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Zeng, Ziming. "Medical image segmentation on multimodality images." Thesis, Aberystwyth University, 2013. http://hdl.handle.net/2160/17cd13c2-067c-451b-8217-70947f89164e.
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Segmentation is a hot issue in the domain of medical image analysis. It has a wide range of applications on medical research. A great many medical image segmentation algorithms have been proposed, and many good segmentation results were obtained. However, due to the noise, density inhomogenity, partial volume effects, and density overlap between normal and abnormal tissues in medical images, the segmentation accuracy and robustness of some state-of-the-art methods still have room for improvement. This thesis aims to deal with the above segmentation problems and improve the segmentation accuracy. This project investigated medical image segmentation methods across a range of modalities and clinical applications, covering magnetic resonance imaging (MRI) in brain tissue segmentation, MRI based multiple sclerosis (MS) lesions segmentation, histology based cell nuclei segmentation, and positron emission tomography (PET) based tumour detection. For the brain MRI tissue segmentation, a method based on mutual information was developed to estimate the number of brain tissue groups. Then a unsupervised segmentation method was proposed to segment the brain tissues. For the MS lesions segmentation, 2D/3D joint histogram modelling were proposed to model the grey level distribution of MS lesions in multimodality MRI. For the PET segmentation of the head and neck tumours, two hierarchical methods based on improved active contour/surface modelling were proposed to segment the tumours in PET volumes. For the histology based cell nuclei segmentation, a novel unsupervised segmentation based on adaptive active contour modelling driven by morphology initialization was proposed to segment the cell nuclei. Then the segmentation results were further processed for subtypes classification. Among these segmentation approaches, a number of techniques (such as modified bias field fuzzy c-means clustering, multiimage spatially joint histogram representation, and convex optimisation of deformable model, etc.) were developed to deal with the key problems in medical image segmentation. Experiments show that the novel methods in this thesis have great potential for various image segmentation scenarios and can obtain more accurate and robust segmentation results than some state-of-the-art methods.
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Horne, Caspar. "Unsupervised image segmentation /." Lausanne : EPFL, 1991. http://library.epfl.ch/theses/?nr=905.
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Bhalerao, Abhir. "Multiresolution image segmentation." Thesis, University of Warwick, 1991. http://wrap.warwick.ac.uk/60866/.
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Image segmentation is an important area in the general field of image processing and computer vision. It is a fundamental part of the 'low level' aspects of computer vision and has many practical applications such as in medical imaging, industrial automation and satellite imagery. Traditional methods for image segmentation have approached the problem either from localisation in class space using region information, or from localisation in position, using edge or boundary information. More recently, however, attempts have been made to combine both region and boundary information in order to overcome the inherent limitations of using either approach alone. In this thesis, a new approach to image segmentation is presented that integrates region and boundary information within a multiresolution framework. The role of uncertainty is described, which imposes a limit on the simultaneous localisation in both class and position space. It is shown how a multiresolution approach allows the trade-off between position and class resolution and ensures both robustness in noise and efficiency of computation. The segmentation is based on an image model derived from a general class of multiresolution signal models, which incorporates both region and boundary features. A four stage algorithm is described consisting of: generation of a low-pass pyramid, separate region and boundary estimation processes and an integration strategy. Both the region and boundary processes consist of scale-selection, creation of adjacency graphs, and iterative estimation within a general framework of maximum a posteriori (MAP) estimation and decision theory. Parameter estimation is performed in situ, and the decision processes are both flexible and spatially local, thus avoiding assumptions about global homogeneity or size and number of regions which characterise some of the earlier algorithms. A method for robust estimation of edge orientation and position is described which addresses the problem in the form of a multiresolution minimum mean square error (MMSE) estimation. The method effectively uses the spatial consistency of output of small kernel gradient operators from different scales to produce more reliable edge position and orientation and is effective at extracting boundary orientations from data with low signal-to-noise ratios. Segmentation results are presented for a number of synthetic and natural images which show the cooperative method to give accurate segmentations at low signal-to-noise ratios (0 dB) and to be more effective than previous methods at capturing complex region shapes.
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Craske, Simon. "Natural image segmentation." Thesis, University of Bristol, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266990.
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Draelos, Timothy John 1961. "INTERACTIVE IMAGE SEGMENTATION." Thesis, The University of Arizona, 1987. http://hdl.handle.net/10150/276392.
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Salem, Mohammed Abdel-Megeed Mohammed. "Multiresolution image segmentation." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2008. http://dx.doi.org/10.18452/15846.
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Systeme der Computer Vision spielen in der Automatisierung vieler Prozesse eine wichtige Rolle. Die wichtigste Aufgabe solcher Systeme ist die Automatisierung des visuellen Erkennungsprozesses und die Extraktion der relevanten Information aus Bildern oder Bildsequenzen. Eine wichtige Komponente dieser Systeme ist die Bildsegmentierung, denn sie bestimmt zu einem großen Teil die Qualitaet des Gesamtsystems. Fuer die Segmentierung von Bildern und Bildsequenzen werden neue Algorithmen vorgeschlagen. Das Konzept der Multiresolution wird als eigenstaendig dargestellt, es existiert unabhaengig von der Wavelet-Transformation. Die Wavelet-Transformation wird zur Verarbeitung von Bildern und Bildsequenzen zu einer 2D- bzw. 3D-Wavelet- Transformation erweitert. Fuer die Segmentierung von Bildern wird der Algorithmus Resolution Mosaic Expectation Maximization (RM-EM) vorgeschlagen. Das Ergebnis der Vorverarbeitung sind unterschiedlich aufgeloesten Teilbilder, das Aufloesungsmosaik. Durch dieses Mosaik lassen sich raeumliche Korrelationen zwischen den Pixeln ausnutzen. Die Verwendung unterschiedlicher Aufloesungen beschleunigt die Verarbeitung und verbessert die Ergebnisse. Fuer die Extraktion von bewegten Objekten aus Bildsequenzen werden neue Algorithmen vorgeschlagen, die auf der 3D-Wavelet-Transformation und auf der Analyse mit 3D-Wavelet-Packets beruhen. Die neuen Algorithmen haben den Vorteil, dass sie sowohl die raeumlichen als auch die zeitlichen Bewegungsinformationen beruecksichtigen. Wegen der geringen Berechnungskomplexitaet der Wavelet-Transformation ist fuer den ersten Segmentierungsschritt Hardware auf der Basis von FPGA entworfen worden. Aktuelle Anwendungen werden genutzt, um die Algorithmen zu evaluieren: die Segmentierung von Magnetresonanzbildern des menschlichen Gehirns und die Detektion von bewegten Objekten in Bildsequenzen von Verkehrsszenen. Die neuen Algorithmen sind robust und fuehren zu besseren Segmentierungsergebnissen.<br>More and more computer vision systems take part in the automation of various applications. The main task of such systems is to automate the process of visual recognition and to extract relevant information from the images or image sequences acquired or produced by such applications. One essential and critical component in almost every computer vision system is image segmentation. The quality of the segmentation determines to a great extent the quality of the final results of the vision system. New algorithms for image and video segmentation based on the multiresolution analysis and the wavelet transform are proposed. The concept of multiresolution is explained as existing independently of the wavelet transform. The wavelet transform is extended to two and three dimensions to allow image and video processing. For still image segmentation the Resolution Mosaic Expectation Maximization (RM-EM) algorithm is proposed. The resolution mosaic enables the algorithm to employ the spatial correlation between the pixels. The level of the local resolution depends on the information content of the individual parts of the image. The use of various resolutions speeds up the processing and improves the results. New algorithms based on the 3D wavelet transform and the 3D wavelet packet analysis are proposed for extracting moving objects from image sequences. The new algorithms have the advantage of considering the relevant spatial as well as temporal information of the movement. Because of the low computational complexity of the wavelet transform an FPGA hardware for the primary segmentation step was designed. Actual applications are used to investigate and evaluate all algorithms: the segmentation of magnetic resonance images of the human brain and the detection of moving objects in image sequences of traffic scenes. The new algorithms show robustness against noise and changing ambient conditions and gave better segmentation results.
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Hillman, Peter. "Segmentation of motion picture images and image sequences." Thesis, University of Edinburgh, 2002. http://hdl.handle.net/1842/15026.
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For Motion Picture Special Effects, it is often necessary to take a source image of an actor, segment the actor from the unwanted background, and then composite over a new background. The resultant image appears as if the actor was filmed in front of the new background. The standard approach requires the unwanted background to be a blue or green screen. While this technique is capable of handling areas where the foreground (the actor) blends into the background, the physical requirements present many practical problems. This thesis investigates the possibility of segmenting images where the unwanted background is more varied. Standard segmentation techniques tend not to be effective, since motion picture images have extremely high resolution and high accuracy is required to make the result appear convincing. A set of novel algorithms which require minimal human interaction to initialise the processing is presented. These algorithms classify each pixel by comparing its colour to that of known background and foreground areas. They are shown to be effective where there is a sufficient distinction between the colours of the foreground and background. A technique for assessing the quality of an image segmentation in order to compare these algorithms to alternative solutions is presented. Results are included which suggest that in most cases the novel algorithms have the best performance, and that they produce results more quickly than the alternative approaches. Techniques for segmentation of moving images sequences are then presented. Results are included which show that only a few frames of the sequence need to be initialised by hand, as it is often possible to generate automatically the input required to initialise processing for the remaining frames. A novel algorithm which can produce acceptable results on image sequences where more conventional approaches fail or are too slow to be of use is presented.
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Chowdhury, Md Mahbubul Islam. "Image segmentation for coding." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0017/MQ55494.pdf.
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Wang, Jingdong. "Graph based image segmentation /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20WANG.
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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 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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Vyas, Aseem. "Medical Image Segmentation by Transferring Ground Truth Segmentation." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32431.
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The segmentation of medical images is a difficult task due to the inhomogeneous intensity variations that occurs during digital image acquisition, the complicated shape of the object, and the medical expert’s lack of semantic knowledge. Automated segmentation algorithms work well for some medical images, but no algorithm has been general enough to work for all medical images. In practice, most of the time the segmentation results are corrected by the experts before the actual use.
In this work, we are motivated to determine how to make use of manually segmented data in automatic segmentation. The key idea is to transfer the ground truth segmentation from the database of train images to a given test image. The ground truth segmentation of MR images is done by experts.
The process includes a hierarchical image decomposition approach that performs the shape matching of test images at several levels, starting with the image as a whole (i.e. level 0) and then going through a pyramid decomposition (i.e. level 1, level 2, etc.) with the database of the train images and the given test image. The goal of pyramid decomposition is to find the section of the training image that best matches a section of the test image of a different level. After that, a re-composition approach is taken to place the best matched sections of the training image to the original test image space. Finally, the ground truth segmentation is transferred from the best training images to their corresponding location in the test image.
We have tested our method on a hip joint MR image database and the experiment shows successful results on level 0, level 1 and level 2 re-compositions. Results improve with deeper level decompositions, which supports our hypotheses.
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Murphy, Sean Daniel. "Medical image segmentation in volumetric CT and MR images." Thesis, University of Glasgow, 2012. http://theses.gla.ac.uk/3816/.
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This portfolio thesis addresses several topics in the field of 3D medical image analysis. Automated methods are used to identify structures and points of interest within the body to aid the radiologist. The automated algorithms presented here incorporate many classical machine learning and imaging techniques, such as image registration, image filtering, supervised classification, unsupervised clustering, morphology and probabilistic modelling. All algorithms are validated against manually collected ground truth. Chapter two presents a novel algorithm for automatically detecting named anatomical landmarks within a CT scan, using a linear registration based atlas framework. The novel scans may contain a wide variety of anatomical regions from throughout the body. Registration is typically posed as a numerical optimisation problem. For this problem the associated search space is shown to be non-convex and so standard registration approaches fail. Specialised numerical optimisation schemes are developed to solve this problem with an emphasis placed on simplicity. A semi-automated algorithm for finding the centrelines of coronary arterial trees in CT angiography scans given a seed point is presented in chapter three. This is a modified classical region growing algorithm whereby the topology and geometry of the tree are discovered as the region grows. The challenges presented by the presence of large organs and other extraneous material in the vicinity of the coronary trees is mitigated by the use of an efficient modified 3D top-hat transform. Chapter four compares the accuracy of three unsupervised clustering algorithms when applied to automated tissue classification within the brain on 3D multi-spectral MR images. Chapter five presents a generalised supervised probabilistic framework for the segmentation of structures/tissues in medical images called a spatially varying classifier (SVC). This algorithm leverages off non-rigid registration techniques and is shown to be a generalisation of atlas based techniques and supervised intensity based classification. This is achieved by constructing a multivariate Gaussian classifier for each voxel in a reference scan. The SVC is applied in the context of tissue classification in multi-spectral MR images in chapter six, by simultaneously extracting the brain and classifying the tissues types within it. A specially designed pre-processing pipeline is presented which involves inter-sequence registration, spatial normalisation and intensity normalisation. The SVC is then applied to the problem of multi-compartment heart segmentation in CT angiography data with minimal modification. The accuracy of this method is shown to be comparable to other state of the art methods in the field.
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Sharma, Karan. "The Link Between Image Segmentation and Image Recognition." PDXScholar, 2012. https://pdxscholar.library.pdx.edu/open_access_etds/199.
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A long standing debate in computer vision community concerns the link between segmentation and recognition. The question I am trying to answer here is, Does image segmentation as a preprocessing step help image recognition? In spite of a plethora of the literature to the contrary, some authors have suggested that recognition driven by high quality segmentation is the most promising approach in image recognition because the recognition system will see only the relevant features on the object and not see redundant features outside the object (Malisiewicz and Efros 2007; Rabinovich, Vedaldi, and Belongie 2007). This thesis explores the following question: If segmentation precedes recognition, and segments are directly fed to the recognition engine, will it help the recognition machinery? Another question I am trying to address in this thesis is of scalability of recognition systems. Any computer vision system, concept or an algorithm, without exception, if it is to stand the test of time, will have to address the issue of scalability.
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Lundström, Claes. "Segmentation of Medical Image Volumes." Thesis, Linköping University, Linköping University, Computer Vision, 1997. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54357.
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<p>Segmentation is a process that separates objects in an image. In medical images, particularly image volumes, the field of application is wide. For example 3D visualisations of the anatomy could benefit enormously from segmentation. The aim of this thesis is to construct a segmentation tool.</p><p>The project consist three main parts. First, a survey of the actual need of segmentation in medical image volumes was carried out. Then a unique three-step model for a segmentation tool was implemented, tested and evaluated.</p><p>The first step of the segmentation tool is a seed-growing method that uses the intensity and an orientation tensor estimate to decide which voxels that are part of the project. The second step uses an active contour, a deformable “balloon”. The contour is shrunk to fit the segmented border from the first step, yielding a surface suitable for visualisation. The last step consists of letting the contour reshape according to the orientation tensor estimate.</p><p>The use evaluation establishes the usefulness of the tool. The model is flexible and well adapted to the users’ requests. For unclear objects the segmentation may fail, but the cause is mostly poor image quality. Even though much work remains to be done on the second and third part of the tool, the results are most promising.</p>
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Keshtkar, Abolfazl. "Swarm intelligence-based image segmentation." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27525.
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One of the major difficulties met in image segmentation lies in the varying degrees of homogeneousness of the different regions in a given image. Hence, it is more efficient to adopt adaptive threshold type methodologies to identify the regions in the images. Throughout the last decade, many image processing tools and techniques have emerged based on the former technology which we called conventional and new technologies such as intelligent-based image processing techniques and algorithm. In some cases, a combination of both technologies is adapted to form a hybrid image processing technique.
Intelligent-based techniques are increasing nowadays. Due to the rapid growth of agent-based technology's environments which are adopting numerous agent-based applications, tools, models and softwares to enhance and improve the quality of the agent based approach. In case of intelligent techniques to doing image processing; swarm intelligence techniques rarely have been used in term of image segmentation or boundary detection. However, there are many factors that make this task challenging. These factors include not only the limited such increasing number of agents in the environment, and the presence of techniques., but also how to efficiently find the right threshold in the image, develop a flexible design, and fully autonomous system that support different platform. A flexible architecture and tools need to be defined that overcomes these problems and permits a smooth and valuable image processing based on these new techniques in image processing. It would satisfy the needs of end users. This thesis illustrates the theoretical background, design, swarm based intelligent techniques and implementation of a fully agent-based model system that is called SIBIS (Swarm Intelligent Based Image Segmentation).
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Johnson, M. A. "Semantic segmentation and image search." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.605649.
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Understanding the meaning behind visual data is increasingly important as the quantity of digital images in circulation explodes, and as computing in general and the Internet in specific shifts quickly towards an increasingly visual presentation of data. However, the remarkable amount of variance inside categories (e.g. different kinds of chairs) combined with the occurrence of similarity between categories (e.g. similar breeds of cats and dogs) makes this problem incredibly difficult to solve. In particular, the <i>semantic segmentation</i> of images into contiguous regions of similar interpretation combines the difficulties of object recognition and image segmentation to result in a problem of great complexity, yet great reward. This thesis proposes a novel solution to the problem of semantic segmentation, and explores its application to image search and retrieval. Our primary contribution is a new image information processing tool: the <i>semantic texton forest</i>. We use semantic texton forests to perform (i) semantic segmentation of images and (ii) image categorization, achieving state-of-the-art results for both on two challenging datasets. We then apply this to the problem of image search and retrieval, resulting in the Palette Search System. With Palette Search, the user is able to search for the first time using <i>Query by Semantic Composition</i>, in which he communicates both what he wants in the result image and where he wants it.
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Morgan, Pamela Sheila. "Medical image coding and segmentation :." Thesis, University of Bristol, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442206.
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Tweed, David S. "Motion segmentation across image sequences." Thesis, University of Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364960.
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O'Connor, Kevin Luke. "Image segmentation through optimal tessellation." Thesis, Imperial College London, 1988. http://hdl.handle.net/10044/1/47210.
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O'Donnell, Lauren (Lauren Jean) 1976. "Semi-automatic medical image segmentation." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/87175.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2002.<br>Includes bibliographical references (leaves 92-96).<br>by Lauren O'Donnell.<br>S.M.
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Spencer, Jack A. "Variational methods for image segmentation." Thesis, University of Liverpool, 2016. http://livrepository.liverpool.ac.uk/3003758/.
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The work in this thesis is concerned with variational methods for two-phase segmentation problems. We are interested in both the obtaining of numerical solutions to the partial differential equations arising from the minimisation of a given functional, and forming variational models that tackle some practical problem in segmentation (e.g. incorporating prior knowledge, dealing with intensity inhomogeneity). With that in mind we will discuss each aspect of the work as follows. A seminal two-phase variational segmentation problem in the literature is that of Active Contours Without Edges, introduced by Chan and Vese in 2001, based on the piecewise-constant formulation of Mumford and Shah. The idea is to partition an image into two regions of homogeneous intensity. However, despite the extensive success of this work its reliance on the level set method means that it is nonconvex. Later work on the convex reformulation of ACWE by Chan, Esedoglu, and Nikolova has led to a burgeoning of related methods, known as the convex relaxation approach. In Chapter 4, we introduce a method to find global minimisers of a general two-phase segmentation problem, which forms the basis for work in the rest of the thesis. We introduce an improved additive operator splitting (AOS) method based on the work of Weickert et al. and Tai et al. AOS has been frequently used for segmentation problems, but not in the convex relaxation setting. The adjustment made accounts for how to impose the relaxed binary constraint, fundamental to this approach. Our method is analogous to work such as Bresson et al. and we quantitatively compare our method against this by using a number of appropriate metrics. Having dealt with globally convex segmentation (GCS) for the general case in Chapter 4, we then bear in mind two important considerations. Firstly, we discuss the matter of selective segmentation and how it relates to GCS. Many recent models have incorporated user input for two-phase formulations using piecewise-constant fitting terms. In Chapter 5 we discuss the conditions for models of this type to be reformulated in a similar way. We then propose a new model compatible with convex relaxation methods, and present results for challenging examples. Secondly, we consider the incorporation of priors for GCS in Chapter 8. Here, the intention is to select objects in an image of a similar shape to a given prior. We consider the most appropriate way to represent shape priors in a variational formulation, and the potential applications of our approach. We also investigate the problem of segmentation where the observed data is challenging. We consider two cases in this thesis; in one there is significant intensity inhomogeneity, and in the other the image has been corrupted by unknown blur. The first has been widely studied and is closely related to the piecewise-smooth formulation of Mumford and Shah. In Chapter 6 we discuss a Variant Mumford- Shah Model by D.Chen et al. that uses the bias field framework. Our work focuses on improving results for methods of this type. The second has been less widely studied, but is more commonly considered when there is knowledge of the blur type. We discuss the advantages of simultaneously reconstructing and segmenting the image, rather than treating each problem separately and compare our method against comparable models. The aim of this thesis is to develop new variational methods for two-phase image segmentation, with potential applications in mind. We also consider new schemes to compute numerical solutions for generalised segmentation problems. With both approaches we focus on convex relaxation methods, and consider the challenges of formulating segmentation problems in this manner. Where possible we compare our ideas against current approaches to determine quantifiable improvements, particularly with respect to accuracy and reliability.
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Brown, Ryan Charles. "IRIS: Intelligent Roadway Image Segmentation." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/49105.
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The problem of roadway navigation and obstacle avoidance for unmanned ground vehicles has typically needed very expensive sensing to operate properly. To reduce the cost of sensing, it is proposed that an algorithm be developed that uses a single visual camera to image the roadway, determine where the lane of travel is in the image, and segment that lane. The algorithm would need to be as accurate as current lane finding algorithms as well as faster than a standard k- means segmentation across the entire image.
This algorithm, named IRIS, was developed and tested on several sets of roadway images. The algorithm was tested for its accuracy and speed, and was found to be better than 86% accurate across all data sets for an optimal choice of algorithm parameters. IRIS was also found to be faster than a k-means segmentation across the entire image. IRIS was found to be adequate for fulfilling the design goals for the algorithm. IRIS is a feasible system for lane identification and segmentation, but it is not currently a viable system. More work to increase the speed of the algorithm and the accuracy of lane detection and to extend the inherent lane model to more complex road types is needed. IRIS represents a significant step forward in the single camera roadway perception field.<br>Master of Science
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Wu, Qian. "Segmentation-based Retinal Image Analysis." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18524.
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Context. Diabetic retinopathy is the most common cause of new cases of legal blindness in people of working age. Early diagnosis is the key to slowing the progression of the disease, thus preventing blindness. Retinal fundus image is an important basis for judging these retinal diseases. With the development of technology, computer-aided diagnosis is widely used. Objectives. The thesis is to investigate whether there exist specific regions that could assist in better prediction of the retinopathy disease, it means to find the best region in fundus image that works the best in retinopathy classification with the use of computer vision and machine learning techniques. Methods. An experiment method was used as research methods. With image segmentation techniques, the fundus image is divided into regions to obtain the optic disc dataset, blood vessel dataset, and other regions (regions other than blood vessel and optic disk) dataset. These datasets and original fundus image dataset were tested on Random Forest (RF), Support Vector Machines (SVM) and Convolutional Neural Network (CNN) models, respectively. Results. It is found that the results on different models are inconsistent. As compared to the original fundus image, the blood vessel region exhibits the best performance on SVM model, the other regions perform best on RF model, while the original fundus image has higher prediction accuracy on CNN model. Conclusions. The other regions dataset has more predictive power than original fundus image dataset on RF and SVM models. On CNN model, extracting features from the fundus image does not significantly improve predictive performance as compared to the entire fundus image.
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Pan, Huizhu. "Variational Image Segmentation with Constraints." Thesis, Curtin University, 2020. http://hdl.handle.net/20.500.11937/80866.
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The research of Huizhu Pan addresses the problem of image segmentation with constraints though designing and solving various variational models. A novel constraint term is designed for the use of landmarks in image segmentation. Two region-based segmentation models were proposed where the segmentation contour passes through landmark points. A more stable and memory efficient solution to the self-repelling snakes model, a variational model with the topology preservation constraint, was also designed.
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Felhi, Mehdi. "Document image segmentation : content categorization." Thesis, Université de Lorraine, 2014. http://www.theses.fr/2014LORR0109/document.
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Dans cette thèse, nous abordons le problème de la segmentation des images de documents en proposant de nouvelles approches pour la détection et la classification de leurs contenus. Dans un premier lieu, nous étudions le problème de l'estimation d'inclinaison des documents numérisées. Le but de ce travail étant de développer une approche automatique en mesure d'estimer l'angle d'inclinaison du texte dans les images de document. Notre méthode est basée sur la méthode Maximum Gradient Difference (MGD), la R-signature et la transformée de Ridgelets. Nous proposons ensuite une approche hybride pour la segmentation des documents. Nous décrivons notre descripteur de trait qui permet de détecter les composantes de texte en se basant sur la squeletisation. La méthode est appliquée pour la segmentation des images de documents numérisés (journaux et magazines) qui contiennent du texte, des lignes et des régions de photos. Le dernier volet de la thèse est consacré à la détection du texte dans les photos et posters. Pour cela, nous proposons un ensemble de descripteurs de texte basés sur les caractéristiques du trait. Notre approche commence par l'extraction et la sélection des candidats de caractères de texte. Deux méthodes ont été établies pour regrouper les caractères d'une même ligne de texte (mot ou phrase) ; l'une consiste à parcourir en profondeur un graphe, l'autre consiste à établir un critère de stabilité d'une région de texte. Enfin, les résultats sont affinés en classant les candidats de texte en régions « texte » et « non-texte » en utilisant une version à noyau du classifieur Support Vector Machine (K-SVM)<br>In this thesis I discuss the document image segmentation problem and I describe our new approaches for detecting and classifying document contents. First, I discuss our skew angle estimation approach. The aim of this approach is to develop an automatic approach able to estimate, with precision, the skew angle of text in document images. Our method is based on Maximum Gradient Difference (MGD) and R-signature. Then, I describe our second method based on Ridgelet transform.Our second contribution consists in a new hybrid page segmentation approach. I first describe our stroke-based descriptor that allows detecting text and line candidates using the skeleton of the binarized document image. Then, an active contour model is applied to segment the rest of the image into photo and background regions. Finally, text candidates are clustered using mean-shift analysis technique according to their corresponding sizes. The method is applied for segmenting scanned document images (newspapers and magazines) that contain text, lines and photo regions. Finally, I describe our stroke-based text extraction method. Our approach begins by extracting connected components and selecting text character candidates over the CIE LCH color space using the Histogram of Oriented Gradients (HOG) correlation coefficients in order to detect low contrasted regions. The text region candidates are clustered using two different approaches ; a depth first search approach over a graph, and a stable text line criterion. Finally, the resulted regions are refined by classifying the text line candidates into « text» and « non-text » regions using a Kernel Support Vector Machine K-SVM classifier
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Felhi, Mehdi. "Document image segmentation : content categorization." Electronic Thesis or Diss., Université de Lorraine, 2014. http://www.theses.fr/2014LORR0109.
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Dans cette thèse, nous abordons le problème de la segmentation des images de documents en proposant de nouvelles approches pour la détection et la classification de leurs contenus. Dans un premier lieu, nous étudions le problème de l'estimation d'inclinaison des documents numérisées. Le but de ce travail étant de développer une approche automatique en mesure d'estimer l'angle d'inclinaison du texte dans les images de document. Notre méthode est basée sur la méthode Maximum Gradient Difference (MGD), la R-signature et la transformée de Ridgelets. Nous proposons ensuite une approche hybride pour la segmentation des documents. Nous décrivons notre descripteur de trait qui permet de détecter les composantes de texte en se basant sur la squeletisation. La méthode est appliquée pour la segmentation des images de documents numérisés (journaux et magazines) qui contiennent du texte, des lignes et des régions de photos. Le dernier volet de la thèse est consacré à la détection du texte dans les photos et posters. Pour cela, nous proposons un ensemble de descripteurs de texte basés sur les caractéristiques du trait. Notre approche commence par l'extraction et la sélection des candidats de caractères de texte. Deux méthodes ont été établies pour regrouper les caractères d'une même ligne de texte (mot ou phrase) ; l'une consiste à parcourir en profondeur un graphe, l'autre consiste à établir un critère de stabilité d'une région de texte. Enfin, les résultats sont affinés en classant les candidats de texte en régions « texte » et « non-texte » en utilisant une version à noyau du classifieur Support Vector Machine (K-SVM)<br>In this thesis I discuss the document image segmentation problem and I describe our new approaches for detecting and classifying document contents. First, I discuss our skew angle estimation approach. The aim of this approach is to develop an automatic approach able to estimate, with precision, the skew angle of text in document images. Our method is based on Maximum Gradient Difference (MGD) and R-signature. Then, I describe our second method based on Ridgelet transform.Our second contribution consists in a new hybrid page segmentation approach. I first describe our stroke-based descriptor that allows detecting text and line candidates using the skeleton of the binarized document image. Then, an active contour model is applied to segment the rest of the image into photo and background regions. Finally, text candidates are clustered using mean-shift analysis technique according to their corresponding sizes. The method is applied for segmenting scanned document images (newspapers and magazines) that contain text, lines and photo regions. Finally, I describe our stroke-based text extraction method. Our approach begins by extracting connected components and selecting text character candidates over the CIE LCH color space using the Histogram of Oriented Gradients (HOG) correlation coefficients in order to detect low contrasted regions. The text region candidates are clustered using two different approaches ; a depth first search approach over a graph, and a stable text line criterion. Finally, the resulted regions are refined by classifying the text line candidates into « text» and « non-text » regions using a Kernel Support Vector Machine K-SVM classifier
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Toh, Vivian. "Statistical image analysis : length estimation and colour image segmentation." Thesis, University of Strathclyde, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.415373.
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Xu, Dongxiang. "Image segmentation and its application on MR image analysis /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/6063.
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Muñoz, Pujol Xavier 1976. "Image segmentation integrating colour, texture and boundary information." Doctoral thesis, Universitat de Girona, 2003. http://hdl.handle.net/10803/7719.
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La tesis se centra en la Visión por Computador y, más concretamente, en la segmentación de imágenes, la cual es una de las etapas básicas en el análisis de imágenes y consiste en la división de la imagen en un conjunto de regiones visualmente distintas y uniformes considerando su intensidad, color o textura.<br/>Se propone una estrategia basada en el uso complementario de la información de región y de frontera durante el proceso de segmentación, integración que permite paliar algunos de los problemas básicos de la segmentación tradicional. La información de frontera permite inicialmente identificar el número de regiones presentes en la imagen y colocar en el interior de cada una de ellas una semilla, con el objetivo de modelar estadísticamente las características de las regiones y definir de esta forma la información de región. Esta información, conjuntamente con la información de frontera, es utilizada en la definición de una función de energía que expresa las propiedades requeridas a la segmentación deseada: uniformidad en el interior de las regiones y contraste con las regiones vecinas en los límites. Un conjunto de regiones activas inician entonces su crecimiento, compitiendo por los píxeles de la imagen, con el objetivo de optimizar la función de energía o, en otras palabras, encontrar la segmentación que mejor se adecua a los requerimientos exprsados en dicha función. Finalmente, todo esta proceso ha sido considerado en una estructura piramidal, lo que nos permite refinar progresivamente el resultado de la segmentación y mejorar su coste computacional.<br/>La estrategia ha sido extendida al problema de segmentación de texturas, lo que implica algunas consideraciones básicas como el modelaje de las regiones a partir de un conjunto de características de textura y la extracción de la información de frontera cuando la textura es presente en la imagen.<br/>Finalmente, se ha llevado a cabo la extensión a la segmentación de imágenes teniendo en cuenta las propiedades de color y textura. En este sentido, el uso conjunto de técnicas no-paramétricas de estimación de la función de densidad para la descripción del color, y de características textuales basadas en la matriz de co-ocurrencia, ha sido propuesto para modelar adecuadamente y de forma completa las regiones de la imagen.<br/>La propuesta ha sido evaluada de forma objetiva y comparada con distintas técnicas de integración utilizando imágenes sintéticas. Además, se han incluido experimentos con imágenes reales con resultados muy positivos.<br>Image segmentation is an important research area in computer vision and many segmentation methods have been proposed. However, elemental segmentation techniques based on boundary or region approaches often fail to produce accurate segmentation results. Hence, in the last few years, there has been a tendency towards the integration of both techniques in order to improve the results by taking into account the complementary nature of such information. This thesis proposes a solution to the image segmentation integrating region and boundary information. Moreover, the method is extended to texture and colour texture segmentation.<br/>An exhaustive analysis of image segmentation techniques which integrate region and boundary information is carried out. Main strategies to perform the integration are identified and a classification of these approaches is proposed. Thus, the most relevant proposals are assorted and grouped in their corresponding approach. Moreover, characteristics of these strategies as well as the general lack of attention that is given to the texture is noted. The discussion of these aspects has been the origin of all the work evolved in this thesis, giving rise to two basic conclusions: first, the possibility of fusing several approaches to the integration of both information sources, and second, the necessity of a specific treatment for textured images.<br/>Next, an unsupervised segmentation strategy which integrates region and boundary information and incorporates three different approaches identified in the previous review is proposed. Specifically, the proposed image segmentation method combines the guidance of seed placement, the control of decision criterion and the boundary refinement approaches. The method is composed by two basic stages: initialisation and segmentation. Thus, in the first stage, the main contours of the image are used to identify the different regions present in the image and to adequately place a seed for each one in order to statistically model the region. Then, the segmentation stage is performed based on the active region model which allows us to take region and boundary information into account in order to segment the whole image. Specifically, regions start to shrink and expand guided by the optimisation of an energy function that ensures homogeneity properties inside regions and the presence of real edges at boundaries. Furthermore, with the aim of imitating the Human Vision System when a person is slowly approaching to a distant object, a pyramidal structure is considered. Hence, the method has been designed on a pyramidal representation which allows us to refine the region boundaries from a coarse to a fine resolution, and ensuring noise robustness as well as computation efficiency.<br/>The proposed segmentation strategy is then adapted to solve the problem of texture and colour texture segmentation. First, the proposed strategy is extended to texture segmentation which involves some considerations as the region modelling and the extraction of texture boundary information. Next, a method to integrate colour and textural properties is proposed, which is based on the use of texture descriptors and the estimation of colour behaviour by using non-parametric techniques of density estimation. Hence, the proposed strategy of segmentation is considered for the segmentation taking both colour and textural properties into account.<br/>Finally, the proposal of image segmentation strategy is objectively evaluated and then compared with some other relevant algorithms corresponding to the different strategies of region and boundary integration. Moreover, an evaluation of the segmentation results obtained on colour texture segmentation is performed. Furthermore, results on a wide set of real images are shown and discussed.
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Elmowafy, Osama Mohammed Elsayed. "Image processing systems for TV image tracking." Thesis, University of Kent, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310164.
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Li, Xiaobing. "Automatic image segmentation based on level set approach: application to brain tumor segmentation in MR images." Reims, 2009. http://theses.univ-reims.fr/exl-doc/GED00001120.pdf.
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L'objectif de la thèse est de développer une segmentation automatique des tumeurs cérébrales à partir de volumes IRM basée sur la technique des « level sets ». Le fonctionnement «automatique» de ce système utilise le fait que le cerveau normal est symétrique et donc la localisation des régions dissymétriques permet d'estimer le contour initial de la tumeur. La première étape concerne le prétraitement qui consiste à corriger l'inhomogénéité de l'intensité du volume IRM et à recaler spatialement les volumes d'IRM d'un même patient à différents instants. Le plan hémisphérique du cerveau est recherché en maximisant le degré de similarité entre la moitié du volume et de sa réflexion. Le contour initial de la tumeur est ainsi extrait à partir de la dissymétrie entre les deux hémisphères. Ce contour initial est évolué et affiné par une technique de « level set » afin de trouver le contour réel de la tumeur. Les critères d'arrêt de l'évolution ont été proposés en fonction des propriétés de la tumeur. Finalement, le contour de la tumeur est projetée sur les images adjacentes pour former les nouveaux contours initiaux. Ce traitement est itéré sur toutes les coupes pour obtenir la segmentation de la tumeur en 3D. Le système ainsi réalisé est utilisé pour suivre un patient pendant toute la période thérapeutique, avec des examens tous les quatre mois, ce qui permet au médecin de contrôler l'état de développement de la tumeur et ainsi d'évaluer l'efficacité du traitement thérapeutique. La méthode a été évaluée quantitativement par la comparaison avec des tracés manuels des experts. De bons résultats sont obtenus sur des images réelles IRM<br>The aim of this dissertation is to develop an automatic segmentation of brain tumors from MRI volume based on the technique of "level sets". The term "automatic" uses the fact that the normal brain is symmetrical and the localization of asymmetrical regions permits to estimate the initial contour of the tumor. The first step is preprocessing, which is to correct the intensity inhomogeneity of volume MRI and spatially realign the MRI volumes of the same patient at different moments. The plan hemispherical brain is then calculated by maximizing the degree of similarity between the half of the volume and his reflexion. The initial contour of the tumor can be extracted from the asymmetry between the two hemispheres. This initial contour is evolved and refined by the technique "level set" in order to find the real contour of the tumor. The criteria for stopping the evolution have been proposed and based on the properties of the tumor. Finally, the contour of the tumor is projected onto the adjacent images to form the new initial contours. This process is iterated on all slices to obtain the segmentation of the tumor in 3D. The proposed system is used to follow up patients throughout the medical treatment period, with examinations every four months, allowing the physician to monitor the state of development of the tumor and evaluate the effectiveness of the therapy. The method was quantitatively evaluated by comparison with manual tracings experts. Good results are obtained on real MRI images
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Farnebäck, Gunnar. "Motion-based segmentation of image sequences." Thesis, Linköping University, Linköping University, Computer Vision, 1996. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54351.
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<p>This Master's Thesis addresses the problem of segmenting an image sequence with respect to the motion in the sequence. As a basis for the motion estimation, 3D orientation tensors are used. The goal of the segmentation is to partition the images into regions, characterized by having a coherent motion. The motion model is affine with respect to the image coordinates. A method to estimate the parameters of the motion model from the orientation tensors in a region is presented. This method can also be generalized to a large class of motion models.</p><p>Two segmentation algorithms are presented together with a postprocessing algorithm. All these algorithms are based on the competitive algorithm, a general method for distributing points between a number of regions, without relying on arbitrary threshold values. The first segmentation algorithm segments each image independently, while the second algorithm recursively takes advantage of the previous segmentation. The postprocessing algorithm stabilizes the segmentations of a whole sequence by imposing continuity constraints.</p><p>The algorithms have been implemented and the results of applying them to a test sequence are presented. Interesting properties of the algorithms are that they are robust to the aperture problem and that they do not require a dense velocity ¯eld.</p><p>It is finally discussed how the algorithms can be developed and improved. It is straightforward to extend the algorithms to base the segmentations on alternative or additional features, under not too restrictive conditions on the features.</p>
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Pichon, Eric. "Novel Methods for Multidimensional Image Segmentation." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7504.
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Artificial vision is the problem of creating systems capable of processing visual information. A fundamental sub-problem of artificial vision is image segmentation, the problem of detecting a structure from a digital image. Examples of segmentation problems include the detection of a road from an aerial photograph or the determination of the boundaries of the brain's ventricles from medical imagery. The extraction of structures allows for subsequent higher-level cognitive tasks. One of them is shape comparison. For example, if the brain ventricles of a patient are segmented, can their shapes be used for diagnosis? That is to say, do the shapes of the extracted ventricles resemble more those of healthy patients or those of patients suffering from schizophrenia?
This thesis deals with the problem of image segmentation and shape comparison in the mathematical framework of partial differential equations. The contribution of this thesis is threefold:
1. A technique for the segmentation of regions is proposed. A cost functional is defined for regions based on a non-parametric functional of the distribution of image intensities inside the region. This cost is constructed to favor regions that are homogeneous. Regions that are optimal with respect to that cost can be determined with limited user interaction.
2. The use of direction information is introduced for the segmentation of open curves and closed surfaces. A cost functional is defined for structures (curves or surfaces) by integrating a local, direction-dependent pattern detector along the structure. Optimal structures, corresponding to the best match with the pattern detector, can be determined using efficient algorithms.
3. A technique for shape comparison based on the Laplace equation is proposed. Given two surfaces, one-to-one correspondences are determined that allow for the characterization of local and global similarity measures. The local differences among shapes (resulting for example from a segmentation step) can be visualized for qualitative evaluation by a human expert. It can also be used for classifying shapes into, for example, normal and pathological classes.
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Ersoy, Ozan. "Image Segmentation With Improved Region Modeling." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605627/index.pdf.
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Image segmentation is an important research area in digital image processing with several applications in vision-guided autonomous robotics, product quality inspection, medical diagnosis, the analysis of remotely sensed images, etc. The aim of image segmentation can be defined as partitioning an image into homogeneous regions in terms of the features of pixels extracted from the image.
Image segmentation methods can be classified into four main categories: 1) clustering methods, 2) region-based methods, 3) hybrid methods, and 4) bayesian methods. In this thesis, major image segmentation methods belonging to first three categories are examined and tested on typical images. Moreover, improvements are also proposed to well-known Recursive Shortest-Spanning Tree (RSST) algorithm. The improvements aim to better model each region during merging stage. Namely, grayscale histogram, joint histogram and homogeneous texture are used for better region modeling.
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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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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<br>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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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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In this thesis, the image segmentation methods based on the Mumford&<br>#8211<br>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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Ismaili, Imdad Ali. "Natural image segmentation using colour information." Thesis, Imperial College London, 1996. http://hdl.handle.net/10044/1/8010.
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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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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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Ikonomakis, Nicolaos. "A hybrid colour image segmentation scheme." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0006/MQ45988.pdf.
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Akhlaghian, Tab Fardin. "Multiresolution scalable image and video segmentation." Access electronically, 2005. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20060227.100704/index.html.
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Ezzati, Majid. "Fast image segmentation using stereo vision." Thesis, McGill University, 1995. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=23258.
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Binocular stereopsis is a biologically motivated approach that uses two slightly different views of a scene to extract information about its three-dimensional properties. The two underlying principles of our approach to stereo vision are local computation of binocular disparities and the use of the resulting disparity map for image segmentation.<br>The cepstrum is used to provide an estimation of binocular disparity between corresponding regions of the stereo image pair. We study the cepstrum and its properties, and suggest improvements to the initial disparity estimation stage. Next a modified median filtering scheme is employed for the refinement of the initial disparities using neighbourhood information. The overall disparity map is used for image segmentation based on distance.<br>Local estimation of initial disparities provides two fundamental advantages for real-time systems: the possibility of increased computational efficiency through parallel implementation and a fixed running time that is independent of image properties. Furthermore, using stereopsis for figure-ground segmentation rather than surface reconstruction eliminates the need for camera calibration which is essential for methods based on exact depth calculation. Therefore, the approach is well-suited to active vision systems in which the cameras are in constant motion.<br>We provide evidence for the plausibility of the disparity estimation algorithm and the properties of the overall disparity map in the context of biological stereopsis. The algorithm is implemented on a network of TMS320C40 processors to obtain a processing time of one second for a 128-pixel $ times$ 128-pixel image frame.
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Gulshan, Varun. "From interactive to semantic image segmentation." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:706b648a-e5e7-4334-a456-0f0b5701dbc4.
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This thesis investigates two well defined problems in image segmentation, viz. interactive and semantic image segmentation. Interactive segmentation involves power assisting a user in cutting out objects from an image, whereas semantic segmentation involves partitioning pixels in an image into object categories. We investigate various models and energy formulations for both these problems in this thesis. In order to improve the performance of interactive systems, low level texture features are introduced as a replacement for the more commonly used RGB features. To quantify the improvement obtained by using these texture features, two annotated datasets of images are introduced (one consisting of natural images, and the other consisting of camouflaged objects). A significant improvement in performance is observed when using texture features for the case of monochrome images and images containing camouflaged objects. We also explore adding mid-level cues such as shape constraints into interactive segmentation by introducing the idea of geodesic star convexity, which extends the existing notion of a star convexity prior in two important ways: (i) It allows for multiple star centres as opposed to single stars in the original prior and (ii) It generalises the shape constraint by allowing for Geodesic paths as opposed to Euclidean rays. Global minima of our energy function can be obtained subject to these new constraints. We also introduce Geodesic Forests, which exploit the structure of shortest paths in implementing the extended constraints. These extensions to star convexity allow us to use such constraints in a practical segmentation system. This system is evaluated by means of a “robot user” to measure the amount of interaction required in a precise way, and it is shown that having shape constraints reduces user effort significantly compared to existing interactive systems. We also introduce a new and harder dataset which augments the existing GrabCut dataset with more realistic images and ground truth taken from the PASCAL VOC segmentation challenge. In the latter part of the thesis, we bring in object category level information in order to make the interactive segmentation tasks easier, and move towards fully automated semantic segmentation. An algorithm to automatically segment humans from cluttered images given their bounding boxes is presented. A top down segmentation of the human is obtained using classifiers trained to predict segmentation masks from local HOG descriptors. These masks are then combined with bottom up image information in a local GrabCut like procedure. This algorithm is later completely automated to segment humans without requiring a bounding box, and is quantitatively compared with other semantic segmentation methods. We also introduce a novel way to acquire large quantities of segmented training data relatively effortlessly using the Kinect. In the final part of this work, we explore various semantic segmentation methods based on learning using bottom up super-pixelisations. Different methods of combining multiple super-pixelisations are discussed and quantitatively evaluated on two segmentation datasets. We observe that simple combinations of independently trained classifiers on single super-pixelisations perform almost as good as complex methods based on jointly learning across multiple super-pixelisations. We also explore CRF based formulations for semantic segmentation, and introduce novel visual words based object boundary description in the energy formulation. The object appearance and boundary parameters are trained jointly using structured output learning methods, and the benefit of adding pairwise terms is quantified on two different datasets.
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Hasan, Basela Sharif. "Image segmentation using deformable spatial priors." Thesis, University of Leeds, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.590422.
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Image segmentation is one of the main problems that need to be solved as a component procedure in many computer vision tasks such as recognition, image editing, and indexing. Poor quality segmentation results can markedly deteriorate the performance demonstrated by the whole task. Therefore, a great deal of research heeds to the set of segmentation techniques focused on finding high accuracy segmentations. Existing methods tend to exploit low and high level information about the object in a given image. Incorporating shape priors within the MRF formulation were shown to be extremely helpful in finding desired segmentations. This thesis presents a method for segmenting the parts of a known object within images. The method builds on an existing MRF formulation incorporating a prior shape model and colour distributions for the constituent parts. As a means to tackle this problem when these instances exhibit large variations in projected shape and colour: the proposed approach is to learn a. probabilistic model for variations in the shape of the class of objects and to use this model in segmenting. For efficient search on shape latent parameters, a Branch & Bound approach is formulated to provide upper bounds on the pixelwise prior probabilities over the selected shape space used in this search. Moreover, a simple extension is made to the MRF formulation to deal simultaneously with multiple objects within a global optimisation. Finally, the method is evaluated on a library of images depicting people wearing suits - the aim being to segment the shirt, jacket , tie, and head/face for each individual. Results demonstrate improved performance in terms of accuracy over the state of the art for this task.
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梁志堅 and Chi-kin Leung. "Segmentation based on segmented-image entropy." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31234987.
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Heerlein, Danny. "Image segmentation for improvised explosive devices." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/27843.
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This thesis creates algorithms to preprocess colored images in order to segment Improvised Explosive Devices (IEDs). IEDs are usually concealed and camouflaged and therefore more difficult to segment than other objects. We address the increased difficulty with three key contributions: 1) Our algorithm automatically divides a user-defined background area into smaller areas. We generate separate color models for each of these areas to ensure that a color model includes only colors that appear in the same area of the background. 2) We compress each of these complex color models into a statistical model. This increases the number of background models we can hold simultaneously in working memory, and allows us to generate a set of background models that describes a complete environment. 3) We estimate the initial object labels based on the color distance to the background. This approach allows us to generate color models for IEDs without user input that labels parts of the IED
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Zahedi, Fariborz. "A systems approach to image segmentation." Thesis, University of Brighton, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.260978.
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