Relevant bibliographies by topics / Segmentation texture

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Segmentation texture'

Author: Grafiati
Published: 8 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Segmentation texture"

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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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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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Dissertations / Theses on the topic "Segmentation texture"

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Camilleri, Kenneth P. "Multiresolution texture segmentation." Thesis, University of Surrey, 1999. http://epubs.surrey.ac.uk/843549/.

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The problem of unsupervised texture segmentation was studied and a texture segmentation algorithm was developed making use of the minimum number of prior assumptions. In particular, no prior information about the type of textures, the number of textures and the appropriate scale of analysis for each texture was required. The texture image was analysed by the multiresolution Gabor expansion. The Gabor expansion generates a large number of features for each image and the most suitable feature space for segmentation needs to be determined automatically. The two-point correlation function was used
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Reyes-Aldasoro, Constantino Carlos. "Multiresolution volumetric texture segmentation." Thesis, University of Warwick, 2004. http://wrap.warwick.ac.uk/67756/.

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This thesis investigates the segmentation of data in 2D and 3D by texture analysis using Fourier domain filtering. The field of texture analysis is a well-trodden one in 2D, but many applications, such as Medical Imaging, Stratigraphy or Crystallography, would benefit from 3D analysis instead of the traditional, slice-by-slice approach. With the intention of contributing to texture analysis and segmentation in 3D, a multiresolution volumetric texture segmentation (M-VTS) algorithm is presented. The method extracts textural measurements from the Fourier domain of the data via sub-band filtering
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Linnett, L. M. "Multi-texture image segmentation." Thesis, Heriot-Watt University, 1991. http://hdl.handle.net/10399/856.

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Visual perception of images is closely related to the recognition of the different texture areas within an image. Identifying the boundaries of these regions is an important step in image analysis and image understanding. This thesis presents supervised and unsupervised methods which allow an efficient segmentation of the texture regions within multi-texture images. The features used by the methods are based on a measure of the fractal dimension of surfaces in several directions, which allows the transformation of the image into a set of feature images, however no direct measurement of the fra
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Porter, Robert Mark Stefan. "Texture classification and segmentation." Thesis, University of Bristol, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389032.

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Haddad, Stephen. "Texture measures for segmentation." Master's thesis, University of Cape Town, 2007. http://hdl.handle.net/11427/7461.

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Includes bibliographical references (p. 67-72).<br>Texture is an important visual cue in both human and computer vision. Segmenting images into regions of constant texture is used in many applications. This work surveys a wide range of texture descriptors and segmentation methods to determine the state of the art in texture segmentation. Two types of texture descriptors are investigated: filter bank based methods and local descriptors. Filter banks deconstruct an image into several bands, each of which emphasises areas of the image with different properties. Textons are an adaptive histogram m
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Pongratananukul, Nattorn. "Texture Segmentation Using Fractal Features." Honors in the Major Thesis, University of Central Florida, 2000. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/677.

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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf<br>Bachelors<br>Engineering<br>Electrical Engineering
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Wen, Wen. "Computational texture analysis and segmentation." Thesis, University of Strathclyde, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358812.

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Pandit, Sanjay. "Texture segmentation by global optimization." Thesis, University of Surrey, 1999. http://epubs.surrey.ac.uk/843855/.

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This thesis is concerned with the investigation of a specific approach to the problem of texture segmentation, namely that based on the global optimization of a cost function. Many tasks in image analysis are expressed as global optimization problems in which the general issue is to find the global minimum of a cost function which describes the interaction between the different variables modelling the image features and the interaction of these variables with the data in a given problem. The minimization of such a global cost function is a difficult problem since the number of hidden variables
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Tan, Tieniu. "Image texture analysis : classification and segmentation." Thesis, Imperial College London, 1990. http://hdl.handle.net/10044/1/8697.

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Xie, Zhi-Yan. "Multi-scale analysis and texture segmentation." Thesis, University of Oxford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.260776.

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Books on the topic "Segmentation texture"

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Camilleri, K. P. Approaches to unsupervised texture segmentation. Department of Electronic & Electrical Engineering, University of Surrey, 1997.

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Spann, Michael. Texture description and segmentation in image processing. University of Aston. Department of Electrical and Electronic Engineering, 1985.

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Fatemi-Ghomi, N. Texture segmentation using wavelet packets and c-means fuzzy clustering. Dept. of Electronic and Electrical Engineering, 1995.

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Anil, Phatak, Chatterji Gano, and Ames Research Center, eds. Scene segmentation of natural images using texture measures and back-propagation. National Aeronautics and Space Administration, Ames Research Center, 1993.

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Intelmann, Steven S. Automated, objective texture segmentation of multibeam echosounder data: Seafloor survey and substrate maps from James Island to Ozette Lake, Washington outer coast. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Ocean Service, National Marine Sanctuary Program, 2007.

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Jähne, Bernd. Digital image processing: Concepts, algorithms, and scientific applications. Springer-Verlag, 1991.

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Jähne, Bernd. Digital image processing: Concepts, algorithms, and scientific applications. 3rd ed. Springer-Verlag, 1995.

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Jähne, Bernd. Digital image processing: Concepts, algorithms, and scientific applications. 2nd ed. Springer-Verlag, 1993.

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University of Aston in Birmingham, ed. Texture description and segmentation in image processing. 1985.

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Radner, Hilary, and Alistair Fox. Film Analysis: Image and Movement. Edinburgh University Press, 2018. http://dx.doi.org/10.3366/edinburgh/9781474422888.003.0002.

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This chapter assesses Raymond Bellour’s contribution to the area of research known as “film analysis,” arguing that it is best understood as an “art” rather than a scientific practice. Grounded in the French tradition of “explication du texte” as a means of approaching literature, Bellour was among the first film scholars to bring a French literary sensibility to the analysis of Classical Hollywood film, which enabled him to recognize the rhetorical refinements of the cinematic medium and its potential for poetic expression. The chapter explores the significant concepts that define Bellour’s a
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Book chapters on the topic "Segmentation texture"

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Dana, Kristin J. "Texture Segmentation." In Computational Texture and Patterns. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01823-7_4.

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Bajcsy, P., and N. Ahuja. "Hierarchical texture segmentation." In Computer Vision — ACCV'98. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63931-4_229.

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Haindl, Michal. "Unsupervised texture segmentation." In Advances in Pattern Recognition. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0033333.

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Zwiggelaar, Reyer, and Erika R. E. Denton. "Texture Based Segmentation." In Digital Mammography. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11783237_58.

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Pietikäinen, Matti, Abdenour Hadid, Guoying Zhao, and Timo Ahonen. "Texture Classification and Segmentation." In Computational Imaging and Vision. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-748-8_4.

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Wang, Yuzhong, Jie Yang, and Yue Zhou. "Unsupervised Color-Texture Segmentation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30125-7_14.

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Haindl, Michal, and Stanislav Mikeš. "Model-Based Texture Segmentation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30126-4_38.

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Hung, Chih-Cheng, Enmin Song, and Yihua Lan. "Image Texture, Texture Features, and Image Texture Classification and Segmentation." In Image Texture Analysis. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13773-1_1.

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Jenicka, S. "Supervised Texture-Based Segmentation Using Basic Texture Models." In Land Cover Classification of Remotely Sensed Images. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66595-1_4.

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Ruiz-del-Solar, Javier, and Daniel Kottow. "Bio-inspired Texture Segmentation Architectures." In Biologically Motivated Computer Vision. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45482-9_45.

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Conference papers on the topic "Segmentation texture"

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Turner, Mark R. "Gabor functions and textural segmentation." In OSA Annual Meeting. Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.wj38.

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This paper investigates the applicability of Gabor functions to textural segmentation. Gabor functions are sinusoidal plane waves in 2-D Gaussian envelopes. The choice of parameters characterizing the geometry of an individual Gabor function affects its spatial extent as well as orientation and spatial frequency tuning. Daugman has indicated that these functions belong to a class of filters having optimal joint resolution in the 2-D space and 2-D frequency domains. They are, therefore, appropriate filter choices for tasks which require selective measurement in these domains. Textural segmentat
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McKeekin, M. Sue, and William T. Rhodes. "Texture segmentation by threshold decomposition filtering." In OSA Annual Meeting. Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.thr2.

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An optoelectronic system is described that can segment a natural scene into regions of different texture characteristics, independent of the illumination levels in those regions. The system consists of a cascade of a thresholding hard-limiting spatial light modulator with controllable threshold, a bank of coherent spatial filters designed for specific textures, a square-law operation (coherent-to-incoherent conversion), a low-pass incoherent spatial filter, and a final thresholding-hard-limiting operation. The scheme works because thresholding at the local texture median produces a binary dist
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Doretto, Cremers, Favaro, and Soatto. "Dynamic texture segmentation." In ICCV 2003: 9th International Conference on Computer Vision. IEEE, 2003. http://dx.doi.org/10.1109/iccv.2003.1238632.

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Haindl, Michal, and Stanislav Mikes. "Texture segmentation benchmark." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761118.

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Brown, C. David, Charles S. Ih, Gonzalo R. Arce, and David A. Fertell. "Scene segmentation using laser projected structured light." In OSA Annual Meeting. Optica Publishing Group, 1987. http://dx.doi.org/10.1364/oam.1987.mm3.

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A formidable task in many image processing applications is the segmentation of the scene into regions of interest. A major distinguishing feature between these regions is often the 3-D texture of these regions. Typical methods of textural image segmentation are very computationally intensive, often lack the required robustness, and are incapable of sensing the 3-D texture of various regions of the scene. Scanned laser lines of structured light viewed by a stereoscopically located single video camera result in an image in which the 3-D characteristics of the scene are represented by the discont
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Wilscy, M., and Remya K. Sasi. "Wavelet based Texture Segmentation." In 2010 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2010. http://dx.doi.org/10.1109/iccic.2010.5705877.

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Barilla, Maria E., Manuel G. Forero, and Michael Spann. "Colour-based texture segmentation." In INFORMATION OPTICS: 5th International Workshop on Information Optics (WIO'06). AIP, 2006. http://dx.doi.org/10.1063/1.2361244.

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Zhan, De-Chen, Xuan-Jing Shen, Jing-Chun Chen, and Zhong-Rong Li. "Fast texture image segmentation." In San Diego '90, 8-13 July, edited by Andrew G. Tescher. SPIE, 1990. http://dx.doi.org/10.1117/12.23536.

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Todorovic, Sinisa, and Narendra Ahuja. "Texel-based texture segmentation." In 2009 IEEE 12th International Conference on Computer Vision (ICCV). IEEE, 2009. http://dx.doi.org/10.1109/iccv.2009.5459308.

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Giraud, Remi, Vinh-Thong Ta, Nicolas Papadakis, and Yannick Berthoumieu. "Texture-Aware Superpixel Segmentation." In 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019. http://dx.doi.org/10.1109/icip.2019.8803085.

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Reports on the topic "Segmentation texture"

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Du, Li-Jen. Segmentation of Synthetic Aperture Radar (SAR) Images of Ocean Surface by the Texture Energy Transform Method. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada199536.

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Koop, L., M. Snellen, and D. Simons. Classifying the seafloor using a textural segmentation approach. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2017. http://dx.doi.org/10.4095/305875.

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Ward, Anderson, Gamal Seedahmend, Greg Anderson, and Fred Zhang. Integration of Multi-Tension Permeametry and Photogrammetric Textural Segmentation for Estimating Directional Permeability. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada571373.

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