Recognition of 2-D Objects by Optimal Matching | Zendy

Lu Liu | Zendy; Fang Luo | Zendy; Nanno Mulder | Zendy

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Recognition of 2-D Objects by Optimal Matching

Author(s) -

Lu Liu,

Fang Luo,

Nanno Mulder

Publication year - 1994

Publication title -

citeseer x (the pennsylvania state university)

Language(s) - English

Resource type - Conference proceedings

DOI - 10.5244/c.8.36

Subject(s) - matching (statistics) , object (grammar) , cognitive neuroscience of visual object recognition , artificial intelligence , feature (linguistics) , computer science , pattern recognition (psychology) , optimal matching , minification , computer vision , feature extraction , identification (biology) , process (computing) , 3d single object recognition , feature matching , object detection , mathematics , statistics , linguistics , philosophy , botany , biology , programming language , operating system

This paper introduces an approach of recognizing 2-D objects by optimal matching. The method consists of two stages: object identification and object localization. Both of them are accomplished through optimal feature matching, in which the radiometric distribution of an object as a global feature extracted from an image is matched directly to the object model. A cost function is defined as a quantitative evaluation of the feature fitting and the recognition process is based on cost minimization. In this method, every subproblem in object recognition is formulated as an optimization problem and techniques of optimization are utilized to solve these problems.

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