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Добірка наукової літератури з теми "IMAGE SEGMENTATION TECHNIQUES"

Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "IMAGE SEGMENTATION TECHNIQUES"

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Haralick, Robert M., and Linda G. Shapiro. "Image segmentation techniques." Computer Vision, Graphics, and Image Processing 29, no. 1 (January 1985): 100–132. http://dx.doi.org/10.1016/s0734-189x(85)90153-7.

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Singh, Inderpal, and Dinesh Kumar. "A Review on Different Image Segmentation Techniques." Indian Journal of Applied Research 4, no. 4 (October 1, 2011): 1–3. http://dx.doi.org/10.15373/2249555x/apr2014/200.

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3

Tongbram, Simon. "Clustering-based Image Segmentation Techniques: A Review." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 701–7. http://dx.doi.org/10.5373/jardcs/v12sp7/20202160.

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Sharma, Dr Kamlesh, and Nidhi Garg. "An Extensive Review on Image Segmentation Techniques." Indian Journal of Image Processing and Recognition 1, no. 2 (June 10, 2021): 1–5. http://dx.doi.org/10.35940/ijipr.b1002.061221.

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Анотація:
Image processing is the use of algorithms to perform various operations on digital images. The techniques that are explained further are image segmentation and image enhancement. Image Segmentation is a method to partition an image into multiple segments, to change the presentation of an image into something more meaningful and easier to analyze. The current image segmentation techniques include region-based segmentation and edge detection segmentation. Image Enhancement is the process of improving the quality of image. Under this section there are two broad divisions- Spatial Domain Technique and Frequency Domain Technique.
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Sharma, Dr Kamlesh, and Nidhi Garg. "An Extensive Review on Image Segmentation Techniques." Indian Journal of Image Processing and Recognition 1, no. 2 (June 10, 2021): 1–5. http://dx.doi.org/10.54105/ijipr.b1002.061221.

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Анотація:
Image processing is the use of algorithms to perform various operations on digital images. The techniques that are explained further are image segmentation and image enhancement. Image Segmentation is a method to partition an image into multiple segments, to change the presentation of an image into something more meaningful and easier to analyze. The current image segmentation techniques include region-based segmentation and edge detection segmentation. Image Enhancement is the process of improving the quality of image. Under this section there are two broad divisions- Spatial Domain Technique and Frequency Domain Technique.
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6

Patel, Dr Bharat C., and Dr Jagin M. Patel. "Comparative Study on Text Segmentation Techniques." YMER Digital 21, no. 01 (January 19, 2022): 372–80. http://dx.doi.org/10.37896/ymer21.01/35.

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Анотація:
Text segmentation, whether printed, handwritten or cursive, is one of the most complicated phases in any OCR. The accuracy of recognition will be heavily reliant on good segmentation. Image segmentation is a crucial component of image analysis and the field of computer vision. Researchers have developed several techniques for segmentation, each of which is used for different types of segmented objects. At present no any universal method is available for image segmentation. Existing image segmentation techniques are not capable to deal with images of any types. This survey looked at a variety of image segmentation techniques, evaluated them, and discussed the issues that came up as a result of using them
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7

Gehlot, Shiv, and John Deva Kumar. "The Image Segmentation Techniques." International Journal of Image, Graphics and Signal Processing 9, no. 2 (February 8, 2017): 9–18. http://dx.doi.org/10.5815/ijigsp.2017.02.02.

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8

Abdul, Wadood. "Region Based Segmentation Techniques for Digital Images." Journal of Computational and Theoretical Nanoscience 16, no. 9 (September 1, 2019): 3792–801. http://dx.doi.org/10.1166/jctn.2019.8252.

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Анотація:
This paper discusses region based segmentation techniques for digital images. For a few applications, such as image compression or recognition, we cannot handle the entire picture straightforwardly as it is unconventional and inefficient. Due to these reasons, many algorithms related to image segmentation are proposed in the literature to divide an image prior to compression or recognition. The segmentation of an image is basically done to arrange or group the image in a few fragments (districts) as specified by the elements of an image, for instance, according to the value of pixel or frequency response. Currently, many image segmentation approaches exist and are widely used in across scientific disciplines and daily human life. The segmentation approaches can be generally categorized to segmentation based on region, segmentation based on edges, and information grouping.
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Tripathi, Rakesh, and Neelesh Gupta. "A Review on Segmentation Techniques in Large-Scale Remote Sensing Images." SMART MOVES JOURNAL IJOSCIENCE 4, no. 4 (April 20, 2018): 7. http://dx.doi.org/10.24113/ijoscience.v4i4.143.

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Анотація:
Information extraction is a very challenging task because remote sensing images are very complicated and can be influenced by many factors. The information we can derive from a remote sensing image mostly depends on the image segmentation results. Image segmentation is an important processing step in most image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation. Labeling different parts of the image has been a challenging aspect of image processing. Segmentation is considered as one of the main steps in image processing. It divides a digital image into multiple regions in order to analyze them. It is also used to distinguish different objects in the image. Several image segmentation techniques have been developed by the researchers in order to make images smooth and easy to evaluate. Various algorithms for automating the segmentation process have been proposed, tested and evaluated to find the most ideal algorithm to be used for different types of images. In this paper a review of basic image segmentation techniques of satellite images is presented.
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Chandrakala, M. "Image Analysis of Sauvola and Niblack Thresholding Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 14, 2021): 2353–57. http://dx.doi.org/10.22214/ijraset.2021.34569.

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Анотація:
Image segmentation is a critical problem in computer vision and other image processing applications. Image segmentation has become quite challenging over the years due to its widespread use in a variety of applications. Image thresholding is a popular image segmentation technique. The segmented image quality is determined by the techniques used to determine the threshold value.A locally adaptive thresholding method based on neighborhood processing is presented in this paper. The performance of locally thresholding methods like Niblack and Sauvola was demonstrated using real-world images, printed text, and handwritten text images. Threshold-based segmentation methods were investigated using misclassification error, MSE and PSNR. Experiments have shown that the Sauvola method outperforms real-world images, printed and handwritten text images in terms of misclassification error, PSNR, and MSE.
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Дисертації з теми "IMAGE SEGMENTATION TECHNIQUES"

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Duramaz, Alper. "Image Segmentation Based On Variational Techniques." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607721/index.pdf.

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Анотація:
Recently, solutions to the problem of image segmentation and denoising are developed based on the Mumford-Shah model. The model provides an energy functional, called the Mumford-Shah functional, which should be minimized. Since the minimization of the functional has some difficulties, approximate approaches are proposed. Two such methods are the gradient flows method and the Chan-Vese active contour method. The performance evolution in terms of speed shows that the gradient flows method converges to the boundaries of the smooth parts faster
but for the hierarchical four-phase segmentation, it is observed that this method sometimes gives unsatisfactory results. In this work, a fast hierarchical four-phase segmentation method is proposed where the Chan-Vese active contour method is applied following the gradient flows method. After the segmentation process, the segmented regions are denoised using diffusion filters. Additionally, for the low signal-to-noise ratio applications, the prefiltering scheme using nonlinear diffusion filters is included in the proposed method. Simulations have shown that the proposed method provides an effective solution to the image segmentation and denoising problem.
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2

Altinoklu, Metin Burak. "Image Segmentation Based On Variational Techniques." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610415/index.pdf.

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Анотація:
In this thesis, the image segmentation methods based on the Mumford&
#8211
Shah variational approach have been studied. By obtaining an optimum point of the Mumford-Shah functional which is a piecewise smooth approximate image and a set of edge curves, an image can be decomposed into regions. This piecewise smooth approximate image is smooth inside of regions, but it is allowed to be discontinuous region wise. Unfortunately, because of the irregularity of the Mumford Shah functional, it cannot be directly used for image segmentation. On the other hand, there are several approaches to approximate the Mumford-Shah functional. In the first approach, suggested by Ambrosio-Tortorelli, it is regularized in a special way. The regularized functional (Ambrosio-Tortorelli functional) is supposed to be gamma-convergent to the Mumford-Shah functional. In the second approach, the Mumford-Shah functional is minimized in two steps. In the first minimization step, the edge set is held constant and the resultant functional is minimized. The second minimization step is about updating the edge set by using level set methods. The second approximation to the Mumford-Shah functional is known as the Chan-Vese method. In both approaches, resultant PDE equations (Euler-Lagrange equations of associated functionals) are solved by finite difference methods. In this study, both approaches are implemented in a MATLAB environment. The overall performance of the algorithms has been investigated based on computer simulations over a series of images from simple to complicated.
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3

Storve, Sigurd. "Kalman Smoothing Techniques in Medical Image Segmentation." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for elektronikk og telekommunikasjon, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-18823.

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An existing C++ library for efficient segmentation of ultrasound recordings by means of Kalman filtering, the real-time contour tracking library (RCTL), is used as a building block to implement and assess the performance of different Kalman smoothing techniques: fixed-point, fixed-lag, and fixed-interval smoothing. An experimental smoothing technique based on fusion of tracking results and learned mean state estimates at different positions in the heart-cycle is proposed. A set of $29$ recordings with ground-truth left ventricle segmentations provided by a trained medical doctor is used for the performance evaluation.The clinical motivation is to improve the accuracy of automatic left-ventricle tracking, which can be applied to improve the automatic measurement of clinically important parameters such as the ejection fraction. The evaluation shows that none of the smoothing techniques offer significant improvements over regular Kalman filtering. For the Kalman smoothing algorithms, it is argued to be a consequence of the way edge-detection measurements are performed internally in the library. The statistical smoother's lack of improvement is explained by too large interpersonal variations; the mean left-ventricular deformation pattern does not generalize well to individual cases.
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4

Seemann, Torsten 1973. "Digital image processing using local segmentation." Monash University, School of Computer Science and Software Engineering, 2002. http://arrow.monash.edu.au/hdl/1959.1/8055.

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5

Matalas, Ioannis. "Segmentation techniques suitable for medical images." Thesis, Imperial College London, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339149.

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Yeo, Si Yong. "Implicit deformable models for biomedical image segmentation." Thesis, Swansea University, 2011. https://cronfa.swan.ac.uk/Record/cronfa42416.

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In this thesis, new methods for the efficient segmentation of images are presented. The proposed methods are based on the deformable model approach, and can be used efficiently in the segmentation of complex geometries from various imaging modalities. A novel deformable model that is based on a geometrically induced external force field which can be conveniently generalized to arbitrary dimensions is presented. This external force field is based on hypothesized interactions between the relative geometries of the deformable model and the object boundary characterized by image gradient. The evolution of the deformable model is solved using the level set method so that topological changes are handled automatically. The relative geometrical configurations between the deformable model and the object boundaries contributes to a dynamic vector force field that changes accordingly as the deformable model evolves. The geometrically induced dynamic interaction force has been shown to greatly improve the deformable model performance in acquiring complex geometries and highly concave boundaries, and give the deformable model a high invariance in initialization configurations. The voxel interactions across the whole image domain provides a global view of the object boundary representation, giving the external force a long attraction range. The bidirectionality of the external force held allows the new deformable model to deal with arbitrary cross-boundary initializations, and facilitates the handling of weak edges and broken boundaries. In addition, it is shown that by enhancing the geometrical interaction field with a nonlocal edge-preserving algorithm, the new deformable model can effectively overcome image noise. A comparative study on the segmentation of various geometries with different topologies from both synthetic and real images is provided, and the proposed method is shown to achieve significant improvements against several existing techniques. A robust framework for the segmentation of vascular geometries is described. In particular, the framework consists of image denoising, optimal object edge representation, and segmentation using implicit deformable model. The image denoising is based on vessel enhancing diffusion which can be used to smooth out image noise and enhance the vessel structures. The image object boundaries are derived using an edge detection technique which can produce object edges of single pixel width. The image edge information is then used to derive the geometric interaction field for optimal object edge representation. The vascular geometries are segmented using an implict deformable model. A region constraint is added to the deformable model which allows it to easily get around calcified regions and propagate across the vessels to segment the structures efficiently. The presented framework is ai)plied in the accurate segmentation of carotid geometries from medical images. A new segmentation model with statistical shape prior using a variational approach is also presented in this thesis. The proposed model consists of an image attraction force that propagates contours towards image object boundaries, and a global shape force that attracts the model towards similar shapes in the statistical shape distribution. The image attraction force is derived from gradient vector interactions across the whole image domain, which makes the model more robust to image noise, weak edges and initializations. The statistical shape information is incorporated using kernel density estimation, which allows the shape prior model to handle arbitrary shape variations. It is shown that the proposed model with shape prior can be used to segment object shapes from images efficiently.
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Alazawi, Eman. "Holoscopic 3D image depth estimation and segmentation techniques." Thesis, Brunel University, 2015. http://bura.brunel.ac.uk/handle/2438/10517.

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Анотація:
Today’s 3D imaging techniques offer significant benefits over conventional 2D imaging techniques. The presence of natural depth information in the scene affords the observer an overall improved sense of reality and naturalness. A variety of systems attempting to reach this goal have been designed by many independent research groups, such as stereoscopic and auto-stereoscopic systems. Though the images displayed by such systems tend to cause eye strain, fatigue and headaches after prolonged viewing as users are required to focus on the screen plane/accommodation to converge their eyes to a point in space in a different plane/convergence. Holoscopy is a 3D technology that targets overcoming the above limitations of current 3D technology and was recently developed at Brunel University. This work is part W4.1 of the 3D VIVANT project that is funded by the EU under the ICT program and coordinated by Dr. Aman Aggoun at Brunel University, West London, UK. The objective of the work described in this thesis is to develop estimation and segmentation techniques that are capable of estimating precise 3D depth, and are applicable for holoscopic 3D imaging system. Particular emphasis is given to the task of automatic techniques i.e. favours algorithms with broad generalisation abilities, as no constraints are placed on the setting. Algorithms that provide invariance to most appearance based variation of objects in the scene (e.g. viewpoint changes, deformable objects, presence of noise and changes in lighting). Moreover, have the ability to estimate depth information from both types of holoscopic 3D images i.e. Unidirectional and Omni-directional which gives horizontal parallax and full parallax (vertical and horizontal), respectively. The main aim of this research is to develop 3D depth estimation and 3D image segmentation techniques with great precision. In particular, emphasis on automation of thresholding techniques and cues identifications for development of robust algorithms. A method for depth-through-disparity feature analysis has been built based on the existing correlation between the pixels at a one micro-lens pitch which has been exploited to extract the viewpoint images (VPIs). The corresponding displacement among the VPIs has been exploited to estimate the depth information map via setting and extracting reliable sets of local features. ii Feature-based-point and feature-based-edge are two novel automatic thresholding techniques for detecting and extracting features that have been used in this approach. These techniques offer a solution to the problem of setting and extracting reliable features automatically to improve the performance of the depth estimation related to the generalizations, speed and quality. Due to the resolution limitation of the extracted VPIs, obtaining an accurate 3D depth map is challenging. Therefore, sub-pixel shift and integration is a novel interpolation technique that has been used in this approach to generate super-resolution VPIs. By shift and integration of a set of up-sampled low resolution VPIs, the new information contained in each viewpoint is exploited to obtain a super resolution VPI. This produces a high resolution perspective VPI with wide Field Of View (FOV). This means that the holoscopic 3D image system can be converted into a multi-view 3D image pixel format. Both depth accuracy and a fast execution time have been achieved that improved the 3D depth map. For a 3D object to be recognized the related foreground regions and depth information map needs to be identified. Two novel unsupervised segmentation methods that generate interactive depth maps from single viewpoint segmentation were developed. Both techniques offer new improvements over the existing methods due to their simple use and being fully automatic; therefore, producing the 3D depth interactive map without human interaction. The final contribution is a performance evaluation, to provide an equitable measurement for the extent of the success of the proposed techniques for foreground object segmentation, 3D depth interactive map creation and the generation of 2D super-resolution viewpoint techniques. The no-reference image quality assessment metrics and their correlation with the human perception of quality are used with the help of human participants in a subjective manner.
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Shaffrey, Cian William. "Multiscale techniques for image segmentation, classification and retrieval." Thesis, University of Cambridge, 2003. https://www.repository.cam.ac.uk/handle/1810/272033.

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9

Sekkal, Rafiq. "Techniques visuelles pour la détection et le suivi d’objets 2D." Thesis, Rennes, INSA, 2014. http://www.theses.fr/2014ISAR0032/document.

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Анотація:
De nos jours, le traitement et l’analyse d’images trouvent leur application dans de nombreux domaines. Dans le cas de la navigation d’un robot mobile (fauteuil roulant) en milieu intérieur, l’extraction de repères visuels et leur suivi constituent une étape importante pour la réalisation de tâches robotiques (localisation, planification, etc.). En particulier, afin de réaliser une tâche de franchissement de portes, il est indispensable de détecter et suivre automatiquement toutes les portes qui existent dans l’environnement. La détection des portes n’est pas une tâche facile : la variation de l’état des portes (ouvertes ou fermées), leur apparence (de même couleur ou de couleur différentes des murs) et leur position par rapport à la caméra influe sur la robustesse du système. D’autre part, des tâches comme la détection des zones navigables ou l’évitement d’obstacles peuvent faire appel à des représentations enrichies par une sémantique adaptée afin d’interpréter le contenu de la scène. Pour cela, les techniques de segmentation permettent d’extraire des régions pseudo-sémantiques de l’image en fonction de plusieurs critères (couleur, gradient, texture…). En ajoutant la dimension temporelle, les régions sont alors suivies à travers des algorithmes de segmentation spatio-temporelle. Dans cette thèse, des contributions répondant aux besoins cités sont présentées. Tout d’abord, une technique de détection et de suivi de portes dans un environnement de type couloir est proposée : basée sur des descripteurs géométriques dédiés, la solution offre de bons résultats. Ensuite, une technique originale de segmentation multirésolution et hiérarchique permet d’extraire une représentation en régions pseudosémantique. Enfin, cette technique est étendue pour les séquences vidéo afin de permettre le suivi des régions à travers le suivi de leurs contours. La qualité des résultats est démontrée et s’applique notamment au cas de vidéos de couloir
Nowadays, image processing remains a very important step in different fields of applications. In an indoor environment, for a navigation system related to a mobile robot (electrical wheelchair), visual information detection and tracking is crucial to perform robotic tasks (localization, planning…). In particular, when considering passing door task, it is essential to be able to detect and track automatically all the doors that belong to the environment. Door detection is not an obvious task: the variations related to the door status (open or closed), their appearance (e.g. same color as the walls) and their relative position to the camera have influence on the results. On the other hand, tasks such as the detection of navigable areas or obstacle avoidance may involve a dedicated semantic representation to interpret the content of the scene. Segmentation techniques are then used to extract pseudosemantic regions based on several criteria (color, gradient, texture...). When adding the temporal dimension, the regions are tracked then using spatiotemporal segmentation algorithms. In this thesis, we first present joint door detection and tracking technique in a corridor environment: based on dedicated geometrical features, the proposed solution offers interesting results. Then, we present an original joint hierarchical and multiresolution segmentation framework able to extract a pseudo-semantic region representation. Finally, this technique is extended to video sequences to allow the tracking of regions along image sequences. Based on contour motion extraction, this solution has shown relevant results that can be successfully applied to corridor videos
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Celik, Mehmet Kemal. "Digital image segmentation using periodic codings." Thesis, Virginia Polytechnic Institute and State University, 1988. http://hdl.handle.net/10919/80099.

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Digital image segmentation using periodic codings is explored with reference to two applications. First, the application of uniform periodic codings, to the problem of segmenting the in-focus regions in an image from the blurred parts, is discussed. The work presented in this part extends a previous investigation on this subject by considering the leakage effects. The method proposed consists of two stages. In each stage, filtering is done in the spatial frequency domain after uniform grating functions are applied to the images in the spatial domain. Then, algorithms for finding the period and phase of a physical grating are explored for a hybrid optical-digital application of the method. Second, a model for textures as the linear superposition of periodic narrowband components, defined as tones, is proposed. A priori information about the number of the tones, their spatial frequencies, and coefficients is necessary to generate tone and texture indicators. Tone indicators are obtained by filtering the image with complex analytical functions defined by the spatial frequencies of the tones present in the image. A criterion for choosing the dimensions of the filter is also provided. Texture indicators are then generated for each texture in the image by applying the a priori information of the tonal coefficients to the filtered images. Several methods for texture segmentation which employ texture indicators are proposed. Finally, examples which illustrate the characteristics of the method are presented.
Master of Science
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Книги з теми "IMAGE SEGMENTATION TECHNIQUES"

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Siddiqui, Fasahat Ullah, and Abid Yahya. Clustering Techniques for Image Segmentation. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-81230-0.

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2

Roland, Wilson. Image segmentation and uncertainty. Letchworth, Herts., England: Research Studies Press, 1988.

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3

Ismail, Ben Ayed, ed. Variational and level set methods in image segmentation. Berlin: Springer Verlag, 2010.

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4

Leppäjärvi, Seppo. Image segmentation and analysis for automatic color correction. Lappeenranta, Finland: Lappeenranta University of Technology, 1999.

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5

Gorte, Ben. Probabilistic segmentation of remotely sensed images. Enschede: International Institute for Aerospace Survey and Earth Sciences (ITC), 1998.

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6

Vernon, David. Fourier vision: Segmentation and velocity measurement using the Fourier transform. Boston: Kluwer Academic, 2001.

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7

Nitzberg, M. Filtering, segmentation, and depth. Berlin: Springer-Verlag, 1993.

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8

Video segmentation and its applications. New York: Springer, 2011.

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9

Batra, Dhruv. Interactive Co-segmentation of Objects in Image Collections. New York, NY: Springer Science+Business Media, LLC, 2011.

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10

1956-, Solimini Sergio, ed. Variational methods in image segmentation: With seven image processing experiments. Boston: Birkhäuser, 1995.

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Частини книг з теми "IMAGE SEGMENTATION TECHNIQUES"

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Bhanu, Bir, and Sungkee Lee. "Image segmentation Techniques." In Genetic Learning for Adaptive Image Segmentation, 15–24. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2774-9_2.

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Zhang, Yu-Jin. "Image Segmentation." In A Selection of Image Analysis Techniques, 31–71. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/b23131-2.

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Chaki, Jyotismita, and Nilanjan Dey. "Segmentation Techniques." In A Beginner's Guide to Image Preprocessing Techniques, 57–72. Boca Raton : Taylor & Francis, a CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academicdivision of T&F Informa, plc, 2019. | Series: Intelligent signalprocessing and data analysis: CRC Press, 2018. http://dx.doi.org/10.1201/9780429441134-5.

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Siddiqui, Fasahat Ullah, and Abid Yahya. "Partitioning Clustering Techniques." In Clustering Techniques for Image Segmentation, 35–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81230-0_2.

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He, Jia, Chang-Su Kim, and C. C. Jay Kuo. "Interactive Image Segmentation Techniques." In SpringerBriefs in Electrical and Computer Engineering, 17–62. Singapore: Springer Singapore, 2013. http://dx.doi.org/10.1007/978-981-4451-60-4_3.

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Siddiqui, Fasahat Ullah, and Abid Yahya. "Novel Partitioning Clustering." In Clustering Techniques for Image Segmentation, 69–91. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81230-0_3.

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Siddiqui, Fasahat Ullah, and Abid Yahya. "Quantitative Analysis Methods of Clustering Techniques." In Clustering Techniques for Image Segmentation, 93–105. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81230-0_4.

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Siddiqui, Fasahat Ullah, and Abid Yahya. "Introduction to Image Segmentation and Clustering." In Clustering Techniques for Image Segmentation, 1–34. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81230-0_1.

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Phonsa, Gurbakash, and K. Manu. "A Survey: Image Segmentation Techniques." In Harmony Search and Nature Inspired Optimization Algorithms, 1123–40. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0761-4_105.

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Mozdren, Karel, Tomas Burianek, Jan Platos, and Václav Snášel. "Evolutionary Techniques for Image Segmentation." In Advances in Intelligent Systems and Computing, 291–300. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08156-4_29.

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Тези доповідей конференцій з теми "IMAGE SEGMENTATION TECHNIQUES"

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Haralick, Robert M., and Linda G. Shapiro. "Image Segmentation Techniques." In 1985 Technical Symposium East, edited by John F. Gilmore. SPIE, 1985. http://dx.doi.org/10.1117/12.948400.

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Taouli, Sidi Ahmed. "Research on the Image Segmentation by Watershed Transforms." In 3rd International Conference on Machine Learning Techniques and Data Science (MLDS 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.122108.

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Segmentation is a very important step in medical image processing. The mathematical morphology is very suitable for the pretreatment and segmentation of medical images, which present rich information content. In this work we presented a segmentation paradigm by Watershed preceded by a filtering to eliminate insignificant minima, a marking to remove unmarked minima, and finally we implemented a hierarchical segmentation using the mosaic image of the original image. In principle, watershed segmentation depends on ridges to perform a proper segmentation, a property that is often fulfilled in contour detection where the boundaries of the objects are expressed as ridges. Watershed is normally implemented by region growing, based on a set of markers to avoid over segmentation. The diversity of segmentation offers us several ways to segment the image, always we must look for the right method to get good results.
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Song, Yuheng, and Hao Yan. "Image Segmentation Techniques Overview." In 2017 Asia Modelling Symposium (AMS). 11th International Conference on Mathematical Modelling & Computer Simulation. IEEE, 2017. http://dx.doi.org/10.1109/ams.2017.24.

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Cornelis, De Becker, Bister, Vanhove, Demonceau, and Cornelis. "Techniques for Cardiac Image Segmentation." In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1992. http://dx.doi.org/10.1109/iembs.1992.590248.

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Comelis, J., J. De Becker, M. Bister, C. Vanhove, G. Demonceau, and A. Cornelis. "Techniques for cardiac image segmentation." In 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1992. http://dx.doi.org/10.1109/iembs.1992.5762094.

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Xu, Haixiang, Guangxi Zhu, Jinwen Tian, Xiang Zhang, and Fuyuan Peng. "Image segmentation using support vector machine." In MIPPR 2005 Image Analysis Techniques, edited by Deren Li and Hongchao Ma. SPIE, 2005. http://dx.doi.org/10.1117/12.655253.

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Zhang, Hong-wei, and Zheng-guang Liu. "Wavelet-based snake model for image segmentation." In MIPPR 2005 Image Analysis Techniques, edited by Deren Li and Hongchao Ma. SPIE, 2005. http://dx.doi.org/10.1117/12.655275.

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Gao, Li, Jie Xia, Junli Liang, and Shuyuan Yang. "Improved Techniques for Unsupervised Image Segmentation." In 2006 International Conference on Communications, Circuits and Systems. IEEE, 2006. http://dx.doi.org/10.1109/icccas.2006.284608.

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Pandey, Rahul, and R. Lalchhanhima. "Segmentation Techniques for Complex Image: Review." In 2020 International Conference on Computational Performance Evaluation (ComPE). IEEE, 2020. http://dx.doi.org/10.1109/compe49325.2020.9200027.

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Sevak, Jay S., Aerika D. Kapadia, Jaiminkumar B. Chavda, Arpita Shah, and Mrugendrasinh Rahevar. "Survey on semantic image segmentation techniques." In 2017 International Conference on Intelligent Sustainable Systems (ICISS). IEEE, 2017. http://dx.doi.org/10.1109/iss1.2017.8389420.

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Звіти організацій з теми "IMAGE SEGMENTATION TECHNIQUES"

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Huang, Haohang, Erol Tutumluer, Jiayi Luo, Kelin Ding, Issam Qamhia, and John Hart. 3D Image Analysis Using Deep Learning for Size and Shape Characterization of Stockpile Riprap Aggregates—Phase 2. Illinois Center for Transportation, September 2022. http://dx.doi.org/10.36501/0197-9191/22-017.

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Анотація:
Riprap rock and aggregates are extensively used in structural, transportation, geotechnical, and hydraulic engineering applications. Field determination of morphological properties of aggregates such as size and shape can greatly facilitate the quality assurance/quality control (QA/QC) process for proper aggregate material selection and engineering use. Many aggregate imaging approaches have been developed to characterize the size and morphology of individual aggregates by computer vision. However, 3D field characterization of aggregate particle morphology is challenging both during the quarry production process and at construction sites, particularly for aggregates in stockpile form. This research study presents a 3D reconstruction-segmentation-completion approach based on deep learning techniques by combining three developed research components: field 3D reconstruction procedures, 3D stockpile instance segmentation, and 3D shape completion. The approach was designed to reconstruct aggregate stockpiles from multi-view images, segment the stockpile into individual instances, and predict the unseen side of each instance (particle) based on the partial visible shapes. Based on the dataset constructed from individual aggregate models, a state-of-the-art 3D instance segmentation network and a 3D shape completion network were implemented and trained, respectively. The application of the integrated approach was demonstrated on re-engineered stockpiles and field stockpiles. The validation of results using ground-truth measurements showed satisfactory algorithm performance in capturing and predicting the unseen sides of aggregates. The algorithms are integrated into a software application with a user-friendly graphical user interface. Based on the findings of this study, this stockpile aggregate analysis approach is envisioned to provide efficient field evaluation of aggregate stockpiles by offering convenient and reliable solutions for on-site QA/QC tasks of riprap rock and aggregate stockpiles.
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Huang, Haohang, Jiayi Luo, Kelin Ding, Erol Tutumluer, John Hart, and Issam Qamhia. I-RIPRAP 3D Image Analysis Software: User Manual. Illinois Center for Transportation, June 2023. http://dx.doi.org/10.36501/0197-9191/23-008.

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Анотація:
Riprap rock and aggregates are commonly used in various engineering applications such as structural, transportation, geotechnical, and hydraulic engineering. To ensure the quality of the aggregate materials selected for these applications, it is important to determine their morphological properties such as size and shape. There have been many imaging approaches developed to characterize the size and shape of individual aggregates, but obtaining 3D characterization of aggregates in stockpiles at production or construction sites can be a challenging task. This research study introduces a new approach based on deep learning techniques that combines three developed research components: field 3D reconstruction procedures, 3D stockpiles instance segmentation, and 3D shape completion. The approach is designed to reconstruct aggregate stockpiles from multiple images, segment the stockpile into individual instances, and predict the unseen sides of each instance (particle) based on the partially visible shapes. The approach was validated using ground-truth measurements and demonstrated satisfactory algorithm performance in capturing and predicting the unseen sides of aggregates. For better user experience, the integrated approach has been implemented into a software application named “I-RIPRAP 3D,” with a user-friendly graphical user interface (GUI). This stockpile aggregate analysis approach is envisioned to provide efficient field evaluation of aggregate stockpiles by offering convenient and reliable solutions for on-site quality assurance and quality control tasks of riprap rock and aggregate stockpiles. This document provides information for users of the I-RIPRAP 3D software to make the best use of the software’s capabilities.
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Patwa, B., P. L. St-Charles, G. Bellefleur, and B. Rousseau. Predictive models for first arrivals on seismic reflection data, Manitoba, New Brunswick, and Ontario. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329758.

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First arrivals are the primary waves picked and analyzed by seismologists to infer properties of the subsurface. Here we try to solve a problem in a small subsection of the seismic processing workflow: first break picking of seismic reflection data. We formulate this problem as an image segmentation task. Data is preprocessed, cleaned from outliers and extrapolated to make the training of deep learning models feasible. We use Fully Convolutional Networks (specifically UNets) to train initial models and explore their performance with losses, layer depths, and the number of classes. We propose to use residual connections to improve each UNet block and residual paths to solve the semantic gap between UNet encoder and decoder which improves the performance of the model. Adding spatial information as an extra channel helped increase the RMSE performance of the first break predictions. Other techniques like data augmentation, multitask loss, and normalization methods, were further explored to evaluate model improvement.
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Asari, Vijayan, Paheding Sidike, Binu Nair, Saibabu Arigela, Varun Santhaseelan, and Chen Cui. PR-433-133700-R01 Pipeline Right-of-Way Automated Threat Detection by Advanced Image Analysis. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), December 2015. http://dx.doi.org/10.55274/r0010891.

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A novel algorithmic framework for the robust detection and classification of machinery threats and other potentially harmful objects intruding onto a pipeline right-of-way (ROW) is designed from three perspectives: visibility improvement, context-based segmentation, and object recognition/classification. In the first part of the framework, an adaptive image enhancement algorithm is utilized to improve the visibility of aerial imagery to aid in threat detection. In this technique, a nonlinear transfer function is developed to enhance the processing of aerial imagery with extremely non-uniform lighting conditions. In the second part of the framework, the context-based segmentation is developed to eliminate regions from imagery that are not considered to be a threat to the pipeline. Context based segmentation makes use of a cascade of pre-trained classifiers to search for regions that are not threats. The context based segmentation algorithm accelerates threat identification and improves object detection rates. The last phase of the framework is an efficient object detection model. Efficient object detection �follows a three-stage approach which includes extraction of the local phase in the image and the use of local phase characteristics to locate machinery threats. The local phase is an image feature extraction technique which partially removes the lighting variance and preserves the edge information of the object. Multiple orientations of the same object are matched and the correct orientation is selected using feature matching by histogram of local phase in a multi-scale framework. The classifier outputs locations of threats to pipeline.�The advanced automatic image analysis system is intended to be capable of detecting construction equipment along the ROW of pipelines with a very high degree of accuracy in comparison with manual threat identification by a human analyst. �
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