Textural Features Corresponding to Visual Perception

By Hideyuki Tamura, Shunji Mori, Takashi Yamawaki. 1978

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Describing Textures in the Wild (2013, background): “The study of perceptual attributes of textures has a long history starting from pre-attentive aspects and grouping [16], to coarse high-level attributes [1, 2, 33], to some recent work aimed at discovering such attributes by automatically mining descriptions of images from the Internet [3, 12].”
Multi-class texture analysis in colorectal cancer histology (2016, methodology): “Tamura et al. 7 showed that the human visual system discriminates texture through several specific attributes that were later on refined and tested by Bianconi et al. 8 .”
Multimedia content analysis-using both audio and visual clues (2000, unknown): “Tamura representation is motivated by the psychological studies in human visual perception of texture and includes measures of coarseness, contrast, directionality, linelikeness, regularity, and roughness [20].”
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope (2001, background): “Several studies have proposed perceptual properties for representing texture images (Amadasun and King, 1989; Rao and Lohse, 1993; Tamura et al., 1978; Heaps and Handel, 1999).”
Directional multiresolution image representations (2002, background): “Perceptual studies [162, 136] identified texture dimensions by conducting experiments that asked observers to group textures according to perceived similarity.”
Content-Based Image Retrieval Systems (2001, methodology): “Other often-used texture measurements are (1) Tamura features [21].”
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance (2002, background): “Perceptual studies [ 12 ], [13] identified texture dimensions by conducting experiments that asked observers to group textures according to perceived similarity.”
Texture Analysis (1993, background): “” [2] • “A region in an image has a constant texture if a set of local statistics or other l Chapter 2.”
Content-based Image Retrieval Using Gabor Texture Features (2000, unknown): “…aylwin.wong, maria.indrawan, guojun.lu}@infotech.monash.edu.au Outline • Problems • Gabor Filters • Experiment • Gabor Texture Features • Rotation Invariance • Retrieval Test • Conclusions, Issues and Future Work Problems I—Image retrieval a. Object (shape) b. Color (histogram) c. Texture ?”
Discrimination between tumour epithelium and stroma via perception-based features (2015, background): “the concept of ‘contrast’ depends on the distribution (histogram) of grey-levels, the sharpness of edges and the period of repeating patterns [15].”
CP-CHARM: segmentation-free image classification made accessible (2016, background): “category contains some well-known texture descriptors such as the Haralick [2] and the Tamura [3] texture features.”
Features for image retrieval: an experimental comparison (2008, methodology): “On one hand, the presented descriptors were selected such that features presented many years ago, such as color histograms [54], Tamura texture features [56], Gabor features, and spatial autocorrelation features [58], as well as very recent features such as SIFT descriptors [44] and patches [40] wer”
Support vector machine active learning for image retrieval (2001, background): “Studies [22, 32, 33, 21] have shown that characterizing texture features in terms of structuredness, orientation, and scale (coarseness) ts well with models of human perception.”