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Different kinds of illustrations and artistic imagery can be generated or simulated through the nonphotorealistic rendering (NPR) technique. However, designing and simulating new NPR artistic styles remains extremely challenging. Chalk art style is a very famous artistic work all over the world, and few algorithms have been put forward to illustrate this style. This paper presents a novel NPR technique which generates a chalk art drawing from a 2D photograph automatically. We aim at obtaining a set of lines surface with coarse appearance and generating stroke textures of the real chalk painting. Firstly, the edge of the source image is extracted by difference-of-Gaussian filter method. To simulate chalk painting's lines, image diffusion and enhancement techniques are proposed to produce coarse and rough lines. Secondly, we developed an improved line integral convolution and dilation operation methods to produce the chalk stroke texture. Finally, the edge image, stroke texture image and color image will be mapped to another background image to generate the chalk art drawing. Experimental results are presented to show the effectiveness of our method in producing the color chalk stylistic illustrations, and the methods can simulate the characters of the real chalk art painting. The proposed method of this paper will enlarge the research and application fields of NPR. Meanwhile, it provides a tool for the user to create chalk art paintings via computers even without painting skill. [ABSTRACT FROM AUTHOR]

Green plant species identification plays an important role in so many aspects, such as ecological environment protection, Chinese medicine preparation, agricultural and horticultural application, etc. A method on green plants recognition based on wavelet transform and variable local edge patterns (VLEP) is proposed in this paper. Firstly, the original image is decomposed by wavelet transformation. Then texture features are extracted using VLEPs. At the same time, block-based and multi-resolution ideas are considered together to extract features after images are transformed by wavelet. Finally, the fused texture features are classified by the nearest neighbor method. The experimental results show that the proposed method is a promising method for recognizing the common green plants with the natural and complex background compared with the other state-of-the-art methods, and combination of block-based and multi-resolution ideas can further improve the accuracy rate effectively. [ABSTRACT FROM AUTHOR]

The liver cancer is one of the most common fatal diseases worldwide, and its early detection through medical imaging is a major contributor to the reduction in mortality from certain cancer. This paves the way to work on diagnosing liver diseases effectively. An accurate diagnosis of liver disease in CT image requires an efficient description of textures and classification methods. This paper performs comparative analysis on proposed texture feature descriptor with the different existing texture features with various classifiers to classify six types of diffused and focal liver diseases. The classification of liver diseases is done in two stages. In first stage, features like segmentation based fractal texture analysis, counting label occurrence matrix, local configuration pattern, eXtended center-symmetric local binary pattern and the proposed local symmetric tetra pattern are used for extracting information from the CT liver structure and classifiers like support vector machine, k -nearest neighbor, and naive Bayes are used for classifying the pathologic liver. When pathologic conditions are detected, the best feature descriptors and classifiers are used to classify the results into any of six exclusive pathologic liver diseases, in second stage. The experiments are carried out in medically validated liver datasets containing normal and six-disease category of liver. The first experiment is analyzed using sensitivity, specificity, and accuracy. The second experiment is evaluated using precision, recall, BCR, and F-measure. The results demonstrate that the local symmetric tetra pattern with k -nearest neighbor classifier culminates in a state-of-the-art performance for diagnosing liver diseases. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Natural images exhibit a high degree of complexity, randomness and irregularity in color and texture, however fractal can be an effective tool to describe various irregular phenomena in nature. Fractal dimensions are important because they can be defined in connection with real-world data, and they can be measured approximately by means of experiments. In this paper, we proposed a fractal dimension estimation method for RGB color images. In the proposed method, we present a hyper-surface partition method which considers the hyper-surface as continuous and divide the image into nonoverlapped blocks. We also defined a counting method in color domain. To validate the proposed method, experiments were carried on two types of color images: synthesized fractal images and natural RGB color images. The experimental results demonstrate that the proposed method is effective and efficient. The behaviors of the proposed method on the rescale images are also shown in the paper. And it can be performed as a reliable FD estimation approach for the RGB color images. [ABSTRACT FROM AUTHOR]

This paper presents a new diffusion coefficient which is based on second order derivative and local entropy information for image denoising. In the proposed model, a second order derivative term is introduced, which reduces the staircasing effect and preserves edge in a processed image. The local entropy information can preserve texture. The Perona-Malik model with a new diffusion coefficient improves the denoised effects, and prevents edges from being over-smoothed. Comparative experiments show that the proposed model obtains more satisfied results than the other two existing models. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to introduce and study new notions of continuity, compactness and stability in ditopological texture spaces based on the notions of β - g -open and β - g -closed sets and some of their characterizations are obtained. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Texture classification is a challenging and important problem in image analysis. graphical models (GM) are promising tools for texture analysis. In this paper, we address the problem of learning the structure of Gaussian graphical models (GGM) for texture models. GGM can be considered as regression problems due to the connection between the local Markov properties and conditional regression of a Gaussian random variable. We utilize L1-penalty regularization technique for appropriate neighborhood selection and parameter estimation simultaneously. The proposed algorithms are applied in texture synthesis and classification. Experimental results on Brodatz textures demonstrate that the proposed algorithms have good performance and prospects. [ABSTRACT FROM AUTHOR]

Optical coherence tomography (OCT) is employed in the diagnosis of skin cancer. Particularly, quantitative image features extracted from OCT images might be used as indicators to classify the skin tumors. In the present paper, we investigated intensity-based, texture-based and fractal-based features for automatically classifying the melanomas, basal cell carcinomas and pigment nevi. Generalized estimating equations were used to test for differences between the skin tumors. A modified p value of 0.001 was considered statistically significant. Significant increase of mean and median of intensity and significant decrease of mean and median of absolute gradient were observed in basal cell carcinomas and pigment nevi as compared with melanomas. Significant decrease of contrast, entropy and fractal dimension was also observed in basal cell carcinomas and pigment nevi as compared with melanomas. Our results suggest that the selected quantitative image features of OCT images could provide useful information to differentiate basal cell carcinomas and pigment nevi from the melanomas. Further research is warranted to determine how this approach may be used to improve the classification of skin tumors. [ABSTRACT FROM AUTHOR]

This paper proposes an image effect creation system that can create various types of effects for images, to satisfy different needs of users for specific design goals. Some operations needed for creating image effects, such as flow-based bilateral filter, flow-based Gaussian filter, curve-shaped filters, line drawing, pencil texture generator, and modified shock filter, are first proposed, and various image effects that can be converted by the proposed system are illustrated. The experiments show very rich results in terms of image effects. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Texture is an important feature that aids in identifying objects of interest or region of interest irrespective of the source of the image. In this paper, a novel and simple isopattern-based texture feature is introduced. Spatial gray scale dependencies represented by bit plane is analyzed for specific patterns and are accumulated in bins. These are scaled by half-normal weighting function to provide isopattern texture feature. The ability of this texture feature in capturing textural variations of the images despite the presence of illumination, scale and rotation is demonstrated by conducting texture analysis on Brodatz, OuTex texture datasets and its classification accuracy on Kylberg dataset. The results of these two experimentation indicate that the proposed textural feature picks variation in texture significantly and has a better texture classification accuracy of 98.26% when compared with the state-of-the-art features like Gabor, GLCM and LBP. [ABSTRACT FROM AUTHOR]

Unsupervised image segmentation is a fundamental but challenging problem in computer vision. In this paper, we propose a novel unsupervised segmentation algorithm, which could find diverse applications in pattern recognition, particularly in computer vision. The algorithm, named Two-stage Fuzzy c-means Hybrid Approach (TFHA), adaptively clusters image pixels according to their multichannel Gabor responses taken at multiple scales and orientations. In the first stage, the fuzzy c-means (FCM) algorithm is applied for intelligent estimation of centroid number and initialization of cluster centroids, which endows the novel segmentation algorithm with adaptivity. To improve the efficiency of the algorithm, we utilize the Gray Level Co-occurrence Matrix (GLCM) feature extracted at the hyperpixel level instead of the pixel level to estimate centroid number and hyperpixel-cluster memberships, which are used as initialization parameters of the following main clustering stage to reduce the computational cost while keeping the segmentation performance in terms of accuracy close to original one. Then, in the second stage, the FCM algorithm is utilized again at the pixel level to improve the compactness of the clusters forming final homogeneous regions. To examine the performance of the proposed algorithm, extensive experiments were conducted and experimental results show that the proposed algorithm has a very effective segmentation results and computational behavior, decreases the execution time and increases the quality of segmentation results, compared with the state-of-the-art segmentation methods recently proposed in the literature. [ABSTRACT FROM AUTHOR]

Feature saliency estimation and feature selection are important tasks in machine learning applications. Filters, such as distance measures are commonly used as an efficient means of estimating the saliency of individual features. However, feature rankings derived from different distance measures are frequently inconsistent. This can present reliability issues when the rankings are used for feature selection. Two novel consensus approaches to creating a more robust ranking are presented in this paper. Our experimental results show that the consensus approaches can improve reliability over a range of feature parameterizations and various seabed texture classification tasks in sidescan sonar mosaic imagery. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, a fast pre-classified-based super-resolution model has been proposed to overcome the problems of degraded imaging in weak-target real-time detection system, specialized to copper defect detection. To accurately characterize the defected image, textural features based on the statistical function of gray-gradient are presented. Furthermore, to improve the effectiveness and practicality of the online detection, a concept of pre-classified learning is introduced and an edge smoothness rule is designed. Some experiments are carried out on defect images in different environments and the experimental results show the efficiency and effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

In this work, we introduce a feature adaptive second-order p-norm filter with local constraints for image restoration and texture preservation. The p-norm value of the filter is chosen adaptively between 1 and 2 in a local region based on the regional image characteristics. The filter behaves like a mean curvature motion (MCM) [A. Marquina and S. Osher, SIAM Journal of Scientific Computing 22, 387-405 (2000)] in the regions where the p-norm value is 1 and switches to a Laplacian filter in the rest of the regions (where the p-norm value is 2). The proposed study considerably reduces stair-case effect and effectively removes noise from images while deblurring them. The noise is assumed as Gaussian distributed (with zero mean and variance ) and blur is linearly shift invariant (out-of-focus). The filter converges at a faster rate with semi-implicit Crank-Nicholson scheme. The regularization parameter is initialized and updated based on the local image features and therefore this filter preserves edges, structures, textures and fine details present in images very well. The method is applied on different kinds of images with different image characteristics. We show the response of the filter to various kinds of images and numerically quantify the performance in terms of standard statistical measures. [ABSTRACT FROM AUTHOR]

Image completion is an active and interesting research area in image processing and computer graphics. Restoration and retouching of damaged areas in an undetectable form is the objective of image completion techniques. Most of the recently developed video completion methods are extensions of image completion techniques to restore the damaged frames. With respect to video completion challenges and image completion future work, we survey existing methods and introduce a new classification. The methods in each category are described in detail. In the second part of the paper, we provide a comparison and evaluation study between the most recent image completion methods qualitatively as well as quantitatively. For a fair comparison, we introduced a new dataset and evaluated four available image completion methods on the same hardware. Experimental results are conducted to highlight the strengths and drawbacks of each image completion method. [ABSTRACT FROM AUTHOR]

This paper proposes the utility of texture and color for iris recognition systems. It contributes for improvement of system accuracy with reduced feature vector size of just 1 × 3 and reduction of false acceptance rate (FAR) and false rejection rate (FRR). It avoids the iris normalization process used traditionally in iris recognition systems. Proposed method is compared with the existing methods. Experimental results indicate that the proposed method using only color achieves 99.9993 accuracy, 0.0160 FAR, and 0.0813 FRR. Computational time efficiency achieved is of 947.7 ms. [ABSTRACT FROM AUTHOR]

Belief propagation (BP) algorithm still exists some shortages, such as inaccurate edge preservation and ambiguous detail information in the foreground, while self-adapting dissimilarity measure (SDM) also exists some shortages, such as ill textureless and occluded information in the background. To address these problems, we present a novel stereo matching algorithm fusing BP and SDM with an excellent background and foreground information. Lots of experiments show that BP and SDM can complement each other. BP algorithm can hold the better background information due to message propagation inference, whereas SDM can possess the better foreground information due to detail treatment. Therefore, a piecewise function is proposed, which can combine BP algorithm in an excellent background information and SDM in the foreground information, and greatly improve the disparity effect as a whole. We also expect that this work can attract more attention on combination of local methods and global methods, due to its simplicity, efficiency, and accuracy. Experimental results show that the proposed method can keep the superior performance and hold better background and foreground on the Middlebury datasets, compared to BP and SDM. [ABSTRACT FROM AUTHOR]

An illumination and expression invariant face recognition method based on uniform local binary patterns (uLBP) and Legendre moments is proposed in this work. The proposed method exploits uLBP texture features and Legendre moments to make a feature representation with enhanced discriminating power. The input images are preprocessed to extract the face region and normalized. From normalized image, uLBP codes are extracted to obtain texture image which overcomes the effect of monotonic temperature changes. Legendre moments are computed from this texture image to get the required feature vector. Legendre moments conserve the spatial structure information of the texture image. The resultant feature vector is classified using k-nearest neighbor classifier with norm. To evaluate the proposed method, experiments are performed on IRIS and NVIE databases. The proposed method is tested on both visible and infrared images under different illumination and expression variations and performance is compared with recently published methods in terms of recognition rate, recall, length of feature vector, and computational time. The proposed method gives better recognition rates and outperforms other recent face recognition methods. [ABSTRACT FROM AUTHOR]

A leukocyte recognition method for human peripheral blood smear based on island-clustering texture (ICT) is proposed. By analyzing the features of the five typical classes of leukocyte images, a new ICT model is established. Firstly, some feature points are extracted in a gray leukocyte image by mean-shift clustering to be the centers of islands. Secondly, the growing region is employed to create regions of the islands in which the seeds are just these feature points. These islands distribution can describe a new texture. Finally, a distinguished parameter vector of these islands is created as the ICT features by combining the ICT features with the geometric features of the leukocyte. Then the five typical classes of leukocytes can be recognized successfully at the correct recognition rate of more than 92.3% with a total sample of 1310 leukocytes. Experimental results show the feasibility of the proposed method. Further analysis reveals that the method is robust and results can provide important information for disease diagnosis. [ABSTRACT FROM AUTHOR]

The present work focuses on the development of a novel computer-based approach for tear ferning (TF) featuring. The original TF images of the recently developed five-point grading scale have been used to assign a grade for any TF image automatically. A vector characteristic (VC) representing each grade was built using the reference images. A weighted combination between features selected from textures analysis using gray level co-occurrence matrix (GLCM), power spectrum (PS) analysis and linear specificity of the image were used to build the VC of each grade. A total of 14 features from texture analysis were used. PS at different frequency points and number of line segments in each image were also used. Five features from GLCM have shown significant differences between the recently developed grading scale images which are: angular second moment at 0° and 45°, contrast, and correlation at 0° and 45° these five features were all included in the characteristic vector. Three specific power frequencies were used in the VC because of the discrimination power. Number of line segments was also chosen because of dissimilarities between images. A VC for each grade of TF reference images was constructed and was found to be significantly different from each other's. This is a basic and fundamental step toward an automatic grading for computer-based diagnosis for dry eye. [ABSTRACT FROM AUTHOR]

Clustering is a popular and effective method for image segmentation. However, existing cluster methods often suffer the following problems: (1) Need a huge space and a lot of computation when the input data are large. (2) Need to assign some parameters (e.g. number of clusters) in advance which will affect the clustering results greatly. To save the space and computation, reduce the sensitivity of the parameters, and improve the effectiveness and efficiency of the clustering algorithms, we construct a new clustering algorithm for image segmentation. The new algorithm consists of two phases: coarsening clustering and exact clustering. First, we use Affinity Propagation (AP) algorithm for coarsening. Specifically, in order to save the space and computational cost, we only compute the similarity between each point and its t nearest neighbors, and get a condensed similarity matrix (with only t columns, where t << N and N is the number of data points). Second, to further improve the efficiency and effectiveness of the proposed algorithm, the Self-tuning Spectral Clustering (SSC) is used to the resulted points (the representative points gotten in the first phase) to do the exact clustering. As a result, the proposed algorithm can quickly and precisely realize the clustering for texture image segmentation. The experimental results show that the proposed algorithm is more efficient than the compared algorithms FCM, K-means and SOM. [ABSTRACT FROM AUTHOR]

Automatic Kinship verification aims at recognizing the degree of kinship of two individuals from their facial images and it has possible applications in image retrieval and annotation, forensics and historical studies. This is a recent and challenging problem, which must deal with different degrees of kinship and variations in age and gender. Our work explores the computer identification of parent-child pairs using a combination of (i) features of different natures, based on geometric and textural data, (ii) feature selection and (iii) state-of-the-art classifiers. Experiments show that the proposed approach provides a valuable solution to the kinship verification problem, as suggested by its comparison with different methods on the same data and the same experimental protocols. We further show the good generalization capabilities of our method in several cross-database experiments. [ABSTRACT FROM AUTHOR]

The issue of texture specific mass matrices has been discussed by incorporating Weak Basis transformations and the concept of "naturalness." Interestingly, we find that starting from the most general mass matrices, one can arrive at texture four zero mass matrices which can fit both quark as well as lepton mixing data and are similar to the original Fritzsch ansatze. [ABSTRACT FROM AUTHOR]

A systematic analysis of the textures arising in lepton mass matrices have been carried out using unitary transformations and condition of naturalness for the Dirac and Majorana neutrino possibilities. It is observed that the recent three neutrino oscillation data together with the effective mass in neutrinoless double beta decay provide vital clues in predicting the general structures of these lepton mass matrices. [ABSTRACT FROM AUTHOR]