Texture discrimination by Gabor functions

By M. J. Turner. 1986

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Citations in the corpus

Multi-class texture analysis in colorectal cancer histology (2016, methodology): “Finally, we also tested Gabor filters, another common texture measure based on the response of a set of orientation-and frequency-selective filters 28,36 .”
CellProfiler: image analysis software for identifying and quantifying cell phenotypes (2006, background): “This includes many standard features [39,40], but also complex measurements like Zernike shape features [41], and Haralick and Gabor texture features [42-44], which are described in detail in the online help and manual.”
Vehicle Detection Techniques for Collision Avoidance Systems: A Review (2015, background): “Gabor features had been known for texture analysis of images [155]–[158] as they capture local lines and edges information at different orientations and scales.”
A Galaxy of Texture Features (2008, background): “(23) Power spectrum (− − −) The power spectrum depicts the energy distribution in the frequency domain.”
Rotation-invariant and scale-invariant Gabor features for texture image retrieval (2007, methodology): “As a relaxed wavelet transformation [10], the Gabor filter family essentially performs a multi-channel representation, which is in line with the multichannel filtering mechanism of the HVS in perceiving visual information [11–13].”
Contour detection based on nonclassical receptive field inhibition (2003, methodology): “compression [35], motion analysis [36], image enhancement [37], face recognition [38], person identification based on iris pattern analysis [39], texture analysis [40]–[45], retrieval from image databases [46]–[48], and edge detection [49]–[53].”
A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques (2008, unknown): “Turner [135] and Bovik et al.”
Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY (2020, background): “Turner [87] and Clark et al. [88] ﬁrst proposed the use of Gabor ﬁlters (GF) in texture analysis.”
From BoW to CNN: Two Decades of Texture Representation for Texture Classification (2018, methodology): “One pioneering approach was that of Laws [108], where a bank of separable ﬁlters was applied, with subsequent ﬁltering methods including Gabor ﬁlters [20, 89, 225], Gabor wavelets [148], wavelet pyramids [61, 144], and simple linear ﬁlters like Differences of Gaussians [142].”
Visual to Text: Survey of Image and Video Captioning (2019, background): “Popular features include a large number of visual descriptors such as histogram of gradients [4], SIFT [5], SURF [6], Gabor textures [7], etc.”