Texture analysis is used in a very broad range of fields and applications, from texture classification (e.g., for remote sensing) to segmentation (e.g., in biomedical imaging), passing through image synthesis or pattern recognition (e.g., for image inpainting). For each of these image processing procedures, first, it is necessary to extract—from raw images—meaningful features that describe the texture properties. Various feature extraction methods have been proposed in the last decades. Each of them has its advantages and limitations: performances of some of them are not modified by translation, rotation, affine, and perspective transform; others have a low computational complexity; others, again, are easy to implement; and so on. This paper provides a comprehensive survey of the texture feature extraction methods. The latter are categorized into seven classes: statistical approaches, structural approaches, transform-based approaches, model-based approaches, graph-based approaches, learning-based approaches, and entropy-based approaches. For each method in these seven classes, we present the concept, the advantages, and the drawbacks and give examples of application. This survey allows us to identify two classes of methods that, particularly, deserve attention in the future, as their performances seem interesting, but their thorough study is not performed yet.

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Texture Feature Extraction Methods: A Survey

Ceramic and tile industries should indispensably include a grading stage to quantify the quality of products. Actually, human control systems are often used for grading purposes. An automatic grading system is essential to enhance the quality control and marketing of the products. Since there generally exist six different types of defects originating from various stages of tile manufacturing lines with distinct textures and morphologies, many image processing techniques have been proposed for defect detection. In this paper, a survey has been made on the pattern recognition and image processing algorithms which have been used to detect surface defects. Each method appears to be limited for detecting some subgroup of defects. The detection techniques may be divided into three main groups: statistical pattern recognition, feature vector extraction and texture/image classification. The methods such as wavelet transform, filtering, morphology and contourlet transform are more effective for pre-processing tasks. Others including statistical methods, neural networks and model-based algorithms can be applied to extract the surface defects. Although, statistical methods are often appropriate for identification of large defects such as Spots, but techniques such as wavelet processing provide an acceptable response for detection of small defects such as Pinhole. A thorough survey is made in this paper on the existing algorithms in each subgroup. Also, the evaluation parameters are discussed including supervised and unsupervised parameters. Using various performance parameters, different defect detection algorithms are compared and evaluated.

Surface defect detection in tiling Industries using digital image processing methods: analysis and evaluation.

To assess the performance of a predictive model of non-response to neoadjuvant chemotherapy (NAC) in patients with breast cancer based on texture, kinetic, and BI-RADS parameters measured from dynamic MRI. Sixty-nine patients with invasive ductal carcinoma of the breast who underwent pre-treatment MRI were studied. Morphological parameters and biological markers were measured. Pathological complete response was defined as the absence of invasive and in situ cancer in breast and nodes. Pathological non-responders, partial and complete responders were identified. Dynamic imaging was performed at 1.5 T with a 3D axial T1W GRE fat-suppressed sequence. Visual texture, kinetic and BI-RADS parameters were measured in each lesion. ROC analysis and leave-one-out cross-validation were used to assess the performance of individual parameters, then the performance of multi-parametric models in predicting non-response to NAC. A model based on four pre-NAC parameters (inverse difference moment, GLN, LRHGE, wash-in) and k-means clustering as statistical classifier identified non-responders with 84 % sensitivity. BI-RADS mass/non-mass enhancement, biological markers and histological grade did not contribute significantly to the prediction. Pre-NAC texture and kinetic parameters help predicting non-benefit to NAC. Further testing including larger groups of patients with different tumor subtypes is needed to improve the generalization properties and validate the performance of the predictive model.

https://link.springer.com/content/pdf/10.1186%2Fs12885-015-1563-8.pdf

Texture analysis on MR images helps predicting non-response to NAC in breast cancer

Technologies and services towards smart-vehicles and Intelligent-Transportation-Systems (ITS), continues to revolutionize many aspects of human life. This paper presents a detailed survey of current techniques and advancements in Automatic-Number-Plate-Recognition (ANPR) systems, with a comprehensive performance comparison of various real-time tested and simulated algorithms, including those involving computer vision (CV). ANPR technology has the ability to detect and recognize vehicles by their number-plates using recognition techniques. Even with the best algorithms, a successful ANPR system deployment may require additional hardware to maximize its accuracy. The number plate condition, non-standardized formats, complex scenes, camera quality, camera mount position, tolerance to distortion, motion-blur, contrast problems, reflections, processing and memory limitations, environmental conditions, indoor/outdoor or day/night shots, software-tools or other hardware-based constraint may undermine its performance. This inconsistency, challenging environments and other complexities make ANPR an interesting field for researchers. The Internet-of-Things is beginning to shape future of many industries and is paving new ways for ITS. ANPR can be well utilized by integrating with RFID-systems, GPS, Android platforms and other similar technologies. Deep-Learning techniques are widely utilized in CV field for better detection rates. This research aims to advance the state-of-knowledge in ITS (ANPR) built on CV algorithms; by citing relevant prior work, analyzing and presenting a survey of extraction, segmentation and recognition techniques whilst providing guidelines on future trends in this area.

https://www.mdpi.com/1424-8220/21/9/3028/pdf

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms.

Due to the rapid increase of different digitised documents, there has been significant attention dedicated to document image retrieval over the past two decades. Finding discriminative and effective features is a fundamental task for providing a fast and more accurate retrieval system. Texture features are generally fast to compute and are suitable for large volume data. Thus, in this study, the effectiveness of texture features widely used in the literature of content-based image retrieval is investigated on document images. Twenty-six different texture feature extraction methods from four main categories of texture features, statistical, transform, model, and structural-based approaches, are considered in this research work to compare their performance on the problem of document image retrieval. Three document image datasets, MTDB, ITESOFT, and CLEF_IP with various content and page layouts are used to evaluate the twenty-six texture-based features on document image retrieval systems. The retrieval results are computed in terms of precision, recall and F-score, and a comparative analysis of the results is also provided. Feature dimensions and time complexity of the texture-based feature methods are further compared. Finally, some conclusions are drawn and suggestions are made about future research directions.

A comparative study of different texture features for document image retrieval

Over the past few years, neural networks have exhibited remarkable results for various applications in machine learning and computer vision. Weight initialization is a significant step employed before training any neural network. The weights of a network are initialized and then adjusted repeatedly while training the network. This is done till the loss converges to a minimum value and an ideal weight matrix is obtained. Thus weight initialization directly drives the convergence of a network. Therefore, the selection of an appropriate weight initialization scheme becomes necessary for end-to-end training. An appropriate technique initializes the weights such that the training of the network is accelerated and the performance is improved. This paper discusses various advances in weight initialization for neural networks. The weight initialization techniques in the literature adopted for feed-forward neural network, convolutional neural network, recurrent neural network and long short term memory network have been discussed in this paper. These techniques are classified as (1) initialization techniques without pre-training, which are further classified into random initialization and data-driven initialization, (2) initialization techniques with pre-training. The different weight initialization and weight optimization techniques which select optimal weights for non-iterative training mechanism have also been discussed. We provide a close overview of different initialization schemes in these categories. This paper concludes with discussions on existing schemes and the future scope for research.

A review on weight initialization strategies for neural networks

Texture is a fundamental characteristic in many natural images that plays an important role in human visual perception and in turn provides information for image understanding and scene interpretation. The textured image can be modeled to describe, analyze and synthesize the texture. The model parameters capture the essential perceived qualities of texture. One of the important characteristics of texture data is the statistical dependence of the gray level at a lattice point on those of its neighbors. The spatial-interaction models characterize this statistical dependency by representing the intensity of a pixel, as a 2-D linear combination of the intensity of its neighbors and an additive noise. One way of specifying this interaction is simultaneous autoregressive (SAR) models. This is one of the most traditional methods used for modeling in the area of image processing. This paper presents the work done by the authors on parameter estimation and synthesis of textured images using Simultaneous Autoregressive (SAR) modeling. Different programs are developed in MATLAB to implement the parameter estimation and synthesis and are tested for their performance. The scope of this work includes the use of causal and noncausal methods for modeling and synthesizing natural textures. Simultaneous or spatial autoregressive models with causal and noncausal neighborhoods are used for parameter estimation and texture pattern generation. Parameter estimation is done by two different methods: The least square error (LSE) and maximum likelihood estimation (MLE). LSE method is preferred for causal models. MLE method is used for noncausal autoregressive models and it uses iterative algorithm. The synthesis procedure is based on generating a two dimensional autoregressive random field driven by a two dimensional zero mean white noise field with unit variance. Two different algorithms are used for synthesis of causal and noncausal AR models. Different image textures are synthesized using a given set of neighborhoods and parameters. Different patterns of synthetic images can be generated using various sets of parameters. A number of images from Brodatz album are tested for parameter estimation and synthesis. The synthesized image retains the pattern in the original image like vertical or horizontal streaks. An interactive graphical user interface (GUI) is developed using MATLAB that allows user to select one image from Brodatz album. The user can choose between causal or noncausal neighborhood and select number of elements in the neighborhood or choose any one set of neighborhood from different sets stored, and find out SAR model parameters by one of the methods of parameter estimation. The user can synthesize the image using these parameters. Both original and synthesized image are displayed side by side on the screen and the user can easily compare the two images. Thus the GUI offers an interactive platform for implementation of parameter estimation using different neighborhoods for the images from Brodatz album and synthesis of the image from these estimated parameters.

Texture representation using autoregressive models

A simple and fast technique is presented in this paper for Indian license plate recognition system. Using OpenALPR’s software framework and RaspberryPi, a real-time Indian license plate recognition system could be implemented for some application specific purposes. HAAR and LBP features are extracted from the acquired vehicle images and subjected to training of cascade classifiers in order to localize license number plates. The validation process is used to optimally select number of stages of cascade classifiers. The extracted number plates are then utilized for character recognition. Cascade classifier with LBP features is suitable for localization of license plates with accuracy more than 98%. Whereas, the average number plate recognition accuracy is above 96% for images captured from front side. The proposed system has been prototyped using C++ and RaspberryPi 3 and experimental results have been shown for recognition of Indian license plates

Real-Time Implementation Of Indian License Plate Recognition System

This paper deals with texture feature extraction operators, which comprise linear filtering eventually followed by post processing. Robust, rotation and scale invariant texture operators are important for digital image libraries and multimedia databases. A method of rotation and scale-invariant texture classification based on a log polar coordinate system is introduced. Texture is an important clue in region based segmentation of images. Here, we provide analysis and implementation of a set of distortion invariant texture operators viz circular Mellin features (CMF). The CMF represent the spectral decomposition of the image scene in the polar log coordinate system and are invariant to both scale and orientation of the target texture pattern. The image and CMF are correlated followed by magnitude detection based on thresholding. The CMF extractors have a functional form that is similar to Gabor functions; they have distortion invariant characteristics, unlike Gabor functions, which makes them more suitable for texture segmentation.

Rotation and scale invariant feature extractors

Structural textures are characterised by a repeating pattern called `Texton` and placement rule - that determines the nature of periodicity. Based on the periodicity, textures are classified as homogeneous - perfectly periodic - and weakly homogeneous - quasi-periodic. Both of these textures are assumed to be combination of structural information, illumination, that is, average brightness at different sites of the texture and stochasticity to allow local variations. A top-down approach extracts structural information, that is, the grid, representative texton and illumination component from the original texture patch and then the information is used to synthesise similar textures. Obviously, the technique does not produce ditto copies in the synthesised texture. The experimentation is carried out to improve quality of synthesised textures by incorporating multiple representative textons. Also a parameter, namely homogeneity co-efficient (HC), is suggested to compare the original texture patch and synthesised texture. The parameter captures variations in the textons, contents and sizes both, and thus can be used to compare the synthesis results. The suitability of the proposed synthesis approach and HC is verified by rigorous experimentation on weakly homogeneous artificial and standard structural textures. Efforts are also made to utilise the multi-core processing capability of the processor to improve the speed of analysis and synthesis phases.

Prashant P. Bartakke

Papers

A review on weight initialization strategies for neural networks

Texture representation using autoregressive models

Real-Time Implementation Of Indian License Plate Recognition System

Rotation and scale invariant feature extractors

Refining structural texture synthesis approach