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Journal articles on the topic 'Segmentation texture'

Author: Grafiati

Published: 8 June 2024

Last updated: 31 July 2025

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1

Volkova, Natalya P., and Viktor N. Krylov. "VECTOR-DIFFERENCE TEXTURE SEGMENTATION METHOD IN TECHNICAL AND MEDICAL EXPRESS DIAGNOSTIC SYSTEMS." Herald of Advanced Information Technology 3, no. 4 (2020): 226–39. http://dx.doi.org/10.15276/hait.04.2020.2.

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The study shows the need for express systems, in which it is necessary to perform the analysis of texture images in various areas of diagnosis, for example, in medical express diagnostics of dermatologic disorders. Since the reliability of decision-making in such systems depends on the quality of image segmentation, which, as a rule, have the nature of spectral-statistical textures, it is advisable to develop methods for segmentation of such images and models for their presentation. A model of spectral-statistical texture is proposed, which takes into account the random nature of changes in th

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2

Soares, Lucas de Assis, Klaus Fabian Côco, Patrick Marques Ciarelli, and Evandro Ottoni Teatini Salles. "A Class-Independent Texture-Separation Method Based on a Pixel-Wise Binary Classification." Sensors 20, no. 18 (2020): 5432. http://dx.doi.org/10.3390/s20185432.

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Texture segmentation is a challenging problem in computer vision due to the subjective nature of textures, the variability in which they occur in images, their dependence on scale and illumination variation, and the lack of a precise definition in the literature. This paper proposes a method to segment textures through a binary pixel-wise classification, thereby without the need for a predefined number of textures classes. Using a convolutional neural network, with an encoder–decoder architecture, each pixel is classified as being inside an internal texture region or in a border between two di

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Song, Andy, and Vic Ciesielski. "Texture Segmentation by Genetic Programming." Evolutionary Computation 16, no. 4 (2008): 461–81. http://dx.doi.org/10.1162/evco.2008.16.4.461.

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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 constr

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Tiwari, Shamik, Akhilesh Kumar Sharma, Izzatdin Abdul Aziz, et al. "Investigations on segmentation-based fractal texture for texture classification in the presence of Gaussian noise." PLOS ONE 20, no. 1 (2025): e0315135. https://doi.org/10.1371/journal.pone.0315135.

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Texture is a significant component used for several applications in content-based image retrieval. Any texture classification method aims to map an anonymously textured input image to one of the existing texture classes. Extensive ranges of methods for labeling image texture were proposed earlier. However, computing the performance of these methods in the presence of various degradations is always an open area of discussion. Image noise is always a dominant factor among various image degradation factors, affecting the performance of these methods and making texture classification challenging.

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Rosenholtz, R. "What Statistics Determine Segmentation of Orientation-Defined Textures?" Perception 26, no. 1_suppl (1997): 331. http://dx.doi.org/10.1068/v970074.

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Beck suggested that texture segmentation is based upon differences in the first-order statistics of stimulus features such as orientation, size, and contrast. However, this theory does not indicate how these differences might be quantified, or what properties of the statistics might be used. Some alternative models postulate that texture segmentation is determined by the responses of spatial-frequency channels, where the channels contain both a linear filtering mechanism and various nonlinearities. Such models do a good job of predicting human performance, but do not give us much insight into

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Wang, Guodong, Zhenkuan Pan, Qian Dong, Ximei Zhao, Zhimei Zhang, and Jinming Duan. "Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information." Journal of Applied Mathematics 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/614613.

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Textures often occur in real-world images and may cause considerable difficulties in image segmentation. In order to segment texture images, we propose a new segmentation model that combines image decomposition model and active contour model. The former model is capable of decomposing structural and oscillating components separately from texture image, and the latter model can be used to provide smooth segmentation contour. In detail, we just replace the data term of piecewise constant/smooth approximation in CCV (convex Chan-Vese) model with that of image decomposition model-VO (Vese-Osher).

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Kinge, Sanjaykumar, B. Sheela Rani, and Mukul Sutaone. "Restored texture segmentation using Markov random fields." Mathematical Biosciences and Engineering 20, no. 6 (2023): 10063–89. http://dx.doi.org/10.3934/mbe.2023442.

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<abstract> <p>Texture segmentation plays a crucial role in the domain of image analysis and its recognition. Noise is inextricably linked to images, just like it is with every signal received by sensing, which has an impact on how well the segmentation process performs in general. Recent literature reveals that the research community has started recognizing the domain of noisy texture segmentation for its work towards solutions for the automated quality inspection of objects, decision support for biomedical images, facial expressions identification, retrieving image data from a hug

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Goyal, Aparna, and Reena Gunjan. "Bleeding Detection in Gastrointestinal Images using Texture Classification and Local Binary Pattern Technique: A Review." E3S Web of Conferences 170 (2020): 03007. http://dx.doi.org/10.1051/e3sconf/202017003007.

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Texture analysis has proven to be a breakthrough in many applications of computer image analysis. It has been used for classification or segmentation of images which requires an effective description of image texture. Due to high discriminative power and simplicity of computation, the local binary pattern descriptors have been used for distinguishing different textures and in extracting texture and color in medical images. This paper discusses performance of various texture classification techniques using Contourlet Transform, Discrete Fourier Transform, Local Binary Patterns and Lacunarity an

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Tripathi, Bharat, Nidhi Srivastava, and Amod Kumar Tiwari. "Innovative quaternion algebra-based segmentation for improved jpeg color texture analysis." Journal of Autonomous Intelligence 7, no. 5 (2024): 1499. http://dx.doi.org/10.32629/jai.v7i5.1499.

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<p>Color texture analysis is a critical component in various computer vision and image processing applications, including object recognition, medical imaging, and remote sensing. Traditional methods like J-Segmentation for color texture segmentation often struggle with capturing complex textures and maintaining color fidelity, especially when dealing with JPEG-compressed images. Quaternion Algebra’s unique ability to represent and manipulate color information in a multidimensional space allows for more accurate feature extraction and segmentation. Our approach Quaternion neural network (

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Anjaiah, P., K. Rajendra Prasad, and C. Raghavendra. "Effective Texture Features for Segmented Mammogram Images." International Journal of Engineering & Technology 7, no. 3.12 (2018): 666. http://dx.doi.org/10.14419/ijet.v7i3.12.16450.

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Textures of mammogram images are useful for finding masses or cancer cases in mammography, which has been used by radiologist. Textures are greatly succeed for segmented images rather than normal images. It is necessary to perform segmentation for exclusive specification of cancer and non-cancer regions separately. Region of interest (ROI) in most commonly used technique for mammogram segmentation. Limitation of this method is that it unable to explore segmentation for large collection of mammogram images. Therefore, this paper is proposed multi-ROI segmentation for addressing the above limita

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Hao, Xinjie, Jiahui Wang, Wei Leng, Rongting Zhang, and Guangyun Zhang. "MFSM-Net: Multimodal Feature Fusion for the Semantic Segmentation of Urban-Scale Textured 3D Meshes." Remote Sensing 17, no. 9 (2025): 1573. https://doi.org/10.3390/rs17091573.

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The semantic segmentation of textured 3D meshes is a critical step in constructing city-scale realistic 3D models. Compared to colored point clouds, textured 3D meshes have the advantage of high-resolution texture image patches embedded on each mesh face. However, existing studies predominantly focus on their geometric structures, with limited utilization of these high-resolution textures. Inspired by the binocular perception of humans, this paper proposes a multimodal feature fusion network based on 3D geometric structures and 2D high-resolution texture images for the semantic segmentation of

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Manokhin, Denys, and Yaroslav Sokolovskyy. "Intracranial Hemorrhage Segmentation using Neural Network and Riesz Fractional Order Derivative-Based Texture Enhancement." Computer Design Systems. Theory and Practice 6, no. 1 (2024): 1–16. http://dx.doi.org/10.23939/cds2024.01.001.

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This paper explores the application of the U-Net architecture for intracranial hemorrhage segmentation, with a focus on enhancing segmentation accuracy through the incorporation of texture enhancement techniques based on the Riesz fractional order derivatives. The study begins by conducting a review of related works in the field of computed tomography (CT) scan segmentation. At this stage also a suitable dataset is selected. Initially it is used to train the UNet, one of the widely adopted deep learning models in the field of medical image segmentation. Training is performed using parallel alg

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Xiang, Ming, Zhen Dong Cui, and Yuan Hong Wu. "A Fingerprint Image Segmentation Method Based on Fractal Dimension." Advanced Materials Research 461 (February 2012): 299–301. http://dx.doi.org/10.4028/www.scientific.net/amr.461.299.

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Fractal analysis is becoming more and more popular in image segmentation community, in which the box-counting based fractal dimension estimations are most commonly used. In this paper, a novel fractal estimation algorithm is proposed. Both the proposed algorithm and the box-counting based methods have been applied to the segmentation of texture images. The comparison results demonstrate that the fractal estimation can differentiate texture images more effectively and provide more robust segmentations

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Kumar, Upendra. "Significant Enhancement of Segmentation Efficiency of Retinal Images Using Texture-Based Gabor Filter Approach Followed by Optimization Algorithm." International Journal of Computer Vision and Image Processing 7, no. 1 (2017): 44–58. http://dx.doi.org/10.4018/ijcvip.2017010103.

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Considering Retinal image as textured image, its texture based segmentation is required to identify the presence of retinal diseases. This pre-processing is important in automatic detection system for recognizing the abnormality present in the retinal images. Likewise, the proposed system mainly focused on diabetic retinopathy disease caused into eye –retina, generally leads to eye-blindness. Inspired from robust human's texture based segmentation capability, a mathematical model of the eye was formulated. A texture based Gabor filter was applied to get the output feature helping in detecting

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LIU, Guoxiang, and Shunichiro OE. "Texture Image Segmentation by Detecting Texture Edges." IEEJ Transactions on Electronics, Information and Systems 122, no. 5 (2002): 808–15. http://dx.doi.org/10.1541/ieejeiss1987.122.5_808.

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Harrison, S. J., and D. R. T. Keeble. "Within-texture alignment improves human texture segmentation." Ophthalmic and Physiological Optics 22, no. 6 (2002): 580–81. http://dx.doi.org/10.1046/j.1475-1313.2002.00086_32.x.

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Harrison, S. J., and D. R. T. Keeble. "Within-texture collinearity improves human texture segmentation." Vision Research 48, no. 19 (2008): 1955–64. http://dx.doi.org/10.1016/j.visres.2008.06.008.

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K., Prabhakar, and Sadyojatha K.M. "Multi-Resolution Feature Embedded Level Set Model for Crosshatched Texture Segmentation." International journal of electrical and computer engineering systems 14, no. 4 (2023): 371–79. http://dx.doi.org/10.32985/ijeces.14.4.1.

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In image processing applications, texture is the most important element utilized by human visual systems for distinguishing dissimilar objects in a scene. In this research article, a variational model based on the level set is implemented for crosshatched texture segmentation. In this study, the proposed model’s performance is validated on the Brodatz texture dataset. The cross-hatched texture segmentation in the lower resolution texture images is difficult, due to the computational and memory requirements. The aforementioned issue has been resolved by implementing a variational model based on

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Liu, Xinlin, Viktor Krylov, Su Jun, et al. "Segmentation and identification of spectral and statistical textures for computer medical diagnostics in dermatology." Mathematical Biosciences and Engineering 19, no. 7 (2022): 6923–39. http://dx.doi.org/10.3934/mbe.2022326.

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<abstract> <p>An important component of the computer systems of medical diagnostics in dermatology is the device for recognition of visual images (DRVI), which includes identification and segmentation procedures to build the image of the object for recognition. In this study, the peculiarities of the application of detection, classification and vector-difference approaches for the segmentation of textures of different types in images of dermatological diseases were considered. To increase the quality of segmented images in dermatologic diagnostic systems using a DRVI, an improved v

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Fan, Lin, Zi-long Deng, Yan He, Xu-long Zhu, Xing-jun Gao, and Zhe Jin. "The effects of micro-texture shape on serrated chip geometry in the hardened steel AISI D2 cutting process." Surface Topography: Metrology and Properties 10, no. 1 (2022): 015031. http://dx.doi.org/10.1088/2051-672x/ac58ad.

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Abstract Five micro-textures were processed on the rake face of PCBN tools: circular pits, elliptical grooves, transverse grooves, composite grooves, and wavy grooves. The effect of different micro-textures on cutting force, the cutting temperature, the micro-morphology of serrated chips, tool wear and surface roughness were investigated utilizing three-dimensional finite element simulation and cutting tests. The results indicates that micro-textured tools can lower cutting force when compared to non-textured tools, while cutting force varies significantly during the cutting process. Micro-tex

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Yuan, Jiangye, Deliang Wang, and Anil M. Cheriyadat. "Factorization-Based Texture Segmentation." IEEE Transactions on Image Processing 24, no. 11 (2015): 3488–97. http://dx.doi.org/10.1109/tip.2015.2446948.

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22

Appelbaum, G., A. Wade, M. Pettet, V. Vildavski, and A. M. Norcia. "Dynamics of texture segmentation." Journal of Vision 5, no. 12 (2005): 12. http://dx.doi.org/10.1167/5.12.12.

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23

Shi, Lilong, and Brian Funt. "Quaternion color texture segmentation." Computer Vision and Image Understanding 107, no. 1-2 (2007): 88–96. http://dx.doi.org/10.1016/j.cviu.2006.11.014.

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24

Tuceryan, Mihran. "Moment-based texture segmentation." Pattern Recognition Letters 15, no. 7 (1994): 659–68. http://dx.doi.org/10.1016/0167-8655(94)90069-8.

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Li, Xinhui, Mingjia Li, Yaxing Wang, Chuan-Xian Ren, and Xiaojie Guo. "Adaptive Texture Filtering for Single-Domain Generalized Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (2023): 1442–50. http://dx.doi.org/10.1609/aaai.v37i2.25229.

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Domain generalization in semantic segmentation aims to alleviate the performance degradation on unseen domains through learning domain-invariant features. Existing methods diversify images in the source domain by adding complex or even abnormal textures to reduce the sensitivity to domain-specific features. However, these approaches depends heavily on the richness of the texture bank and training them can be time-consuming. In contrast to importing textures arbitrarily or augmenting styles randomly, we focus on the single source domain itself to achieve the generalization. In this paper, we pr

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Peromaa, T.-L., and P. I. Laurinen. "An Illusory Contour Induces Texture Segmentation." Perception 26, no. 1_suppl (1997): 310. http://dx.doi.org/10.1068/v970086.

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It is known that neurons in V1 can signal a contour between two out-of-phase gratings [Grosof et al, 1993 Nature (London)365 550 – 552]. We demonstrate that this type of illusory contour can segregate areas of surfaces without any luminance, contrast, or textural difference between the areas. We have studied the conditions under which the illusory contour induces texture segmentation. The target was a circular contrast-inverted area (diameter 0.22 – 8.3 deg) in the centre of an isotropic narrow-band noise texture (centre spatial frequency 0.4 – 7 cycles deg−1). Generally, segmentation was effo

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Palagi, P. M., and A. Guérin-Dugué. "Simulation of Cortex Visual Cells for Texture Segmentation: Foveal and Parafoveal Projections." Perception 25, no. 1_suppl (1996): 120. http://dx.doi.org/10.1068/v96l0708.

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The objective of this work is to simulate visual cortical cells, their sensitivities to frequencies and orientations, and their part in texture segmentation. The simulation of these cells is realised through band-pass, oriented filters (Gabor filters), and multiresolution image decomposition. By this means, the filter sensitivities represent cell sensitivities to preferred orientations according to their frequency and orientation bandwidths, and multiresolution represents the different band frequencies. For texture analysis and segmentation, overlaying of band-pass filters is necessary to comp

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Zhu, Jinchao, Xiaoyu Zhang, Shuo Zhang, and Junnan Liu. "Inferring Camouflaged Objects by Texture-Aware Interactive Guidance Network." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (2021): 3599–607. http://dx.doi.org/10.1609/aaai.v35i4.16475.

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Camouflaged objects, similar to the background, show indefinable boundaries and deceptive textures, which increases the difficulty of detection task and makes the model rely on features with more information. Herein, we design a texture label to facilitate our network for accurate camouflaged object segmentation. Motivated by the complementary relationship between texture labels and camouflaged object labels, we propose an interactive guidance framework named TINet, which focuses on finding the indefinable boundary and the texture difference by progressive interactive guidance. It maximizes th

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Berezina, S. I., Yu O. Gordienko, and O. I. Solonets. "ANALYSIS OF WAYS OF SOLVING THE SEGMENTATION PROBLEM FOR HIGHLY TEXTURED OBJECTS." Проблеми створення, випробування, застосування та експлуатації складних інформаційних систем, no. 17 (December 30, 2019): 27–40. http://dx.doi.org/10.46972/2076-1546.2019.17.03.

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Increment of speed and reliability of aerospace images processing is directly related to solution of the task of automation of images interpretation process, which is achieved by minimizing search areas, detecting masked objects and defining the dynamics of changes in surveillance areas. The primary stage that in general determines the quality of results received by automated processing and interpretation is thematic segmentation of the image. In the process of thematic segmentation it is necessary to take into account presence of a large number of textured objects. The paper analyzes the ways

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Wang, Zhan, Yun Hui Yan, De Wei Dong, and Ke Chen Song. "Texture Segmentation of Natural Images Based on Active Contour Model." Advanced Materials Research 546-547 (July 2012): 553–58. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.553.

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To segment complex texture natural environment images; the first, the texture features of natural images should be analysed and the texture features should be extracted; The second, texture images segmengtation can be achieved by using Mumford-Shah active contour model, this segmentation model can better process fuzzy, default boundary, and this model can be solved by level set method. This method can express well complex texture signal features of natural images. Through making texture segmentation experiment for standard texture synthesis image and natural environmental image, its results sh

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Salhi, Khalid, El Miloud Jaara, and Mohammed Talibi Alaoui. "Texture Image Segmentation Approach Based on Neural Networks." International Journal of Recent Contributions from Engineering, Science & IT (iJES) 6, no. 1 (2018): 19. http://dx.doi.org/10.3991/ijes.v6i1.8166.

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One of the major problems in texture analysis is segmenting images into different regions based on textures. In this paper, we present a new approach of texture segmentation, which is based on both Kohonen maps and mathematical morphology, using three different texture features, namely, Haralick features based on gray-level co-occurrence matrix (GLCM), fractal features based on fractal dimension using the differential box counting method, and wavelet features based on wavelet transform. These features are used to train the Kohonen Network, which will be represented by the underlying probabilit

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Yessenova, Moldir, Gulzira Abdikerimova, Nurgul Baitemirova, et al. "The applicability of informative textural features for the detection of factors negatively influencing the growth of wheat on aerial images." Eastern-European Journal of Enterprise Technologies 4, no. 2(118) (2022): 51–58. http://dx.doi.org/10.15587/1729-4061.2022.263433.

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Automated processing of aerospace information makes it possible to effectively solve scientific and applied problems in cartography, ecology, oceanology, exploration and development of minerals, agriculture and forestry, and many other areas. At the same time, the main way to extract information is to decipher images, which are the main carrier of information about the area. Aerospace images are a combination of natural texture regions and man-made objects. This article discusses methods for analyzing texture images. The main tasks of the analysis of texture areas include the selection and for

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Moldir, Yessenova, Abdikerimova Gulzira, Baitemirova Nurgul, et al. "The applicability of informative textural features for the detection of factors negatively influencing the growth of wheat on aerial images." Eastern-European Journal of Enterprise Technologies 4, no. 2 (118) (2022): 51–58. https://doi.org/10.15587/1729-4061.2022.263433.

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Automated processing of aerospace information makes it possible to effectively solve scientific and applied problems in cartography, ecology, oceanology, exploration and development of minerals, agriculture and forestry, and many other areas. At the same time, the main way to extract information is to decipher images, which are the main carrier of information about the area. Aerospace images are a combination of natural texture regions and man-made objects. This article discusses methods for analyzing texture images. The main tasks of the analysis of texture areas include the selection and for

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Kang, Junhua, Fei Deng, Xinwei Li, and Fang Wan. "AUTOMATIC TEXTURE RECONSTRUCTION OF 3D CITY MODEL FROM OBLIQUE IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 341–47. http://dx.doi.org/10.5194/isprsarchives-xli-b1-341-2016.

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In recent years, the photorealistic 3D city models are increasingly important in various geospatial applications related to virtual city tourism, 3D GIS, urban planning, real-estate management. Besides the acquisition of high-precision 3D geometric data, texture reconstruction is also a crucial step for generating high-quality and visually realistic 3D models. However, most of the texture reconstruction approaches are probably leading to texture fragmentation and memory inefficiency. In this paper, we introduce an automatic framework of texture reconstruction to generate textures from oblique

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Kang, Junhua, Fei Deng, Xinwei Li, and Fang Wan. "AUTOMATIC TEXTURE RECONSTRUCTION OF 3D CITY MODEL FROM OBLIQUE IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 341–47. http://dx.doi.org/10.5194/isprs-archives-xli-b1-341-2016.

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In recent years, the photorealistic 3D city models are increasingly important in various geospatial applications related to virtual city tourism, 3D GIS, urban planning, real-estate management. Besides the acquisition of high-precision 3D geometric data, texture reconstruction is also a crucial step for generating high-quality and visually realistic 3D models. However, most of the texture reconstruction approaches are probably leading to texture fragmentation and memory inefficiency. In this paper, we introduce an automatic framework of texture reconstruction to generate textures from oblique

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SHIRVAIKAR, MUKUL V., and MOHAN M. TRIVEDI. "TEXTURE SEGMENTATION: AN UNSUPERVISED APPROACH." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 03, no. 04 (1995): 431–49. http://dx.doi.org/10.1142/s0218488595000220.

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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 algorith

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MUNEESWARAN, K., L. GANESAN, S. ARUMUGAM, and P. HARINARAYAN. "A NOVEL APPROACH COMBINING GABOR WAVELET AND MOMENTS FOR TEXTURE SEGMENTATION." International Journal of Wavelets, Multiresolution and Information Processing 03, no. 04 (2005): 559–72. http://dx.doi.org/10.1142/s0219691305001020.

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In this work, an effective method has been proposed for texture segmentation, which incorporates the best features of filter bank and statistical approaches. This technique combines the features of Gabor wavelets (filter based) and General Moments (statistical) approaches. The method has been successfully tested for various textures from Brodatz texture collection. The relative performance of this method against the conventional approaches has been analyzed using Fisher Criterion.

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S.Md, Mansoor Roomi, Mareeswari M, and Maragatham G. "Segmentation Using ‘New’ Texture Feature." Advanced Computing: An International Journal 7, no. 1/2 (2016): 39–49. http://dx.doi.org/10.5121/acij.2016.7205.

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Chaudhuri, B. B., and N. Sarkar. "Texture segmentation using fractal dimension." IEEE Transactions on Pattern Analysis and Machine Intelligence 17, no. 1 (1995): 72–77. http://dx.doi.org/10.1109/34.368149.

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Tuceryan, M., and A. K. Jain. "Texture segmentation using Voronoi polygons." IEEE Transactions on Pattern Analysis and Machine Intelligence 12, no. 2 (1990): 211–16. http://dx.doi.org/10.1109/34.44407.

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Wu, Jiang, and Chunxiao Liu. "Superpixel segmentation with texture awareness." Journal of Image and Graphics 26, no. 5 (2021): 1006–16. http://dx.doi.org/10.11834/jig.200259.

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Jacquelin, Christophe, André Aurengo, and Gilles Hejblum. "Evolving descriptors for texture segmentation." Pattern Recognition 30, no. 7 (1997): 1069–79. http://dx.doi.org/10.1016/s0031-3203(96)00150-1.

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Farran, Emily K., and Kate Wilmut. "Texture segmentation in Williams syndrome." Neuropsychologia 45, no. 5 (2007): 1009–18. http://dx.doi.org/10.1016/j.neuropsychologia.2006.09.005.

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Arivazhagan, S., and L. Ganesan. "Texture segmentation using wavelet transform." Pattern Recognition Letters 24, no. 16 (2003): 3197–203. http://dx.doi.org/10.1016/j.patrec.2003.08.005.

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Hoang, Minh A., Jan-Mark Geusebroek, and Arnold W. M. Smeulders. "Color texture measurement and segmentation." Signal Processing 85, no. 2 (2005): 265–75. http://dx.doi.org/10.1016/j.sigpro.2004.10.009.

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