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In wiew of the problem that noise affects workpiece image segmentation and tracking, in this paper, an approach was proposed to address the problem with adaptive square transform. Firstly, the block of noise images was extracted from the noise image and the mean value of the block was subtracted. Then learning square transform with sparse level was fixed. Updating square transform level was done asthe sparse level of the next learning square transform. Finally iteratived learning square transform and sparse level update, and averaged at the denoising blocks’ locations in the image generate denoised image estimate. The experimental results show that the proposed method has better denoising performance. Compared with the K-SVD algorithm, the peak signal-to-noise ratio of the denoised image is about twice that of the K-SVD algorithm, and the denoising speed is 3.9 times that of the K-SVD algorithm. [ABSTRACT FROM AUTHOR]

Cloth patterns and complex pattern make difficult to detect defects in the manufacturing process. Thus,a learning-based Gabor filter in multi-style fabric defect detection is proposed. First, Gabor filters are used to filter multi-style cloth at different directions and scales and select the optimal filter scale. Then, the defect features are extracted by using digital image feature discriminant,the feature images are segmented, and a number of preferred feature information blocks are merged. Finally, the fused images are processed by binarized and morphological filtering. Experimental results show that the method performs effectively both in flaw detecting of multi-style cloth and in real-time performing. Compared with similar algorithms, the detection accuracy and time efficiency have a certain increase. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Xi'an Polytechnic University is the property of Editorial Department of Journal of Xi'an Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)