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Article

Facundo Pieniazek, Agustina Roa Andino and Valeria Messina

Measuring texture parameters are time consuming and expensive; it is necessary to develop an efficient and rapid method to evaluate them. Image analysis can be a useful…

Abstract

Purpose

Measuring texture parameters are time consuming and expensive; it is necessary to develop an efficient and rapid method to evaluate them. Image analysis can be a useful tool. The purpose of this paper is to predict texture parameters in different beef cuts applying image analysis techniques.

Design/methodology/approach

Samples were analyzed by scanning electron microscopy. Texture parameters were analyzed by instrumental, image analysis techniques and by Warner–Bratzler shear force.

Findings

Significant differences (p<0.05) were obtained for image and instrumental texture features. Higher amount of porous were observed in freeze dried samples of beef cuts from Gluteus Medius and semintendinosus muscles. A linear trend with a linear correlation was applied for instrumental and image texture. High correlations were found between image and instrumental texture features. Instrumental parameters showed a positive correlation with image texture feature.

Originality/value

This research suggests that the addition of image texture features improves the accuracy to predict texture parameter. The prediction of quality parameters can be performed easily with a computer by recognizing attributes within an image.

Details

British Food Journal, vol. 120 no. 8
Type: Research Article
ISSN: 0007-070X

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Article

Neda Tadi Bani and Shervan Fekri-Ershad

Large amount of data are stored in image format. Image retrieval from bulk databases has become a hot research topic. An alternative method for efficient image retrieval…

Abstract

Purpose

Large amount of data are stored in image format. Image retrieval from bulk databases has become a hot research topic. An alternative method for efficient image retrieval is proposed based on a combination of texture and colour information. The main purpose of this paper is to propose a new content based image retrieval approach using combination of color and texture information in spatial and transform domains jointly.

Design/methodology/approach

Various methods are provided for image retrieval, which try to extract the image contents based on texture, colour and shape. The proposed image retrieval method extracts global and local texture and colour information in two spatial and frequency domains. In this way, image is filtered by Gaussian filter, then co-occurrence matrices are made in different directions and the statistical features are extracted. The purpose of this phase is to extract noise-resistant local textures. Then the quantised histogram is produced to extract global colour information in the spatial domain. Also, Gabor filter banks are used to extract local texture features in the frequency domain. After concatenating the extracted features and using the normalised Euclidean criterion, retrieval is performed.

Findings

The performance of the proposed method is evaluated based on the precision, recall and run time measures on the Simplicity database. It is compared with many efficient methods of this field. The comparison results showed that the proposed method provides higher precision than many existing methods.

Originality/value

The comparison results showed that the proposed method provides higher precision than many existing methods. Rotation invariant, scale invariant and low sensitivity to noise are some advantages of the proposed method. The run time of the proposed method is within the usual time frame of algorithms in this domain, which indicates that the proposed method can be used online.

Details

The Electronic Library , vol. 37 no. 4
Type: Research Article
ISSN: 0264-0473

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Article

Qiyin Lin, Zhengying Wei, Ning Wang and Wei Chen

The purpose of this paper is to study the influence of large-area texture/slip surface, especially the area and position of large-area texture surface on journal bearing…

Abstract

Purpose

The purpose of this paper is to study the influence of large-area texture/slip surface, especially the area and position of large-area texture surface on journal bearing, and improve the tribological performances of journal bearing.

Design/methodology/approach

A modified texture/slip numerical boundary condition with double parameters is presented and is applied onto the region where surface textures locate to represent the impact of actual texture/slip surface. A phase change condition is used to analyze cavitation phenomena.

Findings

The global/cumulative texture effect can be represented by applying texture/slip condition onto the region where it locates. The area and position of texture/slip surface would significantly affect the cavitation and load-carrying capacity. Texture/slip surface would not affect the pressure and load-carrying capacity when it locates at cavitation zone. The effect of texture/slip surface on load-carrying capacity would be beneficial if it locates at the pressure rise region, but its effect would be adverse if it locates at the pressure drop region. Well-designed texture/slip surface can improve tribological performances.

Originality/value

The developed texture/slip boundary condition can be a suitable and useful tool to analyze the effect of large-area texture/slip surface and especially to optimize the area and position of large-area texture surface. This approach can be complementary to conventional approach which is used to analyze the influence of textures’ real configurations and parameters.

Details

Industrial Lubrication and Tribology, vol. 67 no. 3
Type: Research Article
ISSN: 0036-8792

Keywords

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Article

Manuel Ferreira, Cristina Santos and Joao Monteiro

The purpose of this paper is to propose a set of techniques, in the domain of texture analysis, dedicated to the classification of industrial textures. One of the main…

Abstract

Purpose

The purpose of this paper is to propose a set of techniques, in the domain of texture analysis, dedicated to the classification of industrial textures. One of the main purposes was to deal with a high diversity of textures, including structural and highly random patterns.

Design/methodology/approach

The global system includes a texture segmentation phase and a classification phase. The approach for image texture segmentation is based on features extracted from wavelets transform, fuzzy spectrum and interaction maps. The classification architecture uses a fuzzy grammar inference system.

Findings

The classifier uses the aggregation of features from the several segmentation techniques, resulting in high flexibility concerning the diversity of industrial textures. The resulted system allows on‐line learning of new textures. This approach avoids the need for a global re‐learning of the all textures each time a new texture is presented to the system.

Practical implications

These achievements demonstrate the practical value of the system, as it can be applied to different industrial sectors for quality control operations.

Originality/value

The global approach was integrated in a cork vision system, leading to an industrial prototype that has already been tested. Similarly, it was tested in a textile machine, for a specific fabric inspection, and gave results that corroborate the diversity of possible applications. The segmentation procedure reveals good performance that is indicated by high classification rates, revealing good perspectives for full industrialization.

Details

Sensor Review, vol. 29 no. 2
Type: Research Article
ISSN: 0260-2288

Keywords

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Article

Feng Ye, Di Li, Jie‐xian Huang and Zhi‐jie Dong

The purpose of this paper is to study the application of advanced computer image processing techniques for flaw detection on flexible printed circuit (FPC) solder.

Abstract

Purpose

The purpose of this paper is to study the application of advanced computer image processing techniques for flaw detection on flexible printed circuit (FPC) solder.

Design/methodology/approach

Texture directionality feature is obtained based on texture gradient, contour's position is extracted and directionality information obtained through analyzing the distribution of directionality. Contour similarity function is established to filter out false contour and keep proper contour, and the solder's location work is accomplished based on reversed contour. After that, a combination of grey and texture gradient's value deviation from reference value is utilized to reflect and describe texture on the solder's surface. Flaw can be distinguished from homogeneous texture background.

Findings

The method has been applied to the inspecting system and achieved a higher accuracy and a lower false defect rate. It demonstrates that the method can detect flaws efficiently and effectively.

Research limitations/implications

Although the work on FPC solder's location and flaw detection is presented, defective classification is not involved that is also very important content for inspection.

Originality/value

The paper provides a new way to locate solder based on directionality. The method not only extracts contour feature but also gains directional parameters to help realize accurate location, especially for some solders that are deformed to some extent. Entropy statistic based on distribution of grey and texture gradient is proposed to describe and measure solder's surface texture. The new algorithm performs stably and efficiently and is fit for practical application.

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Article

Babar Khan, Fang Han, Zhijie Wang and Rana J. Masood

This paper aims to propose a biologically inspired processing architecture to recognize and classify fabrics with respect to the weave pattern (fabric texture) and yarn…

Abstract

Purpose

This paper aims to propose a biologically inspired processing architecture to recognize and classify fabrics with respect to the weave pattern (fabric texture) and yarn color (fabric color).

Design/methodology/approach

By using the fabric weave patterns image identification system, this study analyzed the fabric image based on the Hierarchical-MAX (HMAX) model of computer vision, to extract feature values related to texture of fabric. Red Green Blue (RGB) color descriptor based on opponent color channels simulating the single opponent and double opponent neuronal function of the brain is incorporated in to the texture descriptor to extract yarn color feature values. Finally, support vector machine classifier is used to train and test the algorithm.

Findings

This two-stage processing architecture can be used to construct a system based on computer vision to recognize fabric texture and to increase the system reliability and accuracy. Using this method, the stability and fault tolerance (invariance) was improved.

Originality/value

Traditionally, fabric texture recognition is performed manually by visual inspection. Recent studies have proposed automatic fabric texture identification based on computer vision. In the identification process, the fabric weave patterns are recognized by the warp and weft floats. However, due to the optical environments and the appearance differences of fabric and yarn, the stability and fault tolerance (invariance) of the computer vision method are yet to be improved. By using our method, the stability and fault tolerance (invariance) was improved.

Details

Assembly Automation, vol. 36 no. 2
Type: Research Article
ISSN: 0144-5154

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Article

Shaw‐Jyh Shin, I‐Shou Tsai and Po‐Dong Lee

Reports how the theorem of the texture “tuned” mask was modified to solve some problems encountered in the automatic faults (including filling bars, oil stains…

Abstract

Reports how the theorem of the texture “tuned” mask was modified to solve some problems encountered in the automatic faults (including filling bars, oil stains, weft‐lacking and holes) detection and recognition of the plain woven fabrics. These problems are the faults of variable shapes and sizes, those of variable structure and the grey‐level differences in the faults of oil stains. The index of the “tuned” mask in the texture “tuned” mask theorem was modified to converge the variability of the faults, and to elongate the distances between each fault’s average texture energy so that the texture energy in normal texture and in faults can be confined to different fixed ranges. The results show that the optimum texture “tuned” mask found from the modified theorem of the texture “tuned” mask can be used satisfactorily to identify different faults due to structure, shapes and size variation. However, in the case of undertoned oil stains and lower density filling bars, this method may sometimes cause misidentification.

Details

International Journal of Clothing Science and Technology, vol. 8 no. 1/2
Type: Research Article
ISSN: 0955-6222

Keywords

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Article

Elza Maria Meinert, Luiz Henrique Beirão and Evanilda Teixeira

Restructured shrimp made with three different formulations were evaluated using subjective and objective texture analysis. The three formulations showed statistical…

Abstract

Restructured shrimp made with three different formulations were evaluated using subjective and objective texture analysis. The three formulations showed statistical similarities with breaded whole shrimp in terms of gumminess and oily cover in mouth, and differed in relation to firmness, elasticity, cohesivity, adhesivity, moisture release, stickiness in mouth and overall texture impression. The objective texture evaluation showed significant differences in respect to cohesivity, adhesivity and gumminess between breaded whole shrimp and the three formulae, and in reference to hardness, between formulations.

Details

British Food Journal, vol. 101 no. 11
Type: Research Article
ISSN: 0007-070X

Keywords

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Article

M.L. Smith, A.R. Farooq, L.N. Smith and P.S. Midha

The paper presents a new approach to texture analysis. The need for a more formal definition of the term surface texture is first identified, and an appropriate texture

Abstract

The paper presents a new approach to texture analysis. The need for a more formal definition of the term surface texture is first identified, and an appropriate texture taxonomy proposed. A method of analysis is described, synthesising innovative elements of machine vision and computer graphics to achieve an object‐centred inspection technique, which is both robust and flexible in application. A selection of experimental results is presented in the paper.

Details

Sensor Review, vol. 20 no. 4
Type: Research Article
ISSN: 0260-2288

Keywords

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Article

Yih‐Chih Chiou, Chern‐Sheng Lin and Guan‐Zi Chen

The purpose of this paper is to present an automatic inspection method of colors and textures classification of paper and cloth objects.

Abstract

Purpose

The purpose of this paper is to present an automatic inspection method of colors and textures classification of paper and cloth objects.

Design/methodology/approach

In this system, the color image is transformed from RGB model to other suitable color model with one of the components being chosen as the gray‐level image for extracting textures. The gray‐level image is decomposed into four child images using wavelet transformation. Two child images capable of detecting variations along columns and rows are used to generate 0° and 90° co‐occurrence matrices, respectively. Some of the distinguishable texture features are derived from the two co‐occurrence matrixes. Finally, the test image is classified using neural networks. Nine color papers and eight color cloths are used to test the developed classification method.

Findings

The results show that recognition rate higher than 97.86 percent can be achieved if color and texture features are both used as the inputs to the networks.

Originality/value

The paper presents a new approach for testing materials. The multipurpose measurement application with unsophisticated and economical equipment can be confirmed in online inspection of papers and cloth manufacturing.

Details

Sensor Review, vol. 29 no. 3
Type: Research Article
ISSN: 0260-2288

Keywords

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