Abstract
AbstractComputer vision-based rice quality inspection has recently attracted increasing interest in both academic and industrial communities because it is a low-cost tool for fast, non-contact, nondestructive, accurate and objective process monitoring. However, current computer-vision system is far from effective in intelligent perception of complex grainy images, comprised of a large number of local homogeneous particles or fragmentations without obvious foreground and background. We introduce a well known statistical modeling theory of size distribution in comminution processes, sequential fragmentation theory, for the visual analysis of the spatial structure of the complex grainy images. A kind of omnidirectional multi-scale Gaussian derivative filter-based image statistical modeling method is presented to attain omnidirectional structural features of grain images under different observation scales. A modified LS-SVM classifier is subsequently established to automatically identify the processing rice q...
Highlights
With the rapid development of the national economy and the improvement of the living standards of the human beings, consumers put forward higher and higher requirements on food quality
In order to overcome the problems of understanding the complex grainy images comprised of a large number of local homogeneous particles or fragmentations, without obvious distinction between foreground and background, for effective rice quality classification, we present an image statistical modelingbased rice quality inspection method in this study
The computer vision inspection system analyzes the rice images and identifies the rice quality with the iLS-support vector machine (SVM) classifier based on the image feature extraction results
Summary
With the rapid development of the national economy and the improvement of the living standards of the human beings, consumers put forward higher and higher requirements on food quality. Multi-resolution analysis related methods[12,13], such as Wavelet Transform, Gabor filters, fractal pattern analysis, have progressively attracted extensive interest in rice image analysis and perception for rice quality inspection Almost all of these methods attempt to extract statistics in a variety of different scales or bands to characterize the grain images for content understanding. Since the proposed statistical parameters or empirical statistical models are short of actual physical sense perception mean, they are often difficult to effectively characterize the most essential characteristics of the complex grain images or particle images which are comprised of a large number of random local homogeneous particles or fragmentations It severely restricts the further application of industrial vision systems in rice-processing-quality monitoring. A LS-SVM classifier is established to automatically identify the rice quality
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