object class, and learnable from example views of other “prototypical ” objects of the same class. In this paper, we introduce such a technique by extending the notion of linear class proposed by Poggio

Object Detection with Discriminatively Trained Part Based Models

by Pedro F. Felzenszwalb, Ross B. Girshick, David McAllester, Deva Ramanan
"... We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular, their ..."
Abstract - Cited by 1422 (49 self) - Add to MetaCart
We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular
Results 1 - 10 of 28,970