A New Shape-Based Object Category Recognition Technique using Affine Category Shape Model

Title
A New Shape-Based Object Category Recognition Technique using Affine Category Shape Model
Authors
김동환최유경박성기
Keywords
Object Category Recognition; category; contour fragment; Spectral Matching; Affine Category Shape Model; Second-Order Constraints; RANSAC
Issue Date
2009-09
Publisher
로봇공학회논문지= The Journal of Korea Robotics Society
Citation
VOL 4, NO 3, 185-191
Abstract
This paper presents a new shape-based algorithm using affine category shape model for object category recognition and model learning. Affine category shape model is a graph of interconnected nodes whose geometric interactions are modeled using pairwise potentials. In its learning phase, it can efficiently handle large pose variations of objects in training images by estimating 2-D homography transformation between the model and the training images. Since the pairwise potentials are defined on only relative geometric relationship between features, the proposed matching algorithm is translation and in-plane rotation invariant and robust to affine transformation. We apply spectral matching algorithm to find feature correspondences, which are then used as initial correspondences for RANSAC algorithm. The 2-D homography transformation and the inlier correspondences which are consistent with this estimate can be efficiently estimated through RANSAC, and new correspondences also can be detected by using the estimated 2-D homography transformation. Experimental results on object category database show that the proposed algorithm is robust to pose variation of objects and provides good recognition performance.
URI
http://pubs.kist.re.kr/handle/201004/36425
ISSN
1975-6291
Appears in Collections:
KIST Publication > Article
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