Textural Features Corresponding to Visual Perception

  title={Textural Features Corresponding to Visual Perception},
  author={Hideyuki Tamura and Shunji Mori and Takashi Yamawaki},
  journal={IEEE Transactions on Systems, Man, and Cybernetics},
Textural features corresponding to human visual perception are very useful for optimum feature selection and texture analyzer design. We approximated in computational form six basic textural features, namely, coarseness, contrast, directionality, line-likeness, regularity, and roughness. In comparison with psychological measurements for human subjects, the computational measures gave good correspondences in rank correlation of 16 typical texture patterns. Similarity measurements using these… 
Computational measures corresponding to perceptual textural features
A new method based on the autocovariance function to estimate quantitatively these features is shown and the correspondence between these computational measures and the psychological ones made by human subjects is shown using some psychometric method.
Textural features corresponding to textural properties
In comparison with human perceptual measurements, the computational measures have shown good correspondences in the rank ordering of ten natural textures, and the extent to which the measures approximate visual perception was investigated in the form of texture similarity measurements, which were encouraging.
Texural Properties
In comparison with human perceptual measurements, the computational measures had good correspondences in the rank ordering of ten natural textures and the results obtained were encouraging, though not as good as in the ranking ordering of the textures.
Autocovariance-based perceptual textural features corresponding to human visual perception
It has been shown that humans use some perceptual textural features such as coarseness, contrast and direction to distinguish between textured images or regions. The aim of this paper is to present a
Textural Features Corresponding to Human Visual Perception
It has been shown that humans use some perceptual tex­ tural features such as coarseness, contrast and direction to distinguish beMeen textured images or regions. The aim of this paper is to present
Perceptually-Based Functions for Coarseness Textural Feature Representation
A model that associates computational measures to human perception by learning an appropriate function is proposed, and different measures representative of coarseness are chosen and subjects assessments are collected and aggregated.
Textural properties corresponding to visual perception based on the correlation mechanism in the visual system
In this study, it is shown that the autocorrelation function (ACF) analysis provides useful measures for representing three salient perceptual properties of texture: contrast, coarseness, and regularity.
Perceptive visual texture classification and retrieval
The goal of this research is to provide a visual system that, starting from graphical cues representing relevant perceptual features of texture, interactively searches the most similar texture in the set of candidates in the corresponding texture space.
Perceptual Texture Space Improves Perceptual Consistency of Computational Features
The construction of a reliable perceptual texture space is demonstrated, which can be used as a yardstick for assessing the perceptual consistency of computational features and similarity measurements.
Suitability of Texture Analysis Methods for Perceptual Texture
Image texture analysis has been widely used as object recognition methods in the field of computer vision. Most features extracted from conventional texture analysis metrics, however, were found


Computer identification of textured visual scenes
The work deals with computer analysis of textured outdoor scenes involving grass, trees, water and clouds using a sheaf-theoretical model which formalizes the operation of pasting local structure into global structure over a region.
Edge and Curve Detection for Visual Scene Analysis
Simple sets of parallel operations are described which can be used to detect texture edges, "spots," and "streaks" in digitized pictures and it is shown that a composite output is constructed in which edges between differently textured regions are detected, and isolated objects are also detected, but the objects composing the textures are ignored.
Textural Boundary Analysis
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  • Mathematics
    IEEE Transactions on Computers
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A procedure is demonstrated for locating textural boundaries in the digital image representation of a natural scene by development of an edge operator capable of integrating multiple textural features into a single boundary determination.
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    IRE Trans. Inf. Theory
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The condition for discrimination was found to be based primarily on clusters or lines formed by proximate points of uniform brightness, and a similar rule of connectivity with hue replacing brightness was obtained by using varicolored dots of equal subjective brightness.
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The results obtained indicate that the SGLDM is the most powerful algorithm of the four considered, and that the GLDM is more powerful than the PSM.
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This work deals with computer analysis of textured surfaces with descriptions of textures formalized from natural language descriptions obtained from the directional and non-directional components of the Fourier transform power spectrum.
Region Extraction by Averaging and Thresholding
Regions in a picture that differ texturally from their surroundings can often be extracted by 1) applying a local operation to every point of the picture, 2) averaging the results, and 3)
A structural analyzer for a class of textures
Textural Features for Image Classification
These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Visual texture analysis
Differentiation between the coarsenesses of samples of a given texture may be successfully effected using any of the following measures: (1) Amount of edge per unit area, (2) Self-match (as measured