Computational Perceptual Features for Texture Representation and Retrieval

Abbadeni, Noureddine

doi:10.1109/tip.2010.2060345

Cited by 40 publications

(26 citation statements)

References 28 publications

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“…Na Recuperação de Imagens, essas análises da textura tornam possível a realização de cálculos matemáticos para, com valores numéricos, descrever uma imagem. Diversas características podem ser exploradas em Haralick [12] e Abbadeni [1], nas quais são propostos diversos descritores responsáveis pela obtenção dessas características presentes na textura. A função destes descritores é de descrever as texturas permitindo que, depois de processadas as características (contraste, homogeneidade, entre outras) é possível realizar cálculos matemáticos para encontrar a similaridade entre as imagens.…”

Section: Recuperação De Imagem Baseada No Conteúdo (Cbir)unclassified

“…A análise de Textura é tratada em Pedrini [15] e Gonzalez [11] relacionando vários cálculos com bases estatísticas a fim de obter valores das características. Porém, antes de descrever como funcionam os descritores de textura, é preciso demonstrar como funcionam o filtro de Gabor e os Descritores de Textura de Haralick [12] e Abbadeni [1], mencionado no processo para obtenção dos resultados.…”

Section: Análise De Texturasunclassified

“…Iniciando pelos descritores baseados na matriz de co-ocorrência, propostos por Haralick [12]. Apesar de serem antigos, seus descritores são utilizados como referência em muitas pesquisas recentes que visam à descrição da textura (Abbadeni [1] fig. 3.…”

Section: Descritores De Textura Baseados Na Matrix De Co-ocorênciaunclassified

“…Em janeiro de 2011, Abbadeni [1] teve o seu artigo publicado, propondo novos descritores de texturas baseados na função de autocorrelação. Dois de seus descritores foram estudados.…”

Section: Descritores De Textura Baseados Função De Autocorrelaçãounclassified

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Recuperação de Imagens baseadas na textura

Yamashita¹,

Fernandes²

2016

REPA

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Resumo O reconhecimento de características que descrevem uma imagem de maneira concisa e eficiente é uma tarefa relativamente complexa para ser executada. O objetivo deste trabalho é demonstrar como a descrição pode ser feita a através da textura de imagens. Será mostrado como os descritores de textura funcionam e com um estudo comparativo entre seus resultados foi realizado a fim de obter a melhor descrição. AbstractThe recognition of features that describe an image concisely and efficiently is a relatively complex task to be performed. The objective of this study is to demonstrate how an image description can be obtained through the texture images. It will be demonstrated how the texture descriptors operate and a comparative study between the results was carried out to obtain the best description.

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Section: Recuperação De Imagem Baseada No Conteúdo (Cbir)unclassified

Section: Análise De Texturasunclassified

Section: Descritores De Textura Baseados Na Matrix De Co-ocorênciaunclassified

“…Em janeiro de 2011, Abbadeni [1] teve o seu artigo publicado, propondo novos descritores de texturas baseados na função de autocorrelação. Dois de seus descritores foram estudados.…”

Section: Descritores De Textura Baseados Função De Autocorrelaçãounclassified

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Recuperação de Imagens baseadas na textura

Yamashita¹,

Fernandes²

2016

REPA

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“…The method is applied for texture segmentation. Abbadeni [1] proposes a perceptual model to estimate textural feature set using psychometric method. The computational features are applied for content based image retrieval on grayscale images.…”

Section: Introductionmentioning

confidence: 99%

Statistical Learning for Texture Characterization

Goswami

Agarwal

Rao

2014

Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing

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In this paper, we propose a novel method for image texture characterization. Characterization is governed by simple perceptual variations in relative orientations in terms of either no variations present or variations present as row specific, column specific or diagonal specific. This generalization is obtained by modeling the input as a whole or image blocks depending on the broader or narrow coverage respectively. Most of the texture characterization is done either keeping a specific domain (synthetic or natural images specific to a category) or is application specific (segmentation on specific benchmark dataset or image retrieval). Contrary to this, our method is not biased towards any domain or application; rather it acts as a pre-processing step for guiding towards locating both non-textural and orientation specific textural image blocks. The proposed method quantifies the texture-tonal characterization of an image or image blocks using statistical ANOVA grading system. Once the grading for abstraction is assigned for both image as a whole and also for image blocks, the decision as to which higher-level algorithms need to be implemented on which block will become easier. The proposed method can be considered to be a three stage process -progressive sampling, image partitioning in blocks, ANOVA analysis and grading.

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A new directionality tool for assessing microtubule pattern alterations

Liu

Ralston

2014

Cytoskeleton

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The cytoskeleton (microtubules, actin and intermediate filaments) has a cell type-specific spatial organization that is essential and reflects cell health. We are interested in understanding how changes in the organization of microtubules contribute to muscle diseases such as Duchenne muscular dystrophy (DMD). The grid-like immunofluorescence microtubule pattern of fast-twitch muscle fibers lends itself well to visual assessment. The more complicated pattern of other fibers does not. Furthermore, visual assessment is not quantitative. Therefore we have developed a robust software program for detecting and quantitating microtubule directionality. Such a tool was necessary because existing methods focus mainly on local image features and are not well suited for microtubules. Our tool, TeDT, is based on the Haralick texture method and takes into account both local and global features with more weight on the latter. The results are expressed in a graphic form responsive to subtle variations in microtubule distribution, while a numerical score allows quantitation of directionality. Furthermore, the results are not affected by imaging conditions or post-imaging procedures. TeDT successfully assesses test images and microtubules in fast-twitch fibers of wild-type and mdx mice (a model for DMD); TeDT also identifies and quantitates microtubule directionality in slow-twitch fibers, in the fibers of young animals, and in other mouse models which could not be assessed visually. TeDT might also contribute to directionality assessments of other cytoskeletal components.

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Computational Perceptual Features for Texture Representation and Retrieval

Cited by 40 publications

References 28 publications

Recuperação de Imagens baseadas na textura

Recuperação de Imagens baseadas na textura

Statistical Learning for Texture Characterization

A new directionality tool for assessing microtubule pattern alterations

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