Many approaches to Bayesian image segmentation have used maximum a posteriori (MAP) estimation in conjunction with Markov random fields (MRF). While this approach performs well, it has a number of disadvantages. In particular, exact MAP estimates cannot be computed, approximate MAP estimates are computationally expensive to compute, and unsupervised parameter estimation of the MRF is difficult. In this paper, we propose a new approach to Bayesian image segmentation which directly addresses these problems. The new method replaces the MRF model with a novel multiscale random field (MSRF), and replaces the MAP estimator with a sequential MAP (SMAP) estimator derived from a novel estimation criteria. Together, the proposed estimator and model result in a segmentation algorithm which is not iterative and can be computed in time proportional to MN where M is the number of classes and N is the number of pixels. We also develop a computationally effcient method for unsupervised estimation of m...

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We propose an image segmentation method based on texture analysis. Our method is composed of two parts. The first part determines a novel set of texture features derived from a Gaussian--Markov random fields (GMRF) model. Unlike a GMRFbased approach, our method does not employ model parameters as features or require the extraction of features for a fixed set of texture types a priori. The second part is a two-dimensional (2--D) array of locally excitatory globally inhibitory oscillator networks (LEGION). After being filtered for noise suppression, features are used to determine the local couplings in the network. When LEGION runs, the oscillators corresponding to the same texture tend to synchronize, whereas different texture regions tend to correspond to distinct phases. In simulations, a large system of differential equations is solved for the first time using a recently proposed method for integrating relaxation oscillator networks. We provide results on real texture images to demonstrate the performance of our method. Index Terms---Dynamical systems, Gaussian Markov random fields, LEGION, neural networks, relaxation oscillators, texture segmentation. I.

Abstract. Methods for digital-image texture analysis are reviewed based on available literature and research work either carried out or supervised by the authors. The review has been prepared on request of Dr Richard Lerski, Chairman of the Management Committee of the COST B11 action “Quantitation of Magnetic Resonance Image Texture”.

Abstract—The problem ofautomateddefect detection intextured materials is investigated. A new approach for defect detection using linear FIR filterswithoptimized energy separationis proposed. Performance of different feature separation criterion with reference to fabric defects has been evaluated. The issues relating to the design of optimal filters for supervised and unsupervised web inspection are addressed. A general web inspection system based on the optimal filters is proposed. The experiments on this new approach have yielded excellent results. The low computational requirement confirms the usefulness of the approach for industrial inspection. Index Terms—Defect detection, industrial inspection, optimized FIR filters, performance evaluation, quality assurance, textured defects.

The focus of this dissertation is on the design of two-dimensional lters for feature extraction, segmentation, and classi cation of digital images with textural content. The features are extracted by ltering with a linear lter and estimating the local energy of the lter response. The dissertation gives a review covering broadly most previous approaches to texture feature extraction and continues with proposals of several new techniques. Texture feature extraction using a quadrature mirror lter (QMF) bank is proposed, utilizing both critically sampled and full rate implementations. In the critically sampled case, tremendous computational savings can be realized. One of the major conclusions of the experiments is that it is possible to use sub-sampled lters with only a modest degradation in segmentation accuracy realizing considerable computational savings. Furthermore, the commonly used octave band decomposition is evaluated against alternative decompositions, concluding that non-octave decompositions are generally superior. The QMF lter bank features are tested on benchmark images, on document segmentation, and on image content search. The use of linear least squared prediction error lters is also proposed, yielding an optimal representation of the textures. Compared to non-optimized lter banks, this approach yields

Content-based image retrieval, which provides convenient ways to retrieve images from large image databases, has been studied actively. While many previous image retrieval techniques do not look at regions in an image, regionbased image retrieval techniques have been gaining attention recently. We propose a region-based image retrieval method which performs image segmentation and indexing using texture features computed from wavelet coefficients. The proposed method has advantages in texture feature extraction and hierarchical image segmentation over the previous region-based techniques using wavelet transform.

In medical imaging applications such as ultrasound, it is often required to segment the image to determine its meaningful parts. Segmentation may be simple differentiation between the background and the object, or more complexly, distinguishing different objects or parts of objects within each image. There are many methods available for segmenting an image. The number of these methods applicable to segmenting 2D ultrasound images is significantly reduced because of the presence of noise. It was found experimentally that textures detectable in ultrasound images are more appropriate features for segmentation than simple edges. Detection of edges is profoundly affected by the presence of noise. A segmentation algorithm based on texture discrimination is presented in this thesis. This algorithm uses a constrained optimization approach to produce a segmentation. Because this algorithm can not deal with varying texture region sizes effectively a multiresolution algorithm is developed. This...

This paper presents a novel algorithm for unsupervised texture segmentation. The proposed algorithm incorporates the Local Binary Pattern operator under a segmentation framework based on the Active Contour Without Edges model. The experiments performed, show that it can be used for fast segmentation of two-textured images, outperforming recent texture segmentation algorithms, with a segmentation quality that reaches 99 % on average. 1.

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