Shape analysis using multiscale hough transform statistics

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Ramos, Lucas Alexandre [UNESP]
de Souza, Gustavo Botelho
Marana, Aparecido Nilceu [UNESP]

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With the widespread proliferation of computers, many human activities entail the use of automatic image analysis. The basic features used for image analysis include color, texture, and shape. In this paper, we propose MHTS (Multiscale Hough Transform Statistics), a multiscale version of the shape description method called HTS (Hough Transform Statistics). Likewise HTS, MHTS uses statistics from the Hough Transform to characterize the shape of objects or regions in digital images. Experiments carried out on MPEG-7 CE-1 (Part B) shape database show that MHTS is better than the original HTS, and presents superior precision–recall results than some well-known shape description methods, such as: Tensor Scale, Multiscale Fractal Dimension, Fourier, and Contour Salience. Besides, when using the multiscale separability criterion, MHTS is also superior to Zernike Moments and Beam Angle Statistics (BAS) methods. The linear complexity of the HTS algorithm was preserved in this new multiscale version, making MHTS even more appropriate than BAS method for shape analysis in high-resolution image retrieval tasks when very large databases are used.



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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9423, p. 452-459.