Multiple View Object Recognition Based on Iterative Closest Points Framework
LIU Zhou-feng
ZHAO Ya-ru
LI Chun-lei
WANG Bao-rui
LIU Chao-die
Abstract：Object recognition plays a great role in machine vision.Traditional image recognition just considers only viewpoint.An object recognition system has been developed in this paper.Firstly,Gaussian scale space is built to extract scale invariant feature points, and the SURF descriptor is utilized to characterize those feature points and get the transformation parameters.Then, feature similarity is combined with spatial consistency based on the iterate closest point.Finally, object is recognized in multiple view.Comparative experiments show that the algorithm has high performance in accuracy and robustness.
Keywords：feature similarityimage registrationlocal structure constraintsspatial consistency
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 73-77 )