Textural Features Corresponding to Visual Perception

Tokuda, Hideyuki; Mori, Shunji; Yamawaki, T.

doi:10.1109/tsmc.1978.4309999

Cited by 1,872 publications

(991 citation statements)

References 23 publications

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“…• Tamura [15] descriptors: Contrast, Coarseness and Directionality properties from the Tamura set of features. Output is a 3-dimensional feature vector.…”

Section: Texture Descriptorsmentioning

confidence: 99%

An Automated Visual Inspection System for the Classification of the Phases of Ti-6Al-4V Titanium Alloy

Ducato

Fratini

Cascia

et al. 2013

Computer Analysis of Images and Patterns

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Abstract. Metallography is the science of studying the physical properties of metal microstructures, by means of microscopes. While traditional approaches involve the direct observation of the acquired images by human experts, Computer Vision techniques may help experts in the analysis of the inspected materials. In this paper we present an automated system to classify the phases of a Titanium alloy, Ti-6Al-4V. Our system has been tested to analyze the final products of a Friction Stir Welding process, to study the states of the microstructures of the welded material.

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“…• Tamura [15] descriptors: Contrast, Coarseness and Directionality properties from the Tamura set of features. Output is a 3-dimensional feature vector.…”

Section: Texture Descriptorsmentioning

confidence: 99%

An Automated Visual Inspection System for the Classification of the Phases of Ti-6Al-4V Titanium Alloy

Ducato

Fratini

Cascia

et al. 2013

Computer Analysis of Images and Patterns

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“…Each set of stripes is then sampled into a 3-bin histogram, providing 4×4×3=48 image descriptors. (Tamura, Mori & Yamavaki, 1978) of contrast, directionality and coarseness, such that the coarseness descriptors are its sum and its 3-bin histogram, providing 1+1+1+3=6 image descriptors. (Prewitt, 1970), and include the mean, median, variance, and 8-bin histogram of both the magnitude and the direction components.…”

Section: Firstmentioning

confidence: 99%

Evaluation of Face Datasets as Tools for Assessing the Performance of Face Recognition Methods

Shamir

2008

Int J Comput Vis

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Face datasets are considered a primary tool for evaluating the efficacy of face recognition methods.Here we show that in many of the commonly used face datasets, face images can be recognized accurately at a rate significantly higher than random even when no face, hair or clothes features appear in the image. The experiments were done by cutting a small background area from each face image, so that each face dataset provided a new image dataset which included only seemingly blank images. Then, an image classification method was used in order to check the classification accuracy. Experimental results show that the classification accuracy ranged between 13.5% (color FERET) to 99% (YaleB). These results indicate that the performance of face recognition methods measured using face image datasets may be biased. Compilable source code used for this experiment is freely available for download via the internet.

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“…This error can also be viewed as a goodness measurement of the measures used in F . [9] 0.1891 Amadasun Correlation 0.1354 Tamura [2] 0.1994 Abbadeni Tamura 0.1373 Edge density (ED) [3] 0.2044 Amadasun ED 0.1431 DGD [10] 0.2120 Correlation Abbadeni 0.1433 Local Homogeneity [3] 0. Unidimensional fitting.…”

Section: Fitting the Functionmentioning

confidence: 99%

“…It is usual to describe visual textures according to some properties like coarseness-fineness, orientation or regularity [1,2]. From all of them, the coarseness-fineness is one of the most popular, being common to associate the presence of texture with the presence of fineness.…”

Section: Introductionmentioning

confidence: 99%

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Texture Measuring by Means of Perceptually-Based Fineness Functions

Chamorro-Martı́nez

Martínez-Jiménez

2009

Pattern Recognition and Image Analysis

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Abstract. Although texture is a widely analyzed property in computer vision, models which relate this feature with human perception are needed. In this paper, a model that associates computational fineness measures with the human perception of this type of texture property is proposed. The fineness perception is collected from human subjects, performing an aggregation of their assessments. Considering a wide variety of measures, a coarseness-fineness function relating these features with the human assesments is obtained as the function which provides the best fit of the collected data.

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Textural Features Corresponding to Visual Perception

Cited by 1,872 publications

References 23 publications

An Automated Visual Inspection System for the Classification of the Phases of Ti-6Al-4V Titanium Alloy

An Automated Visual Inspection System for the Classification of the Phases of Ti-6Al-4V Titanium Alloy

Evaluation of Face Datasets as Tools for Assessing the Performance of Face Recognition Methods

Texture Measuring by Means of Perceptually-Based Fineness Functions

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