Improving Space Localization Properties of the Discrete Wavelet Transform

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

2016

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In this study, a new wavelet-based approach (system) to the detection of defects in grey-level texture images is presented. This new approach explores space localization properties of the discrete wavelet transform (DWT) and generates statistically-based parameterized defect detection criteria. The introduced system's parameter provides the user with a possibility to control the percentage of both the actually defect-free images detected as defective and/or the actually defective images detected as defect-free, in the class of texture images under investigation. The developed defect detection system was implemented using discrete Haar and Le Gall wavelet transforms. For the experimental part, samples of ceramic tiles, as well as glass samples, taken from real factory environment, were used.

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Section: Partitioning Of the Discrete Wavelet Spectrum Of An Imagementioning

confidence: 93%

Wavelet-based Defect Detection System for Grey-level Texture Images

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

2016

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“…The proposed scheme for localizing defects in the defective texture image X def of size NN (N = 2 n , n  N) comprises of four steps, namely: (1) parti-tioning the image X def into non-overlapping image blocks of size , (2) generating HT spectra for the latter blocks, with the use of a computational algorithm (Valantinas et al, 2013)  ; (4) applying the same defect detection "mechanism" to each image block as in the case of the whole test texture image.…”

Section: Localizing Defects In Defective Texture Imagesmentioning

confidence: 99%

The Use of Haar Wavelets in Detecting and Localizing Texture Defects

2016

Image Anal Stereol

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In this paper, a new Haar wavelet-based approach to the detection and localization of defects in grey-level texture images is presented. This new approach explores space localization properties of the discrete Haar wavelet transform (HT) and generates statistically-based parameterized texture defect detection criteria. The criteria provide the user with a possibility to control the percentage of both the actually defect-free images detected as defective and/or the actually defective images detected as defect-free, in the class of texture images under investigation. The experiment analyses samples of ceramic tiles, glass samples, as well as fabric scraps, taken from real factory environment.

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Haar Wavelet-Based Approach to Locating Defects in Texture Images

Communications in Computer and Information Science

2015