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Articoli di riviste sul tema "Segmentation texture":
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Volkova, Natalya P., e Viktor N. Krylov. "VECTOR-DIFFERENCE TEXTURE SEGMENTATION METHOD IN TECHNICAL AND MEDICAL EXPRESS DIAGNOSTIC SYSTEMS". Herald of Advanced Information Technology 3, n. 4 (20 novembre 2020): 226–39. http://dx.doi.org/10.15276/hait.04.2020.2.
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 the field variations and quasi-harmonics. On its basis, a vector-difference method of texture segmentation has been developed, which is based on the vector transformation of images of spectral and statistical textures based on vector algebra. The stages of the vector-difference method are the following: an evaluation of the spectral texture feature; an evaluation of the statistical texture feature; vector-difference transformation of texture images; a boundary detection of the homogeneous regions. For each pixel of the image in the processing aperture, the features of the spectral and statistical texture are evaluated. Statistical texture evaluation was performed by the quadratic-amplitude transformation. At the stage of vector-difference transformation of texture images, a vector of features of each pixel of an image is constructed, the elements of which are estimates of features of a spectral and statistical texture, and the modulus of the difference of two vectors is calculated. At the stage of boundary detection of homogeneous regions, Canny method was applied. The developed vector-difference texture segmentation method was applied both to model images of spectral-statistical texture and to texture images obtained in technical and medical diagnostics systems, namely, for images of psoriasis disease and wear zones of cutting tools. To compare the segmentation results, frequency-detector and amplitude-detector methods of texture segmentation were applied to these images. The quality of segmentation of homogeneous textured regions was evaluated by the Pratt's criterion and by constructing a confusion matrix. The research results showed that the developed vector-difference texture segmentation method has increased noise tolerance at a sufficient processing speed.
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Song, Andy, e Vic Ciesielski. "Texture Segmentation by Genetic Programming". Evolutionary Computation 16, n. 4 (dicembre 2008): 461–81. http://dx.doi.org/10.1162/evco.2008.16.4.461.
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.
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Soares, Lucas de Assis, Klaus Fabian Côco, Patrick Marques Ciarelli e Evandro Ottoni Teatini Salles. "A Class-Independent Texture-Separation Method Based on a Pixel-Wise Binary Classification". Sensors 20, n. 18 (22 settembre 2020): 5432. http://dx.doi.org/10.3390/s20185432.
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 different textures. The network is trained using the Prague Texture Segmentation Datagenerator and Benchmark and tested using the same dataset, besides the Brodatz textures dataset, and the Describable Texture Dataset. The method is also evaluated on the separation of regions in images from different applications, namely remote sensing images and H&E-stained tissue images. It is shown that the method has a good performance on different test sets, can precisely identify borders between texture regions and does not suffer from over-segmentation.
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Rosenholtz, R. "What Statistics Determine Segmentation of Orientation-Defined Textures?" Perception 26, n. 1_suppl (agosto 1997): 331. http://dx.doi.org/10.1068/v970074.
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 what textures will segment, since the comparison carried out by the model is often obscured by the details of the filtering, nonlinearity, and image-based decision processes. It is suggested here that, for orientation-defined textures (eg in which each ‘texel’ has a single orientation), segmentation is well-described by something like the ‘significance’ of the differences between (1) the mean orientations, and (2) the angular variances of the two textures. The ‘significance’ of the difference in means takes into account the variability in the texture, so that two homogeneous textures with means differing by 30° may easily segment, while two heterogeneous textures with the same difference in mean may not. Furthermore, it is shown that these statistics may be computed in a biologically plausible way, which greatly resembles the typical filter-based approaches to texture segmentation. Thus the connection between statistical theories of texture segmentation and spatial-frequency channel models becomes more transparent.
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Wang, Guodong, Zhenkuan Pan, Qian Dong, Ximei Zhao, Zhimei Zhang e 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.
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). Therefore, our proposed model can estimate both structural and oscillating components of texture images as well as segment textures simultaneously. In addition, we design fast Split-Bregman algorithm for our proposed model. Finally, the performance of our method is demonstrated by segmenting some synthetic and real texture images.
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Kinge, Sanjaykumar, B. Sheela Rani e Mukul Sutaone. "Restored texture segmentation using Markov random fields". Mathematical Biosciences and Engineering 20, n. 6 (2023): 10063–89. http://dx.doi.org/10.3934/mbe.2023442.
<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 huge dataset and many others. Motivated by the latest work on noisy textures, during our work being presented here, Brodatz and Prague texture images are contaminated with Gaussian and salt-n-pepper noise. A three-phase approach is developed for the segmentation of textures contaminated by noise. In the first phase, these contaminated images are restored using techniques with excellent performance as per the recent literature. In the remaining two phases, segmentation of the restored textures is carried out by a novel technique developed using Markov Random Fields (MRF) and objective customization of the Median Filter based on segmentation performance metrics. When the proposed approach is evaluated on Brodatz textures, an improvement of up to 16% segmentation accuracy for salt-n-pepper noise with 70% noise density and 15.1% accuracy for Gaussian noise (with a variance of 50) has been made in comparison with the benchmark approaches. On Prague textures, accuracy is improved by 4.08% for Gaussian noise (with variance 10) and by 2.47% for salt-n-pepper noise with 20% noise density. The approach in the present study can be applied to a diversified class of image analysis applications spanning a wide spectrum such as satellite images, medical images, industrial inspection, geo-informatics, etc.</p> </abstract>
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Goyal, Aparna, e 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.
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 analysis. The study reveals that the incorporation of efficient image segmentation, enhancement and texture classification using local binary pattern descriptor detects bleeding region in human intestines precisely.
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Anjaiah, P., K. Rajendra Prasad e C. Raghavendra. "Effective Texture Features for Segmented Mammogram Images". International Journal of Engineering & Technology 7, n. 3.12 (20 luglio 2018): 666. http://dx.doi.org/10.14419/ijet.v7i3.12.16450.
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 limitation. It supports greatly for finding best texture features of mammogram images. Experimental study demonstrates the effectiveness of proposed work using benchmarked images.
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Tripathi, Bharat, Nidhi Srivastava e Amod Kumar Tiwari. "Innovative quaternion algebra-based segmentation for improved jpeg color texture analysis". Journal of Autonomous Intelligence 7, n. 5 (10 aprile 2024): 1499. http://dx.doi.org/10.32629/jai.v7i5.1499.
<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 (QNN) not only improves the segmentation accuracy but also preserves the visual quality of the segmented regions in JPEG images. We demonstrate the effectiveness of our method through extensive experimentation on diverse datasets, showcasing its superiority over existing techniques. The proposed approach not only achieves state-of-the-art results in terms of segmentation accuracy but also offers computational efficiency. This innovation holds great promise for applications in image analysis, computer vision, and medical imaging, where accurate color texture segmentation is paramount.</p>
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Manokhin, Denys, e Yaroslav Sokolovskyy. "Intracranial Hemorrhage Segmentation using Neural Network and Riesz Fractional Order Derivative-Based Texture Enhancement". Computer Design Systems. Theory and Practice 6, n. 1 (2024): 1–16. http://dx.doi.org/10.23939/cds2024.01.001.
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 algorithm based on CUDA technology. The obtained results are compared with the established baseline for this dataset, assessing segmentation accuracy using the Jaccard and Dice coefficients. Subsequently, the study investigates a texture enhancement technique based on the Riesz fractional order derivatives, applied to the CT-scan images from the dataset. This technique aims to capture finer details and subtle textures that may contribute to improved segmentation accuracy. The U-Net model is then retrained and validated on the texture-enhanced images, and the experimental results are analyzed. The study reveals a modest yet notable enhancement in accuracy, as measured by the Jaccard and Dice coefficients, demonstrating the potential of the proposed texture enhancement technique in refining intracranial hemorrhage segmentation.
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 to test the separability of the distributions in each feature space. A measure was developed to evaluate evidence of multiple clusters from the two-point correlation function, making it possible to determine the most suitable feature space for clustering. Thus, at a given resolution level, the most appropriate feature space was selected and used to segment the image. Due to inherent ambiguities and limitations of the two-point correlation function, this feature space exploration and segmentation was performed several times at the same resolution level until no further evidence of multiple clusters was found, at which point, the process was repeated at the next finer resolution level. In this way, the image was progressively segmented, proceeding from coarse to fine Gabor resolution levels without any knowledge of the actual number of textures present. In order to refine the region-labelled image obtained at the end of the segmentation process, two postprocessing pixel-level algorithms were developed and implemented. The first was the mixed pixel classification algorithm which is based on the analysis of the effect of the averaging window at the boundary between two regions and re-assigns the pixel labels to improve the boundary localisation. Multiresolution probabilistic relaxation is the second postprocessing algorithm which we developed. This algorithm incorporates contextual evidence to relabel pixels close to the boundary in order to smooth it and improve its localisation. The results obtained were quantified by known error measures, as well as by new error measures which we developed. The quantified results were compared to similar results by other authors and show that our unsupervised algorithm performs as well as other methods which assume prior information.
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Reyes-Aldasoro, Constantino Carlos. "Multiresolution volumetric texture segmentation". Thesis, University of Warwick, 2004. http://wrap.warwick.ac.uk/67756/.
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 using a Second Orientation Pyramid. A novel Bhattacharyya space, based on the Bhattacharyya distance is proposed for selecting of the most discriminant measurements and produces a compact feature space. Each dimension of the feature space is used to form a Quad Tree. At the highest level of the tree, new positional features are added to improve the contiguity of the classification. The classified space is then projected to lower levels of the tree where a boundary refinement procedure is performed with a 3D equivalent of butterfly filters. The performance of M-VTS is tested in 2D by classifying a set of standard texture images. The figures contain different textures that are visually stationary. M-VTS yields lower misclassification rates than reported elsewhere ([104, 111, 124]). The algorithm was tested in 3D with artificial isotropic data and three Magnetic Resonance Imaging sets of human knees with satisfactory results. The regions segmented from the knees correspond to anatomical structures that could be used as a starting point for other measurements. By way of example, we demonstrate successful cartilage extraction using our approach.
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Linnett, L. M. "Multi-texture image segmentation". Thesis, Heriot-Watt University, 1991. http://hdl.handle.net/10399/856.
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 fractal dimension is made. Using this set of features, supervised and unsupervised, statistical processing schemes are presented which produce low classification error rates. Natural texture images are examined with particular application to the analysis of sonar images of the seabed. A number of processes based on fractal models for texture synthesis are also presented. These are used to produce realistic images of natural textures, again with particular reference to sonar images of the seabed, and which show the importance of phase and directionality in our perception of texture. A further extension is shown to give possible uses for image coding and object identification.
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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.
Includes bibliographical references (p. 67-72). 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 method which describes the distribution of typical feature vectors. Local descriptors calculate features from smaller neighbourhoods than filter banks. Some local descriptors calculate a scale for their local neighbourhood to achieve scale invariance. Both local and global segmentation methods are investigated. Local segmentation methods consider each pixel in isolation. Global segmentation methods penalise jagged borders or fragmented regions in the segmentation. Pixel labelling and border detection methods are investigated. Methods for measuring the accuracy of segmentation are discussed. Two data sets are used to test the texture segmentation algorithms. The Brodatz Album mosaics are composed of grayscale texture images from the Brodatz Album. The Berkeley Natural Images data set has 300 colour images of natural scenes. The tests show that, of the descriptors tested, filter bank based textons are the best texture descriptors for grayscale images. Local image patch textons are best for colour images. Graph cut segmentation is best for pixel labelling problems and edge detection with regular borders. Non-maxima suppression is best for edge detection with irregular borders. Factors affecting the performance of the algorithms are investigated.
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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.
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 Bachelors Engineering 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.
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 (labels) is very large and the global cost function may have many local minima. This problem can be overcome to a large extent by using a stochastic relaxation algorithm (for example, Simulated annealing). Initially, various classical techniques on texture segmentation are reviewed. Ideally, any texture segmentation algorithm should segment an image, so that there is one to one correspondence between the segmentated edgels and the ground truth edgels. The effectiveness of an algorithm can be quantified in terms of under and over detection errors for each segmented output image. These measures are used throughout this thesis to quantify the quality of the results. A particular method which uses global optimization for texture segmentation is initially identified as potentially interesting and is implemented and studied. The implementation proved that this method suffered from many shortcomings and it is not really as good as it was reported in the literature. As the general approach to the problem is a well established methodology for image processing problems, the rest of the thesis is devoted into different attempts to make this method work. The novel ideas introduced in order to improve the method are: An improved version of the cost function. The use of alternative statistics that characterize each texture. The use of a combination of statistics to charaterize textures. The use of an implicit dictionary of penalizable label configurations, as opposed to an explicit dictionary, leading to penalties applied to anything not acceptable rather than to a selection of unacceptable configurations. The introduction of a modified transfer function that maps statistical differences to label differences. The use of a database of training patterns instead of assuming that one knows a priori which textures are present in the image to be segmented. The use of alternative time schedules with which the model is imposed to the data gradually, in a linear, non-linear and in an adaptive way. The introduction of an enhanced set of labels that allows the use of local orientation of the boundary. The introduction of a novel way to create new states of the system during the process of simulated annealing in order to achieve faster acceleration, by updating the values of 9 label sites instead of a single label site at a time. The results obtained by all these modifications vastly improve the performance of the algorithm from its original version. However, the whole approach does not really produce the quality of the results expected for real applications and it does not exhibit the robustness of a system that could be used in practice. The reason appears to be the bluntness of the statistical tests used to identify the boundary. So, my conclusion is that although global optimization methods are good for edge detection where the data are the local values of the first derivative, the approach is not very appropriate for texture segmentation where one has to rely on statistical differences.
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Tan, Tieniu. "Image texture analysis : classification and segmentation". Thesis, Imperial College London, 1990. http://hdl.handle.net/10044/1/8697.
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.
Camilleri, K. P. Approaches to unsupervised texture segmentation. [Guildford: Department of Electronic & Electrical Engineering, University of Surrey, 1997.
Spann, Michael. Texture description and segmentation in image processing. Birmingham: University of Aston. Department of Electrical and Electronic Engineering, 1985.
Fatemi-Ghomi, N. Texture segmentation using wavelet packets and c-means fuzzy clustering. Guildford: Dept. of Electronic and Electrical Engineering, 1995.
Anil, Phatak, Chatterji Gano e Ames Research Center, a cura di. Scene segmentation of natural images using texture measures and back-propagation. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1993.
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. Silver Spring, Md: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Ocean Service, National Marine Sanctuary Program, 2007.
Regt, L. J. de. Linguistic coherence in biblical Hebrew texts: Arrangement of information, participant reference devices, verb forms, and their contribution to textual segmentation and coherence. Piscataway, NJ: Gorgias Press LLC, 2019.
Capitoli di libri sul tema "Segmentation texture":
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Dana, Kristin J. "Texture Segmentation". In Computational Texture and Patterns, 25–37. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01823-7_4.
Zwiggelaar, Reyer, e Erika R. E. Denton. "Texture Based Segmentation". In Digital Mammography, 433–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11783237_58.
Pietikäinen, Matti, Abdenour Hadid, Guoying Zhao e Timo Ahonen. "Texture Classification and Segmentation". In Computational Imaging and Vision, 69–79. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-748-8_4.
Wang, Yuzhong, Jie Yang e Yue Zhou. "Unsupervised Color-Texture Segmentation". In Lecture Notes in Computer Science, 106–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30125-7_14.
Haindl, Michal, e Stanislav Mikeš. "Model-Based Texture Segmentation". In Lecture Notes in Computer Science, 306–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30126-4_38.
Hung, Chih-Cheng, Enmin Song e Yihua Lan. "Image Texture, Texture Features, and Image Texture Classification and Segmentation". In Image Texture Analysis, 3–14. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13773-1_1.
Jenicka, S. "Supervised Texture-Based Segmentation Using Basic Texture Models". In Land Cover Classification of Remotely Sensed Images, 73–88. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66595-1_4.
Turner, Mark R. "Gabor functions and textural segmentation". In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.wj38.
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 segmentation appears to be one of those tasks. A set of Gabor functions of different frequencies and orientations is applied by computer program to images containing regions of different texture. This process produces a kind of localized and orientation selective frequency spectrum of various fields in the image. The program then attempts to delineate the boundaries of the textured regions by identifying spectrum differences between these fields. Gabor functions are effective in distinguishing between many of the textures used in psychophysical studies differing in first- or second-order statistics. Additional textures in which the difference is related to some aspect of the collinearity of the texture elements have also been tried with promising results.
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McKeekin, M. Sue, e William T. Rhodes. "Texture segmentation by threshold decomposition filtering". In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.thr2.
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 distribution that preserves considerable texture-related information and that has maximum energy per unit area in non-zero spatial frequencies. In addition, that energy per unit area is the same, independent of the specific texture, implying that only the distribution of spectral energy is different and that selective filtering can be used to discriminate in favor of a given texture and against others. The basic system and its underlying theory of operation will be discussed, and the results of numerical experiments will be presented.
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Doretto, Cremers, Favaro e Soatto. "Dynamic texture segmentation". In ICCV 2003: 9th International Conference on Computer Vision. IEEE, 2003. http://dx.doi.org/10.1109/iccv.2003.1238632.
Haindl, Michal, e 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.
Brown, C. David, Charles S. Ih, Gonzalo R. Arce e David A. Fertell. "Scene segmentation using laser projected structured light". In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1987. http://dx.doi.org/10.1364/oam.1987.mm3.
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 discontinuity of the projected lines. The discontinuities of these scan lines can provide the required information for quick identification of the texture in various regions. The image of the laser scan lines is actually a 2-D representation of the 3-D texture of various regions of the scene. This image is conducive to processing with simple regional operators to classify rapidly and robustly regions according to their texture.
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Wilscy, M., e 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.
Barilla, Maria E., Manuel G. Forero e 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.
Zhan, De-Chen, Xuan-Jing Shen, Jing-Chun Chen e Zhong-Rong Li. "Fast texture image segmentation". In San Diego '90, 8-13 July, a cura di Andrew G. Tescher. SPIE, 1990. http://dx.doi.org/10.1117/12.23536.
Giraud, Remi, Vinh-Thong Ta, Nicolas Papadakis e 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.
Rapporti di organizzazioni sul tema "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. Fort Belvoir, VA: Defense Technical Information Center, agosto 1988. http://dx.doi.org/10.21236/ada199536.
Koop, L., M. Snellen e 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.
Ward, Anderson, Gamal Seedahmend, Greg Anderson e Fred Zhang. Integration of Multi-Tension Permeametry and Photogrammetric Textural Segmentation for Estimating Directional Permeability. Fort Belvoir, VA: Defense Technical Information Center, aprile 2010. http://dx.doi.org/10.21236/ada571373.
