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» Learning Visual Invariance
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DICTA
2009
13 years 8 months ago
SIFTing the Relevant from the Irrelevant: Automatically Detecting Objects in Training Images
Many state-of-the-art object recognition systems rely on identifying the location of objects in images, in order to better learn its visual attributes. In this paper, we propose fo...
Edmond Zhang, Michael Mayo
AROBOTS
2010
128views more  AROBOTS 2010»
13 years 7 months ago
Track-based self-supervised classification of dynamic obstacles
Abstract This work introduces a self-supervised architecture for robust classification of moving obstacles in urban environments. Our approach presents a hierarchical scheme that r...
Roman Katz, Juan Nieto, Eduardo Mario Nebot, Bertr...
PAMI
2006
138views more  PAMI 2006»
13 years 7 months ago
Context-Based Segmentation of Image Sequences
We describe an algorithm for context-based segmentation of visual data. New frames in an image sequence (video) are segmented based on the prior segmentation of earlier frames in ...
Jacob Goldberger, Hayit Greenspan
TKDE
2008
123views more  TKDE 2008»
13 years 7 months ago
Explaining Classifications For Individual Instances
We present a method for explaining predictions for individual instances. The presented approach is general and can be used with all classification models that output probabilities...
Marko Robnik-Sikonja, Igor Kononenko
TOG
2008
123views more  TOG 2008»
13 years 7 months ago
Example-based dynamic skinning in real time
In this paper we present an approach to enrich skeleton-driven animations with physically-based secondary deformation in real time. To achieve this goal, we propose a novel, surfa...
Xiaohan Shi, Kun Zhou, Yiying Tong, Mathieu Desbru...