In this paper, we focus on statistical region-based active contour models where image features (e.g. intensity) are random variables whose distribution belongs to some parametric family (e.g. exponential) rather than confining ourselves to the special Gaussian case. In the framework developed in this paper, we consider the general case of region-based terms involving functions of parametric probability densities, for which the anti-log-likelihood function is a special case. Using shape derivative tools, our effort focuses on constructing a general expression for the derivative of the energy (with respect to a domain), and on deriving the corresponding evolution speed. More precisely, we first show by a counterexample that the estimator of the distribution parameters is crucial for the derived speed expression. On the one hand, when using the maximum likelihood (ML) estimator for these parameters, the evolution speed has a closed-form expression that depends simply on the probability density function. On the other hand, complicating additive terms appear when using other estimators, e.g. method of moments. We then proceed by stating a general result within the framework of multi-parameter exponential family. This result is specialized to the case of the antilog-likelihood score with the ML estimator and to the case of the relative entropy. Experimental results on simulated data confirm our expectations that using the appropriate noise model leads to the best segmentation performance. We also report preliminary experiments on real life Synthetic Aperture Radar (SAR) images to demonstrate the potential applicability of our approach.
In this paper, we propose to combine formally noise and shape priors in region-based active contours. On the one hand, we use the general framework of exponential family as a prior model for noise. On the other hand, translation and scale invariant Legendre moments are considered to incorporate the shape prior (e.g. fidelity to a reference shape). The combination of the two prior terms in the active contour functional yields the final evolution equation whose evolution speed is rigorously derived using shape derivative tools. Experimental results on both synthetic images and real life cardiac echography data clearly demonstrate the robustness to initialization and noise, flexibility and large potential applicability of our segmentation algorithm.
In this paper we propose a brief survey on geometric variational approaches and more precisely on statistical region-based active contours for medical image segmentation. In these approaches, image features are considered as random variables whose distribution may be either parametric, and belongs to the exponential family, or non-parametric estimated with a kernel density method.Statistical region-based terms are listed and reviewed showing that these terms can depict a wide spectrum of segmentation problems. A shape prior can also be incorporated to the previous statistical terms. A discussion of some optimization schemes available to solve the variational problem is also provided. Examples on real medical images are given to illustrate some of the given criteria.
Abstract-A great deal of features detectors and descriptors are proposed every years for several computer vision applications. In this paper, we concentrate on dense detector applied to different descriptors. Eight descriptors are compared, three from gradient based family (SIFT, SURF, DAISY), others from binary category (BRIEF, ORB, BRISK, FREAK and LATCH). These descriptors are created and defined with certain invariance properties. We want to verify their invariances with various geometric and photometric transformations, varying one at a time. Deformations are computed from an original image. Descriptors are tested on five transformations: scale, rotation, viewpoint, illumination plus reflection. Overall, descriptors display the right invariances. This paper's objective is to establish a reproducible protocol to test descriptors invariances.
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