JOURNAL ARTICLE

A new generalized affine moment invariants for shape retrieval and object recognition

Abstract

Shape retrieval and object recognition are two important tasks in pattern recognition society. Whether they can be achieved or not owes a lot to the performance of a descriptor primarily. In this paper, a new generalized affine moment invariants called illumination invariant MSA moments is proposed. This method combines the traditional AMI, MSA, MSA moment and the basic ideas used to construct illumination invariants. The results suggest that the new technique offers well discriminative power equivalent to the MSA in shape retrieval task, but is much more robust under illumination distortion and Gaussian noise. In 3D object recognition task, the proposed method outperforms both comparison methods under strong non-affine transformation.

Keywords:
Discriminative model Affine transformation Artificial intelligence Invariant (physics) Pattern recognition (psychology) Moment (physics) Affine combination Affine shape adaptation Cognitive neuroscience of visual object recognition Computer science Transformation (genetics) Harris affine region detector Computer vision Object (grammar) Mathematics Pure mathematics

Metrics

2
Cited By
0.28
FWCI (Field Weighted Citation Impact)
16
Refs
0.60
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Image Retrieval and Classification Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Remote-Sensing Image Classification
Physical Sciences →  Engineering →  Media Technology

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