JOURNAL ARTICLE

Object Shape Recognition Using Mexican Hat Wavelet Descriptors

Abstract

In this paper we present the results of object recognition using Mexican Hat wavelet descriptors. These descriptors are derived from the continuous wavelet transformation using the Mexican Hat function as mother wavelet. To describe an object shape we use an angle function derived from the extracted contour polygon. The angle function is periodical and independent from the size of the object, its position or orientation. It depends only on the starting point of the contour. The contour extraction is based here on the object oriented contour extraction method (OCE). The polygon representation uses the curvature dependent contour approximation method (CDCA). The continuous wavelet transform (CWT) is used in order to derive a suitable number of wavelet descriptors (WD).

Keywords:
Wavelet Artificial intelligence Pattern recognition (psychology) Computer vision Polygon (computer graphics) Wavelet transform Transformation (genetics) Object (grammar) Active contour model Mathematics Curvature Representation (politics) Position (finance) Computer science Image segmentation Segmentation Geometry

Metrics

11
Cited By
1.50
FWCI (Field Weighted Citation Impact)
11
Refs
0.83
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
Medical Image Segmentation Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Image Processing and 3D Reconstruction
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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