Abstract
Two-dimensional sample entropy (SampEn 2D ) has been recently proposed to quantify the irregularity of textures. However, when dealing with small-sized textures, SampEn 2D may lead to either undefined or unreliable values. Moreover, SampEn 2D is too slow for most real-time applications. To alleviate these deficiencies, we introduce bidimensional distribution entropy (DistrEn 2D ). We evaluate DistrEn 2D on both synthetic and real texture datasets. The results indicate that DistrEn 2D can detect different amounts of white Gaussian and salt and pepper noise, and discriminate periodic from synthesized textures. The results also show that DistrEn 2D distinguishes different kinds of textured surfaces. In addition, DistrEn 2D , unlike SampEn 2D , does not lead to undefined values. Moreover, DistrEn 2D is noticeably faster than SampEn 2D . Overall, DistrEn 2D -as an insensitive feature extraction method to rotation-is expected to be very useful for the analysis of real image textures.
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