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» Novel image feature alphabets for object recognition
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ECCV
2004
Springer
15 years 11 months ago
Recognition by Probabilistic Hypothesis Construction
We present a probabilistic framework for recognizing objects in images of cluttered scenes. Hundreds of objects may be considered and searched in parallel. Each object is learned f...
Pierre Moreels, Michael Maire, Pietro Perona
ICPR
2004
IEEE
15 years 11 months ago
Fast Object and Pose Recognition Through Minimum Entropy Coding
We present a pattern recognizer to classify a variety of objects and their pose on a table from real world images. Learning of weights in a linear discriminant is based on estimat...
Günter Westphal, Rolf P. Würtz
CVPR
2010
IEEE
15 years 2 months ago
Model Globally, Match Locally: Efficient and Robust 3D Object Recognition
This paper addresses the problem of recognizing freeform 3D objects in point clouds. Compared to traditional approaches based on point descriptors, which depend on local informati...
Bertram Drost, Markus Ulrich, Nassir Navab, Slobod...
VISUAL
1999
Springer
15 years 2 months ago
Highly Discriminative Invariant FEatures for Image Matching
Abstract. In this paper, we present novel image-derived, invariant features that accurately capture both the geometric and color properties of an imaged object. These features can ...
Ronald-Bryan O. Alferez, Yuan-Fang Wang
ICCV
2007
IEEE
15 years 4 months ago
Latent Model Clustering and Applications to Visual Recognition
We consider clustering situations in which the pairwise affinity between data points depends on a latent ”context” variable. For example, when clustering features arising fro...
Simon Polak, Amnon Shashua