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‣ Watershed framework to region-based image segmentation

Monteiro, Fernando C.
Fonte: IEEE Publicador: IEEE
Tipo: Conferência ou Objeto de Conferência
Português
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Indexado ISI.; This paper proposes a new framework to image segmentation which combines edge- and region-based information with spectral techniques through the morphological algorithm of watersheds. A pre-processing step is used to reduce the spatial resolution without losing important image information. An initial partitioning of the image into primitive regions is set by applying a rainfalling watershed algorithm on the image gradient magnitude. This initial partition is the input to a computationally efficient region segmentation process which produces the final segmentation. The latter process uses a region-based similarity graph representation of the image regions. The experimental results clearly demonstrate the effectiveness of the proposed approach to produce simpler segmentations and to compare favourably with state-of-the-art methods.

‣ Hybrid framework to image segmentation

Monteiro, Fernando C.
Fonte: Springer Publicador: Springer
Tipo: Parte de Livro
Português
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Indexado ISI; This paper proposes a new hybrid framework to image segmentation which combines edge- and region-based information with spectral techniques through the morphological algorithm of watersheds. The image is represented by a weighted undirected graph, whose nodes correspond to over-segmented regions (atomic regions), instead of pixels, that decreases the complexity of the overall algorithm. In addition, the link weights between the nodes are calculated through the intensity similarities combined with the intervening contours information among atomic regions. We outline a procedure for algorithm evaluation through the comparison with some of the most popular segmentation algorithms: the mean-shift-based algorithm, a multiscale graph based segmentation method, and JSEG method for multiscale segmentation of colour and texture. Experiments on the Berkeley segmentation database indicate that the proposed segmentation framework yields better segmentation results due to its region-based representation.

‣ Laplacian coordinates for seeded image segmentation

Casaca, Wallace Correa de Oliveira; Nonato, Luis Gustavo; Taubin, Gabriel
Fonte: The Computer Vision Foundation - CVF; Columbus, Ohio Publicador: The Computer Vision Foundation - CVF; Columbus, Ohio
Tipo: Conferência ou Objeto de Conferência
Português
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Seed-based image segmentation methods have gained much attention lately, mainly due to their good performance in segmenting complex images with little user interaction. Such popularity leveraged the development of many new variations of seed-based image segmentation techniques, which vary greatly regarding mathematical formulation and complexity. Most existing methods in fact rely on complex mathematical formulations that typically do not guarantee unique solution for the segmentation problem while still being prone to be trapped in local minima. In this work we present a novel framework for seed-based image segmentation that is mathematically simple, easy to implement, and guaranteed to produce a unique solution. Moreover, the formulation holds an anisotropic behavior, that is, pixels sharing similar attributes are kept closer to each other while big jumps are naturally imposed on the boundary between image regions, thus ensuring better fitting on object boundaries. We show that the proposed framework outperform state-of-the-art techniques in terms of quantitative quality metrics as well as qualitative visual results; FAPESP (processos nos. 2009/17801-0 e 2011/22749-8 e 2012/14021-7); CNPq (processo no. 302643/2013-3); NSF (subvenções IIS-0808718 e 0915661-CCF)

‣ "Segmentação de imagens e validação de classes por abordagem estocástica" ; Image segmentation and class validation in a stochastic approach

Gerhardinger, Leandro Cavaleri
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 13/04/2006 Português
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Uma etapa de suma importância na análise automática de imagens é a segmentação, que procura dividir uma imagem em regiões cujos pixels exibem um certo grau de similaridade. Uma característica que provê similaridade entre pixels de uma mesma região é a textura, formada geralmente pela combinação aleatória de suas intensidades. Muitos trabalhos vêm sendo realizados com o intuito de estudar técnicas não-supervisionadas de segmentação de imagens por modelos estocásticos, definindo texturas como campos aleatórios de Markov. Um método com esta abordagem que se destaca é o EM/MPM, um algoritmo iterativo que combina a técnica EM para realizar uma estimação de parâmetros por máxima verossimilhança com a MPM, utilizada para segmentação pela minimização do número de pixels erroneamente classificados. Este trabalho desenvolveu um estudo sobre a modelagem e a implementação do algoritmo EM/MPM, juntamente com sua abordagem multiresolução. Foram propostas uma estimação inicial de parâmetros por limiarização e uma combinação com o algoritmo de Annealing. Foi feito também um estudo acerca da validação de classes, ou seja, a busca pelo número de regiões diferentes na imagem, mostrando as principais técnicas encontradas na literatura e propondo uma nova abordagem...

‣ Redução no esforço de interação em segmentação de imagens digitais através de aprendizagem computacional; Reducing the interaction effort in digital image segmentation through machine learning

Klava, Bruno
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 08/10/2014 Português
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A segmentação é um passo importante em praticamente todas as tarefas que envolvem processamento de imagens digitais. Devido à variedade de imagens e diferentes necessidades da segmentação, a automação da segmentação não é uma tarefa trivial. Em muitas situações, abordagens interativas, nas quais o usuário pode intervir para guiar o processo de segmentação, são bastante úteis. Abordagens baseadas na transformação watershed mostram-se adequadas para a segmentação interativa de imagens: o watershed a partir de marcadores possibilita que o usuário marque as regiões de interesse na imagem; o watershed hierárquico gera uma hierarquia de partições da imagem sendo analisada, hierarquia na qual o usuário pode navegar facilmente e selecionar uma particular partição (segmentação). Em um trabalho prévio, propomos um método que integra as duas abordagens de forma que o usuário possa combinar os pontos fortes dessas duas formas de interação intercaladamente. Apesar da versatilidade obtida ao se integrar as duas abordagens, as hierarquias construídas dificilmente contêm partições interessantes e o esforço de interação necessário para se obter um resultado desejado pode ser muito elevado. Nesta tese propomos um método...

‣ Graph Laplacian for spectral clustering and seeded image segmentation; Estudo do Laplaciano do grafo para o problema de clusterização espectral e segmentação interativa de imagens

Casaca, Wallace Correa de Oliveira
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 05/12/2014 Português
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Image segmentation is an essential tool to enhance the ability of computer systems to efficiently perform elementary cognitive tasks such as detection, recognition and tracking. In this thesis we concentrate on the investigation of two fundamental topics in the context of image segmentation: spectral clustering and seeded image segmentation. We introduce two new algorithms for those topics that, in summary, rely on Laplacian-based operators, spectral graph theory, and minimization of energy functionals. The effectiveness of both segmentation algorithms is verified by visually evaluating the resulting partitions against state-of-the-art methods as well as through a variety of quantitative measures typically employed as benchmark by the image segmentation community. Our spectral-based segmentation algorithm combines image decomposition, similarity metrics, and spectral graph theory into a concise and powerful framework. An image decomposition is performed to split the input image into texture and cartoon components. Then, an affinity graph is generated and weights are assigned to the edges of the graph according to a gradient-based inner-product function. From the eigenstructure of the affinity graph, the image is partitioned through the spectral cut of the underlying graph. Moreover...

‣ Corte normalizado em grafos : um algoritmo aglomerativo para segmentação de imagens de colonias de bactérias= Normalized cut on graphs: an aglomerative algorithm for bacterial colonies image segmentation; Normalized cut on graphs : an aglomerative algorithm for bacterial colonies image segmentation

André Luís da Costa
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 22/02/2013 Português
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O problema de segmentação de colônias de bactérias em placas de Petri possui algumas características bem distintas daquelas encontradas, por exemplo, em problemas de segmentação de imagens naturais. A principal característica é o alto número de colônias que podem ser encontradas em uma placa. Desta forma, é primordial que o algoritmo de segmentação seja capaz de realizar a segmentação da imagem em um grande número de regiões. Este cenário extremo é ideal para analisar limitações dos algoritmos de segmentação. De fato, neste trabalho foi verificado que o algoritmo de corte normalizado original, que se fundamenta na teoria espectral de grafos, é inadequado para aplicações que exigem que a segmentação seja realizada em um grande número de regiões. Contudo, a utilização do critério de corte normalizado para segmentar imagens de colônias de bactérias ainda é possível graças a um novo algoritmo que está sendo introduzido neste trabalho. O novo algoritmo fundamenta-se no agrupamento hierárquico dos nós do grafo, ao invés de utilizar conceito da teoria espectral. Experimentos mostram também que o biparticionamento de um grafo pelo novo algoritmo apresenta um valor de corte normalizado médio cerca de 40 vezes menor que o biparticionamento pelo algoritmo baseado na teoria espectral.; The problem of bacteria colonies segmentation in Petri dishes has some very different characteristics from those found...

‣ Segmentação de imagens digitais combinando watershed e corte normalizado em grafos; Digital image segmentation combining watershed and normalized cut

Tiago Willian Pinto
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 26/02/2014 Português
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Em Visão Computacional, a importância da segmentação de imagens é comparável apenas à sua complexidade. Interpretar a semântica de uma imagem com exatidão envolve inúmeras variáveis e condições, o que deixa um vasto campo em aberto aos pesquisadores. O intuito deste trabalho é implementar um método de segmentação de imagens através da combinação de quatro técnicas de computação: A Transformação Watershed, o Watershed Hierárquico, o Contextual Spaces Algorithm e o Corte Normalizado. A Transformação Watershed é uma técnica de segmentação de imagens do campo da Morfologia Matemática baseada em crescimento de regiões e uma forma eficiente de implementá-la é através da Transformada Imagem-Floresta. Esta técnica produz uma super-segmentação da imagem, o que dificulta a interpretação visual do resultado. Uma das formas de simplificar e reduzir essa quantidade de regiões é através da construção de um espaço de escalas chamado Watershed Hierárquico, que agrupa regiões através de um limiar que representa uma característica do relevo. O Contextual Spaces Algorithm é uma técnica de reclassificação utilizada no campo de Busca de Imagens Baseado em contexto, e explora a similaridade entre os diferentes objetos de uma coleção através da análise do contexto entre elas. O Corte Normalizado é uma técnica que explora a análise do grau de dissimilaridade entre regiões e tem suas bases na teoria espectral dos grafos. O Watershed Hierárquico é uma abordagem multiescala de análise das regiões do watershed...

‣ Topological Active Model Optimization by Means of Evolutionary Methods for Image Segmentation

Novo Buján, Jorge
Fonte: Universidade da Corunha Publicador: Universidade da Corunha
Tipo: Trabalho em Andamento
Português
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Presentación de la tesis doctoral defendida por el autor.; Object localization and segmentation are tasks that have been growing in relevance in the last years. The automatic detection and extraction of possible objects of interest is a important step for a higher level reasoning, like the detection of tumors or other pathologies in medical imaging or the detection of the region of interest in fingerprints or faces for biometrics. There are many different ways of facing this problem in the literature, but in this Phd thesis we selected a particular deformable model called Topological Active Model. This model was especially designed for 2D and 3D image segmentation. It integrates features of region-based and boundary-based segmentation methods in order to perform a correct segmentation and, this way, fit the contours of the objects and model their inner topology. The main problem is the optimization of the structure to obtain the best possible segmentation. Previous works proposed a greedy local search method that presented different drawbacks, especially with noisy images, situation quite often in image segmentation. This Phd thesis proposes optimization approaches based on global search methods like evolutionary algorithms, with the aim of overcoming the main drawbacks of the previous local search method...

‣ Coding Theoretic Approach to Image Segmentation

Ndili, Unoma; Ndili, Unoma
Fonte: Universidade Rice Publicador: Universidade Rice
Tipo: Thesis; Text; Text
Português
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Masters Thesis; Using a coding theoretic approach, we achieve unsupervised image segmentation by implementing Rissanen's concept of Minimum Description Length for estimating piecewise homogeneous regions in images. MDL offers a mathematical foundation for balancing brevity of descriptions against their fidelity to the data. Our image model is a Gaussian random field whose mean and variance functions are piecewise constant. Our model is aimed at identifying regions of constant intensity (mean) and texture(variance). Based on a multi-scale encoding approach, we develop two different segmentation schemes. One algorithm is based on an adaptive (greedy) rectangular partitioning, while the second algorithm is an optimally-pruned wedgelet-decorated dyadic partitioning scheme. We compare the two algorithms with the more common signal plus constant noise schemes, which accounts for variations in mean only. We explore applications of our algorithms on Synthetic Aperture Radar (SAR) imagery. Based on our segmentation scheme, we implement a robust Constant False Alarm Rate (CFAR) detector towards Automatic Target Recognition (ATR) on Laser Radar (LADAR) and Infra-Red (IR) images.

‣ Coding Theoretic Approach to Image Segmentation

Ndili, Unoma; Nowak, Robert David; Figueiredo, Mario; Ndili, Unoma; Nowak, Robert David; Figueiredo, Mario
Fonte: Universidade Rice Publicador: Universidade Rice
Tipo: Conference paper; Text; Text
Português
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Conference paper; In this paper, using a coding theoretic approach, we implement Rissanen's concept of minimum description length (MDL) for segmenting an image into piecewise homogeneous regions. Our image model is a Gaussian random field whose mean and variance functions are piecewise constant across the image. The image pixels are (conditionally) independent and Gaussian, given the mean and variance functions. The model is intended to capture variations in both intensity (mean value) and texture (variance). We adopt a multi-scale tree based approach to develop two segmentation algorithms, using MDL to penalize overly complex segmentations. One algorithm is based on an adaptive (greedy) rectangular partitioning scheme. The second algorithm is an optimally-pruned wedgelet decorated dyadic partitioning. We compare the two schemes with an alternative constant variance dyadic CART (classification and regression tree) scheme which accounts only for variations in mean, and demonstrate their performance with SAR image segmentation problems.

‣ 3D model assisted image segmentation

Jayawardena, Srimal; Yang, Di; Hutter, Marcus
Fonte: IEEE Publicador: IEEE
Tipo: Conference paper
Português
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The problem of segmenting a given image into coherent regions is important in Computer Vision and many industrial applications require segmenting a known object into its components. Examples include identifying individual parts of a component for process control work in a manufacturing plant and identifying parts of a car from a photo for automatic damage detection. Unfortunately most of an object's parts of interest in such applications share the same pixel characteristics, having similar colour and texture. This makes segmenting the object into its components a non-trivial task for conventional image segmentation algorithms. In this paper, we propose a "Model Assisted Segmentation" method to tackle this problem. A 3D model of the object is registered over the given image by optimising a novel gradient based loss function. This registration obtains the full 3D pose from an image of the object. The image can have an arbitrary view of the object and is not limited to a particular set of views. The segmentation is subsequently performed using a level-set based method, using the projected contours of the registered 3D model as initialisation curves. The method is fully automatic and requires no user interaction. Also, the system does not require any prior training. We present our results on photographs of a real car.

‣ Techniques in helical scanning, dynamic imaging and image segmentation for improved quantitative analysis with X-ray micro-CT

Sheppard, Adrian; Latham, Shane; Middleton, Jill; Kingston, Andrew; Myers, Glenn; Varslot, Trond; Fogden, Andrew; Sawkins, Tim; Cruikshank, Ron; Saadatfar, Mohammad; Francois, Nicolas; Arns, Christoph; Senden, Tim
Fonte: Elsevier Publicador: Elsevier
Tipo: Artigo de Revista Científica Formato: 8 pages
Português
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This paper reports on recent advances at the micro-computed tomography facility at the Australian National University. Since 2000 this facility has been a significant centre for developments in imaging hardware and associated software for image reconstruction, image analysis and image-based modelling. In 2010 a new instrument was constructed that utilises theoretically-exact image reconstruction based on helical scanning trajectories, allowing higher cone angles and thus better utilisation of the available X-ray flux. We discuss the technical hurdles that needed to be overcome to allow imaging with cone angles in excess of 60°. We also present dynamic tomography algorithms that enable the changes between one moment and the next to be reconstructed from a sparse set of projections, allowing higher speed imaging of time-varying samples. Researchers at the facility have also created a sizeable distributed-memory image analysis toolkit with capabilities ranging from tomographic image reconstruction to 3D shape characterisation. We show results from image registration and present some of the new imaging and experimental techniques that it enables. Finally, we discuss the crucial question of image segmentation and evaluate some recently proposed techniques for automated segmentation.

‣ Um estudo comparativo de segmentação de imagens por aplicações do corte normalizado em grafos; A comparative study of image segmentation by application of normalized cut on graphs

Anselmo Castelo Branco Ferreira
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 17/01/2011 Português
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O particionamento de grafos tem sido amplamente utilizado como meio de segmentação de imagens. Uma das formas de particionar grafos é por meio de uma técnica conhecida como Corte Normalizado, que analisa os autovetores da matriz laplaciana de um grafo e utiliza alguns deles para o corte. Essa dissertação propõe o uso de Corte Normalizado em grafos originados das modelagens por Quadtree e Árvore dos Componentes a fim de realizar segmentação de imagens. Experimentos de segmentação de imagens por Corte Normalizado nestas modelagens são realizados e um benchmark específico compara e classifica os resultados obtidos por outras técnicas propostas na literatura específica. Os resultados obtidos são promissores e nos permitem concluir que o uso de outras modelagens de imagens por grafos no Corte Normalizado pode gerar melhores segmentações. Uma das modelagens pode inclusive trazer outro benefício que é gerar um grafo representativo da imagem com um número menor de nós do que representações mais tradicionais; The graph partitioning has been widely used as a mean of image segmentation. One way to partition graphs is through a technique known as Normalized Cut, which analyzes the graph's Laplacian matrix eigenvectors and uses some of them for the cut. This work proposes the use of Normalized Cut in graphs generated by structures based on Quadtree and Component Tree to perform image segmentation. Experiments of image segmentation by Normalized Cut in these models are made and a specific benchmark compares and ranks the results obtained by other techniques proposed in the literature. The results are promising and allow us to conclude that the use of other image graph models in the Normalized Cut can generate better segmentations. One of the structures can also bring another benefit that is generating an image representative graph with fewer graph nodes than the traditional representations

‣ Medical image segmentation using statistical and fuzzy object shape models = : Segmentação de imagens médicas usando modelos estatísticos e nebulosos da forma do objeto; Segmentação de imagens médicas usando modelos estatísticos e nebulosos da forma do objeto

Renzo Phellan Aro
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 28/11/2014 Português
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A segmentação de imagens médicas consiste de duas tarefas fortemente acopladas: reconhecimento e delineamento. O reconhecimento indica a localização aproximada de um objeto, enquanto o delineamento define com precisão sua extensão espacial na imagem. O reconhecimento também verifica a corretude do delineamento do objeto. Os seres humanos são superiores aos computadores na tarefa de reconhecimento, enquanto o contrário acontece no delineamento. A segmentação manual, por exemplo, é geralmente passível de erro, tediosa, demorada e sujeita à variabilidade. Portanto, os métodos de segmentação interativa mais eficaces limitam a intervenção humana ao reconhecimento. No caso das imagens médicas, os objetos podem ser as estruturas anatômicas do corpo humano, como órgãos, sistemas e tumores. Sua segmentação é uma fase fundamental para obter medidas, como seus tamanhos e distâncias, para poder realizar sua análise quantitativa. A visualização de suas formas em 3D também é importante para sua análise qualitativa. Ambas análises podem ajudar os especialistas a estudar os fenómenos anatômicos e fisiológicos do corpo humano, diferenciar situações normais e anormais, diagnosticar doenças, estabelecer tratamentos...

‣ An attribute-based image segmentation method

Andrade,M.C. de; Bertrand,G.; Araújo,A.A. de
Fonte: ABM, ABC, ABPol Publicador: ABM, ABC, ABPol
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/07/1999 Português
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This work addresses a new image segmentation method founded on Digital Topology and Mathematical Morphology grounds. The ABA (attribute based absorptions) transform can be viewed as a region-growing method by flooding simulation working at the scale of the main structures of the image. In this method, the gray level image is treated as a relief flooded from all its local minima, which are progressively detected and merged as the flooding takes place. Each local minimum is exclusively associated to one catchment basin (CB). The CBs merging process is guided by their geometric parameters as depth, area and/or volume. This solution enables the direct segmentation of the original image without the need of a preprocessing step or the explicit marker extraction step, often required by other flooding simulation methods. Some examples of image segmentation, employing the ABA transform, are illustrated for uranium oxide samples. It is shown that the ABA transform presents very good segmentation results even in presence of noisy images. Moreover, it's use is often easier and faster when compared to similar image segmentation methods.

‣ An Adaptive Color Image Segmentation

Deshmukh K.S.; Shinde G. N.
Fonte: Universidade Autônoma de Barcelona Publicador: Universidade Autônoma de Barcelona
Tipo: Artigo de Revista Científica Formato: application/pdf
Publicado em //2006 Português
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A novel Adaptive Color Image Segmentation (ACIS) System for color image segmentation is presented. The proposed ACIS system uses a neural network with architecture similar to the multilayer perceptron (MLP) network. The main difference is that neurons here uses a multisigmoid activation function. The multisigmoid function is the key for segmentation. The number of steps i.e. thresholds in the multisigmoid function are dependant on the number of clusters in the image. The threshold values for detecting the clusters and their labels are found automatically from the first order derivative of histograms of saturation and intensity in the HSV color space. Here, the main use of neural network is to detect the number of objects automatically from an image. The advantage of this method is that no a priori knowledge is required to segment the color image. ACIS label the objects with their mean colors. The algorithm is found to be reliable and works satisfactorily on different kinds of color images. Experimental results show that the performance of ACIS is robust on noisy images also.

‣ Evaluation of texture features for image segmentation

Serrano, Navid; Luo, Jiebo; Savakis, Andreas
Fonte: The Institute of Electrical and Electronics Engineers (IEEE) Publicador: The Institute of Electrical and Electronics Engineers (IEEE)
Tipo: Artigo de Revista Científica
Português
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Texture features are among the most commonly used image attributes in image understanding applications, such as image retrieval from databases. A number of methods and their variants have been developed over the years for texture feature extraction. Whereas they all have their merits and flaws, it is worthwhile to evaluate their performance in a specific application domain. The goal here is to establish which texture features are better suited for segmentation of natural scenes that contain multiple natural and synthetic textures. The performance of unsupervised texture segmentation based on multiresolution simultaneous autoregressive (MRSAR) models, wavelet coefficients, fractal dimension, edge direction and magnitude, and color moments is examined.; "Evaluation of texture features for image segmentation," Presented at the IEEE Western New York Image Processing Workshop 2001. The Institute of Electrical and Electronics Engineers. Held at the University of Rochester: Rochester, New York: 14 September 2001. ©2001 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists...

‣ 3-D image segmentation and rendering

Wang, Hui
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Tese de Doutorado
Português
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Finding methods for detecting objects in computer tomography images has been an active area of research in the medical and industrial imaging communities. While the raw image can be readily displayed as 2-D slices, 3-D analysis and visualization require explicitly defined object boundaries when creating 3-D models. A basic task in 3-D image processing is the segmentation of an image that classifies voxels/pixels into objects or groups. It is very computation intensive for processing because of the huge volume of data. The objective of this research is to find an efficient way to identify, isolate and enumerate 3-D objects in a given data set consisting of tomographic cross-sections of a device under test. In this research, an approach to 3-D image segmentation and rendering of CT data has been developed. Objects are first segmented from the background and then segmented between each other before 3-D rendering. During the first step of segmentation, current techniques of thresholding and image morphology provide a fast way to accomplish the work. During the second step of segmentation, a new method based on the watershed transform has been developed to deal with objects with deep connections. The new method takes advantage of the similarity between consecutive cross section images. The projections of the objects in the first image are taken as catchment basins for the second image. Only the different pixels in the second image are processed during segmentation. This not only saves time to find catchment basins...

‣ Grayscale Image Segmentation Based on Associative Memories

Guzmán Ramírez,Enrique; Jiménez,Ofelia M. C.; Pérez,Alejandro D.; Pogrebnyak,Oleksiy
Fonte: Centro de Investigación en computación, IPN Publicador: Centro de Investigación en computación, IPN
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/12/2011 Português
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In this paper, a grayscale image segmentation algorithm based on Extended Associative Memories (EAM) is proposed. The algorithm is divided into three phases. First, the uniform distribution of the image pixel values is determined by means of the histogram technique. The result of this phase is a set of regions (classes) where each one is grouped into a certain number of pixel values. Second, the EAM training phase is applied to the information obtained at the first phase. The result of the second phase is an associative network that contains the centroids group of each of the regions in which the image will be segmented. Finally, the centroid to which each pixel belongs is obtained using the EAM classification phase, and the image segmentation process is completed. A quantitative analysis and comparative performance for frequently-used image segmentation by the clustering method, the k-means, and the proposed algorithm when it uses prom and med operators are presented.