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The traditional methods have difficulty to inspection various types of copper strips defects as inclusions, pits and delamination defects under uneven illumination. Therefore, this paper aims to propose an optimal real Gabor filter model for inspection; however, improper selection of Gabor parameters will cause the boundary between the defect and the background image to be not very clear. This will make the defect and the background cannot be completely separated.

The authors proposed an optimal Real Gabor filter model for inspection of copper surface defects under uneven illumination. This proposed method only requires a single filter by calculating the specific convolution energy of the Gabor filter with the image. The Real Gabor filter’s parameter is optimized by particle swarm optimization (PSO), which objective fitness function is maximization of the Gabor filter’s energy average divided by the energy standard deviation, the objective makes a distinction between the defect and normal area.

The authors have verified the effect with different iterations of parameter optimization using PSO, the effects with different control constant of energy and neighborhood window size of real Gabor filter, the experimental results on a number of metal surface have shown the proposed method achieved a well performance in defect recognition of metal surface.

The authors propose a defect detection method based on particle swarm optimization for single Gabor filter parameters optimization. This proposed method only requires a single filter and finds the best parameters of the Gabor filter. By calculating the specific convolution energy of the Gabor filter and the image, to obtain the best Gabor filter parameters and to highlight the defects, the particle swarm optimization algorithm’s fitness objective function is maximize the Gabor filter's average energy divided by the energy standard deviation.

Automatic slub detection is vital in the classification and identification of fabric images. This paper seeks to present a rapid and accurate approach for automatic detection of slub in fabric images using Gabor filters.

Slub can be regarded as defects along weft or warp. Gabor filters as bandpass filters consider the directional characteristics of slub and its frequency spectrum after Fourier transform. Choosing appropriate parameters for Gabor filters, slub can be detected accurately.

The proposed method achieves automatic detection of slub. The experimental results suggest that the authors approach is effective.

This paper considers appropriate parameters to design a Gabor filter for automatic detection of slub. And it is helpful to classify and identify fabric images.

The purpose of this paper is to review and provide a detailed performance evaluation of a number of texture descriptors that analyse texture at micro‐level such as local binary patterns (LBP) and a number of standard filtering techniques that sample the texture information using either a bank of isotropic filters or Gabor filters.

The experimental tests were conducted on standard databases where the classification results are obtained for single and multiple texture orientations. The authors also analysed the performance of standard filtering texture analysis techniques (such as those based of LM and MR8 filter banks) when applied to the classification of texture images contained in standard Outex and Brodatz databases.

The most important finding resulting from this study is that although the LBP/C and the multi‐channel Gabor filtering techniques approach texture analysis from a different theoretical perspective, in this paper the authors have experimentally demonstrated that they share some common properties in regard to the way they sample the macro and micro properties of the texture.

Texture is a fundamental property of digital images and the development of robust image descriptors plays a crucial role in the process of image segmentation and scene understanding.

This paper contrast, from a practical and theoretical standpoint, the LBP and representative multi‐channel texture analysis approaches and a substantial number of experimental results were provided to evaluate their performance when applied to standard texture databases.

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.

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.

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.

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.

The purpose of this paper is to improve the retrieval results of hyperspectral image by integrating both spectral and textural features. For this purpose, an improved multiscale opponent representation for hyperspectral texture is proposed to represent the spatial information of the hyperspectral scene.

In the presented approach, end-member signatures are extracted as spectral features by means of the widely used end-member induction algorithm N-FINDR, and the improved multiscale opponent representation is extracted from the first three principal components of the hyperspectral data based on Gabor filters. Then, the combination similarity between query image and other images in the database is calculated, and the first k more similar images are returned in descending order of the combination similarity.

Some experiments are calculated using the airborne hyperspectral data of Washington DC Mall. According to the experimental results, the proposed method improves the retrieval results, especially for image categories that have regular textural structures.

The paper presents an effective retrieval method for hyperspectral images.

Describes experimental work in using machine vision for inspection and grading in the poultry industry. Outlines the main features of human poultry inspection and grading by size, colour and conditions and describes how neural networks can detect various defects in the poultry [such as bruising, cuts and broken bones]. Explains the development of neural nets, the use of processing elements and HSI histograms for image analysis. Describes texture analysis and the use of two dimensional Gabor filters. Concludes that neural nets appear to be a good tool for detecting poultry defects but further work is needed to develop more specific input.

The purpose of this paper is to show an efficient method for the detection of signs of early lung cancer. Various image processing algorithms are presented for different types of lesions, and a scheme is proposed for the combination of results.

A computer aided detection (CAD) scheme was developed for detection of lung cancer. It enables different lesion enhancer algorithms, sensitive to specific lesion subtypes, to be used simultaneously. Three image processing algorithms are presented for the detection of small nodules, large ones, and infiltrated areas. The outputs are merged, the false detection rate is reduced with four separated support vector machine (SVM) classifiers. The classifier input comes from a feature selection algorithm selecting from various textural and geometric features. A total of 761 images were used for testing, including the database of the Japanese Society of Radiological Technology (JSRT).

The fusion of algorithms reduced false positives on average by 0.6 per image, while the sensitivity remained 80 per cent. On the JSRT database the system managed to find 60.2 per cent of lesions at an average of 2.0 false positives per image. The effect of using different result evaluation criteria was tested and a difference as high as 4 percentage points in sensitivity was measured. The system was compared to other published methods.

The study described in the paper proves the usefulness of lesion enhancement decomposition, while proposing a scheme for the fusion of algorithms. Furthermore, a new algorithm is introduced for the detection of infiltrated areas, possible signs of lung cancer, neglected by previous solutions.

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.

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.

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.

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.

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.

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).

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.

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.

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.

The purpose of this paper is to propose a new inspecting algorithm for defect detection on PCB circuits.

PCB circuit images were processed by a radon transformation. A Radon histogram was formed and utilized to establish a texture directional characteristic similarity function. Then, a region of the image which contained the same texture directionality feature was segmented. Furthermore, a directionality estimation method is presented. As the circuit was damaged, the directionality was weakened correspondingly. According to principle, the concept of directional intensity was proposed and then used to measure directionality through analysis of the Radon histogram fluctuation. Finally, the defect was detected based on directional intensity.

The method has been applied to an inspecting system used in practice and it achieved a higher accuracy and efficiency in comparison with similar methods.

Although work on highly intensive PCB circuitry inspection and flaw detection is presented, defect classification was not involved although this is also a very important requirement of inspection.

The paper provides a new way to detect PCB circuitry defects based on texture directionality and proposes evaluating the similarity between image texture directionalities using a radon transformation to search the inspected area. As the inspected region was located, the concept of directional intensity was defined to measure texture directionality to identify defects. The new algorithm performs stably and efficiently and is fit for practical application.