Texture and Colour in Image Analysis

© 2022 by the authors; CC BY-NC-ND license

Machine vision; image analysis; item counting device; electro-deposition industry; digital intraoral radiography; image preprocessing; periapical lesions; texture analysis; prostate cancer; histopathology; microscopic; tissue image; segmentation; morphological; quantitative; classification; SVM; image resizing; local Tchebichef moments (LTM); scaling; scale-and-stretch; seam carving; faster R-CNN; cutting pieces; multi-period pattern; skew angle; period length; colored texture pattern classification; global–local texture classification; color–texture features; color–texture feature extraction; bagging post-processing; BQMP and Haralick global–local feature integration; maceral components; image segmentation; coal petrography; random forest; two-level clustering; deep neural networks; adaptive gradient methods; stochastic gradient descent; bounded scheduling method; image classification; language modeling; texture; segmentation; deep learning; MB-LBP; surface defect detection; feature extraction; defect recognition; mammogram; meta-heuristics; optimization; breast cancer; segmentation; detection; texture; skin microrelief; water sorption; aging; hair; mathematics of colour and texture; hand-designed image descriptors; rank features; partial orders; river scene segmentation; local binary pattern; hue variance; surface reflection; hand-designed image descriptors; audio classification; dissimilarity space; siamese network; ensemble of classifiers; pattern recognition; animal audio; co-saliency; omnidirectional images; video saliency; visual saliency estimation; n/a