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Continuous Reconstruction of Scene Objects

by Steen Kristensen, Henrik Iskov Christensen - Proceedings of SPIE '93, Sensor Fusion VI , 1993
"... In this paper we describe an approach to continuous scene modeling for an autonomous mobile robot navigation system operating in indoor environments. The continuous scene modeling is based on a cooperative sensor system that comprises two parts: binocular region based stereo, i.e., a passive depth e ..."
Abstract - Cited by 2 (1 self) - Add to MetaCart
at locations where potential occlusions are detected in order to extract the correct depth. Scene maintenance over time is done by generation of expectation images, based on previously sensed scene objects, that are matched with images, recorded by the on--robot stereo camera head. This match allows

Learning hierarchical models of scenes, objects, and parts

by Erik B. Sudderth, Antonio Torralba, William T. Freeman, Alan S. Willsky - In IEEE Intl. Conf. on Computer Vision , 2005
"... We describe a hierarchical probabilistic model for the detection and recognition of objects in cluttered, natural scenes. The model is based on a set of parts which describe the expected appearance and position, in an object centered coordinate frame, of features detected by a low-level interest ope ..."
Abstract - Cited by 188 (14 self) - Add to MetaCart
We describe a hierarchical probabilistic model for the detection and recognition of objects in cluttered, natural scenes. The model is based on a set of parts which describe the expected appearance and position, in an object centered coordinate frame, of features detected by a low-level interest

Saliency of Scene Object

by Ashwini Nanaware, Harish Barapatre
"... Computationally detecting salient object from confusing background is most important in computer vision system. Salient object is unique object which stands out different and having closed boundary in space. Saliency at a given location is determined primarily by how different current location is fr ..."
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for their response to scene images. Early results indicate that saliency maps produced by these attention models can be used for salient object detection algorithms.

Using spin images for efficient object recognition in cluttered 3D scenes

by Andrew E. Johnson, Martial Hebert - IEEE Transactions on Pattern Analysis and Machine Intelligence , 1999
"... We present a 3-D shape-based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion. Recognition is based on matching surfaces by matching points using the spin-image representation. The spin-image is a data level shape descriptor that i ..."
Abstract - Cited by 582 (9 self) - Add to MetaCart
We present a 3-D shape-based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion. Recognition is based on matching surfaces by matching points using the spin-image representation. The spin-image is a data level shape descriptor

Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope

by Aude Oliva, Antonio Torralba - International Journal of Computer Vision , 2001
"... In this paper, we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions. The procedure is based on a very low dimensional representation of the scene, that we term the Spatial Envelope. We propose a se ..."
Abstract - Cited by 1313 (81 self) - Add to MetaCart
In this paper, we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions. The procedure is based on a very low dimensional representation of the scene, that we term the Spatial Envelope. We propose a

Evaluating Color Descriptors for Object and Scene Recognition

by Koen E. A. van de Sande, Theo Gevers, Cees G. M. Snoek , 2010
"... Image category recognition is important to access visual information on the level of objects and scene types. So far, intensity-based descriptors have been widely used for feature extraction at salient points. To increase illumination invariance and discriminative power, color descriptors have been ..."
Abstract - Cited by 423 (33 self) - Add to MetaCart
Image category recognition is important to access visual information on the level of objects and scene types. So far, intensity-based descriptors have been widely used for feature extraction at salient points. To increase illumination invariance and discriminative power, color descriptors have

Object Tracking: A Survey

by Alper Yilmaz, Omar Javed, Mubarak Shah , 2006
"... The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns o ..."
Abstract - Cited by 701 (7 self) - Add to MetaCart
of both the object and the scene, nonrigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the object in every frame. Typically, assumptions are made

Understanding Line Drawings of Scenes with Shadows

by David Waltz - The Psychology of Computer Vision , 1975
"... this paper, how can we recognize the identity of Figs. 2.1 and 2.2? Do we use' learning and knowledge to interpret what we see, or do we somehow automatically see the world as stable and independent bf lighting? What portions of scenes can we understand from local features alone, and what confi ..."
Abstract - Cited by 436 (0 self) - Add to MetaCart
configurations require the use of 1obal hypotheses? 19 In this essay I describe a working collection of computer programs which reconstruct three-dimensional descriptions from line drawings which are obtained from scenes composed of plane-faced objects under various lighting conditions. The system identifies

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
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