2,244 research outputs found
Combining local regularity estimation and total variation optimization for scale-free texture segmentation
Texture segmentation constitutes a standard image processing task, crucial to
many applications. The present contribution focuses on the particular subset of
scale-free textures and its originality resides in the combination of three key
ingredients: First, texture characterization relies on the concept of local
regularity ; Second, estimation of local regularity is based on new multiscale
quantities referred to as wavelet leaders ; Third, segmentation from local
regularity faces a fundamental bias variance trade-off: In nature, local
regularity estimation shows high variability that impairs the detection of
changes, while a posteriori smoothing of regularity estimates precludes from
locating correctly changes. Instead, the present contribution proposes several
variational problem formulations based on total variation and proximal
resolutions that effectively circumvent this trade-off. Estimation and
segmentation performance for the proposed procedures are quantified and
compared on synthetic as well as on real-world textures
Multiresolution analysis using wavelet, ridgelet, and curvelet transforms for medical image segmentation
Copyright @ 2011 Shadi AlZubi et al. This article has been made available through the Brunel Open Access Publishing Fund.The experimental study presented in this paper is aimed at the development of an automatic image segmentation system for classifying region of interest (ROI) in medical images which are obtained from different medical scanners such as PET, CT, or MRI. Multiresolution analysis (MRA) using wavelet, ridgelet, and curvelet transforms has been used in the proposed segmentation system. It is particularly a challenging task to classify cancers in human organs in scanners output using shape or gray-level information; organs shape changes throw different slices in medical stack and the gray-level intensity overlap in soft tissues. Curvelet transform is a new extension of wavelet and ridgelet transforms which aims to deal with interesting phenomena occurring along curves. Curvelet transforms has been tested on medical data sets, and results are compared with those obtained from the other transforms. Tests indicate that using curvelet significantly improves the classification of abnormal tissues in the scans and reduce the surrounding noise
Image interpolation and denoising in discrete wavelet transform domain
Traditionally, processing a compressed image requires decompression first. Following the related manipulations, the processed image is compressed again for storage. To reduce the computational complexity and processing time, manipulating images in the transform domain, which is possible, is an efficient solution; The uniform wavelet thresholding is one of the most widely used methods for image denoising in the Discrete Wavelet Transform (DWT) domain. This method, however, has the drawback of blurring the edges and the textures of an image after denoising. A new algorithm is proposed in this thesis for image denoising in the DWT domain with no blurring effect. This algorithm uses a suite of feature extraction and image segmentation techniques to construct filter masks for denoising. The novelty of the algorithm is that it directly extracts the edges and texture details of an image from the spatial information contained in the LL subband of DWT domain rather than detecting the edges across multiple scales. An added advantage of this method is the substantial reduction in computational complexity. Experimental results indicate that the new algorithm would yield higher quality images (both qualitatively and quantitatively) than the existing methods; In this thesis, new algorithm for image interpolation in the DWT domain is also discussed. Being different from other methods for interpolation, which focus on Haar wavelet, new interpolation algorithm also investigates other wavelets, such as Daubecuies and Bior. Experimental results indicate that the new algorithm is superior to the traditional methods by comparing the time complexity and quality of the processed image
Self-Similar Anisotropic Texture Analysis: the Hyperbolic Wavelet Transform Contribution
Textures in images can often be well modeled using self-similar processes
while they may at the same time display anisotropy. The present contribution
thus aims at studying jointly selfsimilarity and anisotropy by focusing on a
specific classical class of Gaussian anisotropic selfsimilar processes. It will
first be shown that accurate joint estimates of the anisotropy and
selfsimilarity parameters are performed by replacing the standard 2D-discrete
wavelet transform by the hyperbolic wavelet transform, which permits the use of
different dilation factors along the horizontal and vertical axis. Defining
anisotropy requires a reference direction that needs not a priori match the
horizontal and vertical axes according to which the images are digitized, this
discrepancy defines a rotation angle. Second, we show that this rotation angle
can be jointly estimated. Third, a non parametric bootstrap based procedure is
described, that provides confidence interval in addition to the estimates
themselves and enables to construct an isotropy test procedure, that can be
applied to a single texture image. Fourth, the robustness and versatility of
the proposed analysis is illustrated by being applied to a large variety of
different isotropic and anisotropic self-similar fields. As an illustration, we
show that a true anisotropy built-in self-similarity can be disentangled from
an isotropic self-similarity to which an anisotropic trend has been
superimposed
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