The Experts below are selected from a list of 9903 Experts worldwide ranked by ideXlab platform
M.a. Patel - One of the best experts on this subject based on the ideXlab platform.
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Classification of rotated and scaled Textured Images using Gaussian Markov random field models
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991Co-Authors: F.s. Cohen, M.a. PatelAbstract:Consideration is given to the problem of classifying a test Textured Image that is obtained from one of C possible parent texture classes, after possibly applying unknown rotation and scale changes to the parent texture. The training texture Images (parent classes) are modeled by Gaussian Markov random fields (GMRFs). To classify a rotated and scaled test texture, the rotation and scale changes are incorporated in the texture model through an appropriate transformation of the power spectral density of the GMRF. For the rotated and scaled Image, a bona fide likelihood function that shows the explicit dependence of the likelihood function on the GMRF parameters, as well as on the rotation and scale parameters, is derived. Although, in general, the scaled and/or rotated texture does not correspond to a finite-order GMRF, it is possible nonetheless to write down a likelihood function for the Image data. The likelihood function of the discrete Fourier transform of the Image data corresponds to that of a white nonstationary Gaussian random field, with the variance at each pixel (i,j) being a known function of the rotation, the scale, the GMRF model parameters, and (i,j). The variance is an explicit function of the appropriately sampled power spectral density of the GMRF. The estimation of the rotation and scale parameters is performed in the frequency domain by maximizing the likelihood function associated with the discrete Fourier transform of the Image data. Cramer-Rao error bounds on the scale and rotation estimates are easily computed. A modified Bayes decision rule is used to classify a given test Image into one of C possible texture classes. The classification power of the method is demonstrated through experimental results on natural textures from the Brodatz album.
Eiji Kawaguchi - One of the best experts on this subject based on the ideXlab platform.
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Textured Image segmentation using mrf in wavelet domain
International Conference on Image Processing, 2000Co-Authors: Hideki Noda, Mehdi N. Shirazi, Eiji KawaguchiAbstract:One difficulty of Textured Image segmentation in the past was the lack of computationally efficient models which can capture statistical regularities of textures over large distances. Recently, to overcome this difficulty, Bayesian approaches capitalizing on computational efficiency of multiscale representations have received attention. Most Previous research has been based on multiscale stochastic models which use the Gaussian pyramid decomposition as Image decomposition scheme. In this paper, motivated by nonredundant directional selectivity and highly discriminative nature of the wavelet representation, we present an unsupervised Textured Image segmentation algorithm which is based on a multiscale stochastic modeling over the wavelet decomposition of Image. For the sake of computational efficiency, versions of the EM algorithm and MAP estimate, which are based on the mean-field decomposition of a posteriori probability, are used for estimating model parameters and the segmented Image, respectively.
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mean field decomposition of a posteriori probability for mrf based unsupervised Textured Image segmentation
International Conference on Acoustics Speech and Signal Processing, 1999Co-Authors: Hideki Noda, Mehdi N. Shirazi, Bing Zhang, Eiji KawaguchiAbstract:This paper proposes a Markov random field (MRF) model-based method for unsupervised segmentation of multispectral Images consisting of multiple textures. To model such Textured Images, a hierarchical MRF is used with two layers, the first layer representing an unobservable region Image and the second layer representing multiple textures which cover each region. This method uses the expectation-maximization (EM) method for model parameter estimation, where in order to overcome the well-noticed computational problem in the expectation step, we approximate the Baum function using mean-field-based decomposition of a posteriori probability. Given provisionally estimated parameters at each iteration in the EM method, a provisional segmentation is carried out using local a posteriori probability of each pixel's region label, which is derived by mean-field-based decomposition of a posteriori probability of the whole region Image.
Oleh J Tretiak - One of the best experts on this subject based on the ideXlab platform.
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unsupervised Textured Image segmentation
International Conference on Acoustics Speech and Signal Processing, 1992Co-Authors: G K Gregoriou, Oleh J TretiakAbstract:A new algorithm for unsupervised Textured Image segmentation is presented. The Image comprises M Textured regions, each of which is modeled by a stationary Gaussian Markov random field. A feature vector is computed for each pixel in the original Image where these vectors are normally distributed and cluster about some vector means. Thus, the problem is reduced to one of restoring a vector valued underlying field embedded in additive Gaussian noise. The vector means corresponding to the different regions are estimated by using the expectation-maximization (EM) algorithm. An iterative algorithm is used with the underlying field modeled as a multilevel logistic Markov random field. The results obtained on two-region and four-region Textured Images are impressive, and the classification error is less than 3%. The algorithm is not limited to Textured Images but can also be applied to any vector-valued signals. >
N Palanivel - One of the best experts on this subject based on the ideXlab platform.
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texture characterization representation description and classification based on full range gaussian markov random field model with bayesian approach
International Journal of Image and Data Fusion, 2013Co-Authors: K Seetharaman, N PalanivelAbstract:A statistical approach, based on full range Gaussian Markov random field model, is proposed for texture analysis such as texture characterization, unique representation, description, and classification. The parameters of the model are estimated based on the Bayesian approach. The estimated parameters are utilized to compute autocorrelation coefficients. The computed autocorrelation coefficients fall in between –1 and +1. The coefficients are converted into decimal numbers using a simple transformation. Based on the decimal numbers, two texture descriptors are proposed: (i) texnum, the local descriptor; (ii) texspectrum, the global descriptor. The decimal numbers are proposed to represent the textures present in a small Image region. These numbers uniquely represent the texture primitives. The Textured Image under analysis is represented globally by observing the frequency of occurrences of the texnums called texspectrum. The textures are identified and are distinguished from unTextured regions with edges....
Ruth Rosenholtz - One of the best experts on this subject based on the ideXlab platform.
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general purpose localization of Textured Image regions
Human Vision and Electronic Imaging Conference, 1999Co-Authors: Ruth RosenholtzAbstract:In computer vision and Image processing, we often perform different processing on 'objects' than on 'texture'. In order to do this, we must have a way of localizing Textured regions of an Image. For this purpose, we suggest a working definition of texture: Texture is a substance that is more compactly represented by its statistics than by specifying the configuration of its parts. Texture, by this definition, is stuff that seems to belong to the local statistics. Outliers, on the other hand, seem to deviate from the local statistics, and tend to draw our attention, or 'pop out'. This definition suggests that to find texture we first extract certain basic features and compute their local statistics. Then we compute a measure of saliency, or degree to which each portion of the Image seems to be an outlier to the local feature distribution, and label as texture the regions with low saliency. We present a method, based upon this idea, for labeling points in natural scenes as belonging to texture regions. This method is based upon recent psychophysics results on processing of texture and popout.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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general purpose localization of Textured Image regions
Neural Information Processing Systems, 1998Co-Authors: Ruth RosenholtzAbstract:We suggest a working definition of texture: Texture is stuff that is more compactly represented by its statistics than by specifying the configuration of its parts. This definition suggests that to find texture we look for outliers to the local statistics, and label as texture the regions with no outliers. We present a method, based upon this idea, for labeling points in natural scenes as belonging to texture regions, while simultaneously allowing us to label lowlevel, bottom-up cues for visual attention. This method is based upon recent psychophysics results on processing of texture and popout.
