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Texture analysis based on the extraction of contrast features is very effective in terms of both computational complexity and discrimination capability. In this framework, max–min approaches have been proposed in the past as a simple and powerful tool to characterize a statistical texture. In the present work, a method is proposed that allows exploiting the potential of max–min approaches to efficiently solve the problem of detecting local alterations in a uniform statistical texture. Experimental results show a high defect discrimination capability, and a good attitude to real-time applications, which make it particularly attractive for the development of industrial visual inspection systems.

We introduce a new second-order method of texture analysis called Adaptive Multi-Scale Grey Level Co-occurrence Matrix (AMSGLCM), based on the well-known Grey Level Co-occurrence Matrix (GLCM) method. The method deviates significantly from GLCM in that features are extracted, not via a fixed 2D weighting function of co-occurrence matrix elements, but by a variable summation of matrix elements in 3D localized neighborhoods. We subsequently present a new methodology for extracting optimized, highly discriminant features from these localized areas using adaptive Gaussian weighting functions. Genetic Algorithm (GA) optimization is used to produce a set of features whose classification "worth" is evaluated by discriminatory power and feature correlation considerations. We critically appraised the performance of our method and GLCM in pairwise classification of images from visually similar texture classes, captured from Markov Random Field (MRF) synthesized, natural, and biological origins. In these cross-validated classification trials, our method demonstrated significant benefits over GLCM, including increased feature discriminatory power, automatic feature adaptability, and significantly improved classification performance.

This paper presents a novel, fast algorithm for accurate detection of the shape of targets around a mobile robot using a single rotating sonar element. The rotating sonar yields an image built up by the reflections of an ultrasonic beam directed at different scan angles. The image is then interpreted with an image-understanding approach based on texture analysis. Several important tasks are performed in this way, such as noise removal, echo correction and restoration. All these processes are obtained by estimating and restoring the degree of texture continuity. Texture analysis, in fact, allows us to look at the image on a large scale thus giving the possibility to infer the overall behavior of the reflection process. The algorithm has been integrated in a mobile robot. However, the algorithm is not suitable for working during the mobile robot movement, rather it can be used during the period when the robot stays in a fixed position.

Texture is an important visual attribute used to describe the pixel organization in an image. As well as it being easily identified by humans, its analysis process demands a high level of sophistication and computer complexity. This paper presents a novel approach for texture analysis, based on analyzing the complexity of the surface generated from a texture, in order to describe and characterize it. The proposed method produces a texture signature which is able to efficiently characterize different texture classes. The paper also illustrates a novel method performance on an experiment using texture images of leaves. Leaf identification is a difficult and complex task due to the nature of plants, which presents a huge pattern variation. The high classification rate yielded shows the potential of the method, improving on traditional texture techniques, such as Gabor filters and Fourier analysis.

Texture analysis plays a vital role in image processing. The prospect of texture based image analysis depends on the texture features and the texture model. This paper presents a new texture feature extraction method 'Fuzzy Local Texture Patterns (FLTP)' and 'Fuzzy Pattern Spectrum (FPS)', suitable for texture analysis. The local image texture is described by FLTP and the global image texture is described by FPS. The proposed method is tested with texture classification, texture segmentation and texture edge detection. The results show that the proposed method provides a very good and robust performance for texture analysis.

Agriculture robot by mechanical harvesting requires automatic detection and counting of fruits in tree canopy. Because of color similarity, shape irregularity, and background complex, fruit identification turns to be a very difficult task and not to mention to execute pick action. Therefore, green cucumber detection within complex background is a challenging task due to all the above-mentioned problems. In this paper, a technique based on texture analysis and color analysis is proposed for detecting cucumber in greenhouse. RGB image was converted to gray-scale image and HSI image to perform algorithm, respectively. Color analysis was carried out in the first stage to remove background, such as soil, branches, and sky, while keeping green fruit pixels presented cucumbers and leaves as many as possible. In parallel, MSER and HOG were applied to texture analysis in gray-scale image. We can obtain some candidate regions by MSER to obtain the candidate including cucumber. The support vector machine is the classifier used for the identification task. In order to further remove false positives, key points were detected by a SIFT algorithm. Then, the results of color analysis and texture analysis were merged to get candidate cucumber regions. In the last stage, the mathematical morphology operation was applied to get complete cucumber.

Early diagnosis of osteoporosis can efficiently predict fracture risk. There is a great demand to prevent this disease. The goal of this study was to distinguish osteoporotic cases from healthy controls on 2D bone radiograph images, using texture analysis and genetic algorithms (GAs). Gray Level Co-occurrence Matrix (GLCM), Run length Matrix (RLM) and Binarized Statistical Image Features (BSIF) were used for texture analysis. Features are numerous and parameter-dependent. The related experts can pick out the useful input features for the classifier. It however remains a difficult task and may be inefficient or even harmful as the data pattern is not clear. In this paper, GAs were used to optimize the two parameters of the co-occurrence matrix (distance parameter or pixel separation, orientation or direction) and the number of gray levels used in the preprocessing quantification step. GAs were also used to select the best combination of features extracted from GLCM and RLM matrices. Experiments were conducted on two populations composed of Osteoporotic Patients and Control Subjects. Results show that GAs combined with GLCM and BSIF features can improve the classification rates (ACC = 87.50%) obtained using GLCM (ACC = 77.8%) alone.

Mammography associated with clinical breast examination and breast self-examination is the only effective and viable method for mass breast screening. Most of the minimal breast cancers are detected by the presence of microcalcifications. It is however difficult to distinguish between benign and malignant microcalcifications associated with breast cancer. Most of the techniques used in the computerized analysis of mammographic microcalcifications segment the digitized grey-level image into regions representing microcalcifications. Since mammographic images usually suffer from poorly defined microcalcification features, the extraction of microcalcification features based on segmentation process is not reliable and accurate. We present a second-order grey-level histogram based feature extraction approach which does not require the segmentation of microcalcifications into binary regions to extract features to be used in classification. The image structure features, computed from the second-order grey-level histogram statistics, are used for classification of microcalcifications. Several image structure features were computed for 100 cases of “difficult to diagnose” microcalcification cases with known biopsy results. These features were analyzed in a correlation study which provided a set of five best image structure features. A feedforward backpropagation neural network was used to classify mammographic microcalcifications using the image structure features. Four networks were trained for different combinations of training and test cases, and number of nodes in hidden layers. False Positive (FP) and True Positive (TP) rates for microcalcification classification were computed to compare the performance of the trained networks. The results of the neural network based classification were compared with those obtained using multivariate Baye’s classifiers, and the k-nearest neighbor classifier. The neural network yielded good results for classification of “difficult-to-diagnose” micro-calcifications into benign and malignant categories using the selected image structure features.

Texture analysis has many areas of potential application in industry. The problem of determining composition of grain mixtures by texture analysis was recently studied by Kjell. He obtained promising results when using all nine Laws' 3 × 3 features simultaneously and an ordinary feature vector classifier. In this paper the performance of texture classification based on feature distributions in this problem is evaluated. The results obtained are compared to those obtained with a feature vector classifier. The use of distributions of gray level differences as texture measures is also considered.

Analysis of wear debris carried by a lubricant in an oil-wetted system provides important information about the condition of a machine. This paper describes the analysis of microscopic metal particles generated by wear using computer vision and image processing. The aim is to classify these particles according to their morphology and surface texture and by using the information obtained, to predict wear failure modes in engines and other machinery. This approach obviates the need for specialists and reliance on human visual inspection techniques. The procedure reported in this paper, is used to classify surface features of the wear particles by using artificial neural networks. A visual comparison between cooccurrence matrices representing five different texture classes is described. Based on these comparisons, matrices of reduced sizes are utilized to train a feed-forward neural classifier in order to distinguish between the various texture classes.

The fractal dimension has been studied as a feature for texture analysis. It has been found that the fractal dimension is not an effective image texture measure but little is known about the reasons for the fractal dimension failing to be effective for texture analysis. This paper investigates into the underlying causes why the fractal dimension is not an effective image texture feature. Four mathematical properties have been identified which are responsible for the fractal dimension's ineffectiveness. The experimental results show that while the fractal dimension itself is hardly an effective feature for texture classification, it can considerably enhance other feature sets.

The estimation of the crystallite orientation distribution function based on the leading texture coefficients can be rephrased as a maximum entropy moment problem. In this paper, we prove the solvability of these moment problems under quite general assumptions on the moment functions which carries over to general locally compact and σ-compact Hausdorff topological groups.

The behavioral-biometrics methods of writer identification and verification have been considered as a research topic for many years. However, many writer identification and verification methods have been designed based on English handwriting properties, but because of many differences between English and Persian handwriting and the challenges facing Persian handwriting analysis, designing such methods has many interests in Persian yet. In this paper, we have presented a fully text-independent and texture based method for identifying writers of Persian handwritten documents. As a result of special properties of Persian handwriting, a modified version of Gabor filter that is called Extended Gabor (XGabor) filter has been used to extract the features. An MLP (Multi Layer Perceptron (Node)) neural network and a K-NN classifier have been employed to classify the extracted features. In the evaluation phase, an exhaustive database of Persian handwritten documents was prepared and the method applied on. The experimental results showed that the accuracy of proposed method is about 97% and it is competitive with others. We believe that the proposed method may be extended to identify writers in other languages by adjusting some parameters.

Fractal analysis was used in the study to determine a set of feature descriptors which could be applied in the process of diagnosing bone damage caused by osteoporosis. The subject of the research was CT images of vertebrae on the thoraco-lumbar region. The dataset contained images of healthy patients and patients diagnosed with osteoporosis. On the basis of fractal analysis and feature selection by linear stepwise regression, three descriptors were obtained. These were two fractal dimensions calculated by the variation method and fractal lacunarity calculated by the box counting method. The first two descriptors were obtained as a result of the analysis of gray images, and the third was the result of analysis of binary images. The effectiveness of the descriptors was verified using six popular supervised classification methods: linear and quadratic discriminant analyses, naive Bayes classifier, decision tree, $K$-nearest neighbors ($K$-NN) and random forests. The best results were obtained using the $K$-NN classifier; they were as follows: overall classification accuracy: 81%, classification sensitivity: 78%, classification specificity: 90%, positive predictive value: 90% and negative predictive value: 77%. The results of the research have shown that fractal analysis can be a useful tool to extract features of spinal CT images in the diagnosis of osteoporotic bone defects.

The segmentation of scenes into perceptually meaningful partitions has been a basic problem in image understanding, especially when unsupervised methodology has been desired. A novel unsupervised segmentation approach based on texture is developed. The texture model is based on sets of gray level cooccurence (GLC) matrices rather than measures extracted from them. The algorithmic constituents for the segmentation scheme: choice of seed regions, normalized match distances between texture models, region homogeneity, and aggregation criteria are systematically developed. The unsupervised algorithm works so that “seed” regions are discovered by an image search process. Initial estimates of the texture model prototypes are automatically computed for each “seed” region, and classification thresholds are based on the variance of the model over the “seed” region. An aggregation process then results in regions being successively classified and segmented “out” of the image. This recursive process of segmentation is continued until all pixels are classified. The segmentation strategy was tested successfully on natural texture mosaics. The results are analytically presented. These experiments demonstrate that the unsupervised process can correctly identify the perceptual constituents of the image based on texture.

The aim of "Shape From Texture (SFT)" methods is to obtain three dimensional information out of the monocular view of a scene. In this article, we detail a 2-step SFT computational method. A new local scales extraction technique is used in the first step to compute the local scale of each point of the picture through an interpolation of wavelet values. In the second step, the interpolation of the local scales map helps get a hold of the textured plane's orientation, using the perspective projection model. This method, which is an improvement over Lu's, was tested on synthetic and real textures. Results obtained were consistently better, notably in the case of small slant angles. We also improved the orientation computation by using the vanishing line equation for macrotextures.

Computerized tongue diagnosis can make use of a number of pathological features of the tongue. To date, there have been few computerized applications that focus on the very commonly used and distinctive diagnostic and textural features of the tongue, Fungiform Papillae Hyperplasia (FPH). In this paper, we propose a computer-aided system for identifying the presence or absence of FPH. We first define and partition a region of interest (ROI) for texture acquisition. After preprocessing for detection and removal of reflective points, a set of 2D Gabor filter banks is used to extract and represent textural features. Then, we apply the Linear Discriminant Analysis (LDA) to identify the data sets from the tongue image database. The experimental results reasonably demonstrate the effectiveness of the method described in this paper.

Aim of this paper is to develop an automated system for the classification and characterization of carotid wall status and to develop a robust system based on local texture descriptors. A database of 200 longitudinal ultrasound images of carotid artery is used. One-hundred images with Intima-Media Thickness (IMT) value higher than 0.8mm are considered as high risk. Six different rectangular pixel neighborhoods were considered: four areas centered on the selected element, with sizes $7\times 15$, $15\times 7$, $7\times 3$, and $3\times 7$ pixels, and two noncentered areas with sizes $7\times 3$ pixels upwards and downwards. We have extracted various texture descriptors (31 based on the co-occurrence gray level matrix, 13 based on the spatial gray level dependence matrix, and 20 based on the gray level run length matrix (GLRLM) from neighborhood. We have used Quick Reduct Algorithm to select 12 most discriminant features from extracted 211 features. Each pixel is then assigned to the vessel lumen, to the intima-media complex, or to the adventitia by using an integrated system of three feed-forward neural networks. The boundaries between the three regions are used to estimate the IMT value. The texture features associated with GLRLM are found to be clinically most significant. We have obtained an overall classification accuracy of 79.5%, sensitivity of 87%, and specificity of 72%. We observed a unique classification pattern between low risk and high risk images: in the latter ones, a considerable number of pixels of the intima–media complex ($31.2\%\pm 14.4\%$) was classified as belonging to the adventitia. This percentage is statistically higher than that of low risk images ($18.2\%\pm 11.8\%$; $p<0.001$). Locally extracted and pixel-based descriptors are able to capture the inner characteristics of the carotid wall. The presence of misclassified pixels in the intima–media complex is associated to higher cardiovascular risk.

The quality of olive fruit and its virgin olive oil is a main concern for consumers and fruit industrial companies. The effectiveness and fast detection of olive’s skin defects is the most decisive factor in determining its quality. It is necessary to design and implement image processing tools for segmentation and correct classification of the different fresh incoming olive batches. In this paper, we propose a new automatic image segmentation algorithm, based on discrete wavelets transform. The aim of the segmentation algorithm is to discriminate between olives and the background with the challenge of irregular and dispersive lesion borders, low contrast, artifacts in the olive fruit and variety of colors within the interest region. The second part of our work proposes a scheme for olive fruit classification. The classifier first identifies the olive fruit color and then, based upon discrete wavelets transform and Tamura statistical texture features, the healthy olive fruit is distinguished from the damaged one. The new texture feature vector is, then, compared with the robust Local Binary Pattern feature vector. The simplicity of our segmentation and classification algorithms makes them appropriate for designing a productive and profitable computer vision machine.

Image fusion is an important concept in remote sensing. Earth observation satellites provide both high-resolution panchromatic and low-resolution multispectral images. Pansharpening is aimed on fusion of a low-resolution multispectral image with a high-resolution panchromatic image. Because of this fusion, a multispectral image with high spatial and spectral resolution is generated. This paper reports a new method to improve spatial resolution of the final multispectral image. The reported work proposes an image fusion method using wavelet packet transform (WPT) and principal component analysis (PCA) methods based on the textures of the panchromatic image. Initially, adaptive PCA (APCA) is applied to both multispectral and panchromatic images. Consequently, WPT is used to decompose the first principal component of multispectral and panchromatic images. Using WPT, high frequency details of both panchromatic and multispectral images are extracted. In areas with similar texture, extracted spatial details from the panchromatic image are injected into the multispectral image. Experimental results show that the proposed method can provide promising results in fusing multispectral images with high-spatial resolution panchromatic image. Moreover, results show that the proposed method can successfully improve spectral features of the multispectral image.