Texture Segmentation Algorithm Research Articles

A semi-supervised deep learning approach for circular hole detection on composite parts

This paper introduces the usage of semi-supervised learning to obtain competitive detection accuracy of measuring drilled holes on composite parts with very limited noisy training data. An improved texture segmentation algorithm based on local binary patterns algorithm is proposed, named local exponential patterns. The algorithm divides the image texture into nine levels, of which the highest level of texture is selected for contour extraction. An ellipse fitting method is used to fit the target contours and vote for the candidate ellipses. The regions inside the candidate ellipses are taken as the semi-supervised semantic label for images. A new loss named round loss is proposed, and a superior circle segmentation model was trained by learning from incompletely annotated data. To verify the effectiveness of the method, experiments were conducted with the drilled holes on the composite parts. The results show that the proposed semi-supervised deep learning approach is exceedingly suitable for circle detection of holes with different texture information commonly found in robotic drilling. Massive data labeling can be completely avoided with proposed method. The measurement accuracy can reach 0.03 mm, which can meet the visual measurement requirements of the circular holes on composite parts in the robotic drilling system.

Markov Random Field Based Color Texture Segmentation

Texture segmentation is one of the popular research domains and researchers across the globe are working on texture segmentation to enhance segmentation performance to address its requirements in many fields. Color texture segmentation has wide spectrum of applications in diverse fields such as segmentation of natural images, medical image analysis, remote sensing, shape extraction and inspection of products etc. This paper presents color texture segmentation algorithm which can satisfy requirements for such applications. Proposed algorithm is based on Markov Random Field (MRF) model eliminating the need of major contributor viz. Gabor filter used in past four decades for feature extraction and use only color as texture feature. Highly crude segmentation results are produced using only color as texture features. Crude segmentation results are enhanced by using Median filter with enlarged window size quantitatively determined by using parameters viz. structural similarity index (SSIM), mean square error (MSE) and peak signal to noise ratio (PSNR). Feature space dimensions are reduced by factor of 11 in proposed approach and this reduced computations by a factor of 11. The experimentation is carried out on 80 multi-class color texture benchmark images from Prague texture segmentation dataset and 4 benchmark images in Vistex dataset. Mean segmentation accuracy achieved for Prague texture dataset is 87.55% and it is higher by 9.82% over the best performing algorithm among 11 state-of-art algorithms suggested in most recent literature. Accuracy achieved for Vistex dataset is 98.21%. Average SSIM for Prague dataset is 0.91403 and Vistex dataset is 0.9405.

Segmentation of Visual Images by Sequential Extracting Homogeneous Texture Areas

The purpose of the research is to develop a universal algorithm for partial texture segmentation of any visual images. The main peculiarity of the proposed segmentation procedure is the extraction of only homogeneous fine-grained texture segments present in the images. At first, an initial seed point is found for the largest and most homogeneous segment of the image. This initial seed point of the segment is expanded using a region growing method. Other texture segments of the image are extracted analogously in turn. At the second stage, the procedure of merging the extracted segments belonging to the same texture class is performed. Then, the detected texture segments are input to a neural network with competitive layers which accomplishes more accurate delineation of the shapes of the extracted texture segments. The proposed segmentation procedure is fully unsupervised, i.e., it does not use any a priori knowledge on either the type of textures or the number of texture segments in the image. The research results in development of the segmentation algorithm realized as a computer program tested in a series of experiments that demonstrate its efficiency on grayscale natural scenes.

Identification of Mouth Cancer laceration Using Machine Learning Approach

This Paper describes about Identification of Mouth Cancer laceration Using Machine Learning Approach .The SVM algorithm is used for this purpose. Image segmentation operations are performed using: Resizing an image, Gray scale conversion, Histogram equalization and Classifying the Segmented image using SVM. SVM is used to reduce the complexity faced in the existing system comprising of Texture Segmentation and ANN (Artificial Neural Networks) Algorithm. SVM is a simple Machine Learning algorithm when compared to ANN. The outcome of the paper is to segment and classify the Malignancy from the Non-Malignant region using the classifier SVM. SVM performs the classification based on the dataset that contains the trained images.

Voronoi tessellation‐based regionalised segmentation for colour texture image

This study presents a region‐based algorithm for segmenting colour texture image, which uses Voronoi tessellation for partitioning the domain of the image and Markov random field (MRF) for modelling colour texture. In detail, (i) an image domain is divided into polygons (or sub‐regions) by Voronoi tessellation; (ii) two MRF models, improved Potts model and multivariate Gaussian MRF model, are used to characterise colour texture structures inter‐ and intra‐polygons, respectively; (iii) by Bayesian paradigm, a posterior distribution which characterises the segmentation and model parameters conditional on a given colour image can be obtained up to a normalising constant; (iv) a Markov chain Monte Carlo algorithm is developed to simulate from the posterior distribution; finally, (v) a maximum a posteriori scheme is employed to find an optimal segmentation and model parameters. In order to evaluate the proposed colour texture segmentation algorithm, two kinds of colour texture images are tested, including synthetic and real colour texture images. The accuracy assessments are performed qualitatively on all kinds of images and quantitatively on synthetic images. All results demonstrate that the proposed algorithm is efficiently.

Studies on Texture Segmentation Using D-Dimensional Generalized Gaussian Distribution integrated with Hierarchical Clustering

Texture deals with the visual properties of an image. Texture analysis plays a dominant role for image segmentation. In texture segmentation, model based methods are superior to model free methods with respect to segmentation methods. This paper addresses the application of multivariate generalized Gaussian mixture probability model for segmenting the texture of an image integrating with hierarchical clustering. Here the feature vector associated with the texture is derived through DCT coefficients of the image blocks. The model parameters are estimated using EM algorithm. The initialization of model parameters is done through hierarchical clustering algorithm and moment method of estimation. The texture segmentation algorithm is developed using component maximum likelihood under Bayesian frame. The performance of the proposed algorithm is carried through experimentation on five image textures selected randomly from the Brodatz texture database. The texture segmentation performance measures such as GCE, PRI and VOI have revealed that this method outperform over the existing methods of texture segmentation using Gaussian mixture model. This is also supported by computing confusion matrix, accuracy, specificity, sensitivity and F-measure.

Target recognition by texture segmentation algorithm

In order to improve the performance of image segmentation, this paper presented a gray level jump segmentation algorithm, which defined the direction of the texture, simultaneously, calculated the width of ridge line, gave the distance characteristics between textures, and established the mathematical model of the texture border, accordingly presented a new texture segmentation algorithm and compared with other texture segmentation algorithms. The simulation results show that the segmentation algorithm has some advantages to texture segmentation, such as has higher segmentation precision, faster segmentation speed, stronger anti-noise capability, less lost information of target, and so on. The segmented regions hardly contain other texture regions and background region. Moreover, this paper extracted the characteristic points and characteristic parameters in various segmented regions for texture image to obtain the characteristic vector, compared the characteristic vector with the standard template vectors, and identified the type of target in a range of threshold value. Experimental results show that the proposed target recognition approach has higher recognition rate and faster recognition speed than the existing target recognition approaches. Advancements in image processing through the study of texture segmentation are not only applicable to image fields, but also are of important theoretical value to target recognition. These researches in this paper will play an important role in a theoretical reference and practical significance to the development of all target recognition departments based on image system such as the aerospace, public security, road traffic, and so on.

Multispectral textured image segmentation using a multi-resolution fuzzy Markov random field model on variable scales in the wavelet domain

This article proposes a new multispectral image texture segmentation algorithm using a multi-resolution fuzzy Markov random field model for a variable scale in the wavelet domain. The algorithm considers multi-scalar information in both vertical and lateral directions. The feature field of the scalable wavelet coefficients is modelled, combining with the fuzzy label field describing the spatially constrained correlations between neighbourhood features to achieve a more accurate parameter estimation. The extended scalable label field models the label data from different scales to obtain more homogeneous areas; image segmentation results are finally obtained according to the Bayesian rule from a coarser to a finer scale. Multispectral texture images and remote-sensing images are used to test the effectiveness of the the proposed method. Segmentation results show that the new method simultaneously presents a better performance in achieving the homogeneity of the region and accuracy of detected boundaries compared with existing image segmentation algorithms.

Optimization of neural network training using genetic algorithm for texture segmentation of MRI - images

An analysis of existing methods for image processing. These solutions allow you to create an automated system for diagnosing diseases based on data obtained from images of magnetic resonance imaging. Satisfy the requirements of the system diagnostic neural network based on three - layer perceptron. In the process simulation software package MatLab v R2007b, were investigated speed of network training on the number of neurons in the hidden layer for the conventional method of back propagation and combined with genetic algorithm. The study revealed the advantage of the combined method for image classification task. This method halves the number of training effects and requires fewer neurons in the hidden layer, which facilitates network architecture and reduces the computational cost

A Texture Segmentation Algorithm Based on PCA and Global Minimization Active Contour Model for Aerial Insulator Images

In this paper, a novel texture segmentation algorithm is proposed to partition the complex aerial insulator images into sub-regions with closed smooth contours. Firstly, Gray Level Co-occurrence Matrix (GLCM) is employed to extract the texture features of insulators and is calculated by the rapid Gray Level Co-occurrence Integrated Algorithm (GLCIA). We divide the extracted texture features into two categories: one with the stronger discriminative ability and the other with weaker ability. The second category is optimized by Principal Component Analysis (PCA) to better distinguish the different texture objects with low contrast. Then, a new convex energy functional is defined by taking the non-convex model of the Texture Descriptor Active Contour (TDAC) into a global minimization framework (GMAC) during segmentation. The proposed energy functional can avoid the existence of local minima in the minimization of the TDAC. A fast dual formulation is introduced for the efficient evolution of the contour. The experimental results on synthetic and real aerial insulator remote sensing images have shown that the proposed algorithm obtains more satisfactory segmentation compared to the classical models in terms of accuracy, efficiency and independence of initial contour. The influence of the algorithm parameters is also analyzed.

Driver’s eye blinking detection using novel color and texture segmentation algorithms

In this paper we propose a system that measures eye blinking rate and eye closure duration. The system consists of skin-color segmentation, facial features segmentation, iris positioning and blink detection. The proposed skin-segmentation procedure is based on a neural network approximation of a RGB skin-color histogram. This method is robust and adaptive to any skin-color training set. The largest remaining skin-color region among skin-color segmentation results is further segmented into open/closed eyes, lips, nose, eyebrows, and the remaining facial regions using a novel texture segmentation algorithm. The segmentation algorithm classifies pixels according to the highest probability among the estimated facial feature class probability density functions (PDFs). The segmented eye regions are analyzed with the Circular Hough transform with the purpose of finding iris candidates. The finial iris position is selected according to the location of the maximum correlation value obtained from correlation with a predefined mask. The positions of irises and eye states are monitored through time to estimate eye blinking frequency and eye closure duration. The method of the driver drowsiness detection using these parameters is illustrated. The proposed system is tested on CCD and CMOS cameras under different environmental conditions and the experimental results show high system performance.

Subspace learning for Mumford–Shah-model-based texture segmentation through texture patches

In this paper, we develop a robust and effective algorithm for texture segmentation and feature selection. The approach is to incorporate a patch-based subspace learning technique into the subspace Mumford-Shah (SMS) model to make the minimization of the SMS model robust and accurate. The proposed method is fully unsupervised in that it removes the need to specify training data, which is required by existing methods for the same model. We further propose a novel (to our knowledge) pairwise dissimilarity measure for pixels. Its novelty lies in the use of the relevance scores of the features of each pixel to improve its discriminating power. Some superior results are obtained compared to existing unsupervised algorithms, which do not use a subspace approach. This confirms the usefulness of the subspace approach and the proposed unsupervised algorithm.

A high throughput, interactive imaging, bright‐field wound healing assay

The wound healing assay is a commonly used technique to measure cell motility and migration. Traditional methods of performing the wound healing assay suffer from low throughput and a lack of quantitative data analysis. We have developed a new method to perform a high-throughput wound healing assay that produces quantitative data using the LEAP™ instrument. The LEAP™ instrument is used to create reproducible wounds in each well of a 96-well plate by laser ablation. The LEAP™ then records bright field images of each well at several time points. A custom texture segmentation algorithm is used to determine the wound area of each well at each time point. This texture segmentation analysis can provide faster and more accurate image analysis than traditional methods. Experimental results show that reproducible wounds are created by laser ablation with a wound area that varies by less than 10%. This method was tested by confirming that neuregulin-2β increases the rate of wound healing by MCF7 cells in a dose dependent manner. This automated wound healing assay has greatly improved the speed and accuracy, making it a suitable high-throughput method for drug screening.

Combination of MRF model and particle swarm optimization algorithm for texture segmentation of remote sensing images

Markov Random Field ( MRF) model is a classical image texture analysis method, which has been widely used in image texture simulation and segmentation. However, when the classical simulated annealing algorithm is used to compute a global optimal solution, it cannot satisfy the performance requirements of texture analysis and processing due to less efficiency. A fitness function which could determine texture class was designed, and the use of Particle Swarm Optimization ( PSO) was proposed to calculate the optimal solution of it. Based on the proposed method, texture segmentation of remote sensing images was carried out. Compared with the simulated annealing algorithm, the experimental results show that PSO algorithm can reduce the computational complexity and it is a more effectively optimal method of image segmentation.

A new conception of image texture and remote sensing image segmentation based on Markov random field

The texture analysis is often discussed in image processing domain, but most methods are limited within gray-level image or color image, and the present conception of texture is defined mainly based on gray-level image of single band. One of the essential characters of remote sensing image is multidimensional or even high-dimensional, and the traditional texture conception cannot contain enough information for these. Therefore, it is necessary to pursuit a proper texture definition based on remote sensing images, which is the first discussion in this paper. This paper describes the mapping model of spectral vector in two-dimensional image space using Markov random field (MRF), establishes a texture model of multiband remote sensing image based on MRF, and analyzes the calculations of Gibbs potential energy and Gibbs parameters. Further, this paper also analyzes the limitations of the traditional Gibbs model, prefers a new Gibbs model avoiding estimation of parameters, and presents a new texture segmentation algorithm for hyperspectral remote sensing image later.

Paper texture segmentation algorithm based on peak-valley morphological feature

Paper texture segmentation algorithm based on peak-valley morphological feature

Multichannel Texture Segmentation Using Bamberger Pyramids

a multichannel texture segmentation algorithm is presented based on the image pyramids produced with the Bamberger directional filter bank. An extensive evaluation of Bamberger pyramids and their design parameters is presented. The impact on segmentation performance of factors like the number of pyramid levels, number of directional channels, redundancy and filter specifications is considered. The proposed system is shown to provide some of the best results reported to date when compared with other multichannel representations under similar evaluation conditions. It is further shown that segmentation results using the maximally decimated directional filter bank rival those of the undecimated case. To the knowledge of the authors, such performance has not been previously observed for decompositions with decimated channels.

Texture Segmentation by Genetic Programming

This paper describes a texture segmentation method using genetic programming (GP), which is one of the most powerful evolutionary computation algorithms. By choosing an appropriate representation texture, classifiers can be evolved without computing texture features. Due to the absence of time-consuming feature extraction, the evolved classifiers enable the development of the proposed texture segmentation algorithm. This GP based method can achieve a segmentation speed that is significantly higher than that of conventional methods. This method does not require a human expert to manually construct models for texture feature extraction. In an analysis of the evolved classifiers, it can be seen that these GP classifiers are not arbitrary. Certain textural regularities are captured by these classifiers to discriminate different textures. GP has been shown in this study as a feasible and a powerful approach for texture classification and segmentation, which are generally considered as complex vision tasks.

Signaltheoretische Analyse histologischer Daten im Ortsfrequenzraum

In the present study, a semi-automatic segmentation and classification algorithm is proposed for the analysis of histological and cytological images. In view of the fact that histological and cytological images usually exhibit poor contrast and blurred outlines, classical segmentation algorithms often fail to detect relevant structures. A new algorithm for texture segmentation based on signal processing methods in combination with machine learning techniques was therefore developed.

Spiking neural network and wavelets for hiding iris data in digital images

This paper introduces an efficient approach to protect the ownership by hiding the iris data into a digital image for authentication purposes. The idea is to secretly embed an iris code data into the content of the image, which identifies the owner. Algorithms based on Biologically inspired Spiking Neural Networks, called Pulse Coupled Neural Network (PCNN) are first applied to increase the contrast of the human iris image and adjust the intensity with the median filter. It is followed by the PCNN segmentation algorithm to determine the boundaries of the human iris image by locating the pupillary boundary and limbus boundary of the human iris for further processing. A texture segmentation algorithm for isolating the iris from the human eye in a more accurate and efficient manner is presented. A quad tree wavelet transform is first constructed to extract the texture feature. Then, the Fuzzy c-Means (FCM) algorithm is applied to the quad tree in the coarse-to-fine manner by locating the pupillary boundary (inner) and outer (limbus) boundary for further processing. Then, iris codes (watermark) are extracted that characterizes the underlying texture of the human iris by using wavelet theory. Then, embedding and extracting watermarking methods based on Discrete Wavelet Transform (DWT) to insert and extract the generated iris code are presented. The final process deals with the authentication process. In the authentication process, Hamming distance metric that measure the variation between the recorded iris code and the corresponding extracted one from the watermarked image (Stego image) to test weather the Stego image has been modified or not is presented. Simulation results show the effectiveness and efficiency of the proposed approach.