Electronic Letters on Computer Vision and Image Analysis (ELCVIA - Universitat Autònoma de Barcelona)
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343 research outputs found
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MRF-based image segmentation using Ant Colony System
Full text linkIn this paper, we propose a novel method for image segmentation that we call ACS-MRF method. ACS-MRF is a hybrid ant colony system coupled with a local search. We show how a colony of cooperating ants are able to estimate the labels field and minimize the MAP estimate. Cooperation between ants is performed by exchanging information through pheromone updating. The obtained results show the efficiency of the new algorithm, which is able to compete with other stochastic optimization methods like Simulated annealing and Genetic algorithm in terms of solution quality
An Appearance-Based Method for Parametric Video Registration
Full text linkIn this paper we address the problem of multi frame video registration using the combination of an appearance-based technique and a parametric model of the transformations. This technique uses an image that is selected as reference frame, and therefore, estimates the transformation that occurred to each frame in the sequence respect to this absolute referenced one. Both global and local information are employed to the estimation of these registered images. Global information is applied in terms of linear appearance subspace constraints, under the subspace constancy assumption [4], where variabilities of each frame respect to the reference frame are encoded. Local information is used by means of a polynomial parametric model that estimates the velocities field evoluton in each frame. The objective function to be minimized considers both issues at the same time, i.e., the appearance representation and the time evolution across the sequence. This function is the connection between the global coordinates in the subspace representation and the time evolution and the parametric optical flow estimates. Thus, the appearance constraints result to take into account al the images in a sequence in order to estimate the transformation parameters
Classification of breast mass abnormalities using denseness and architectural distortion
Full text linkoai:elcvia.revistes.uab.cat:article/59This paper presents an electronic second opinion system for the classification of mass abnormalities in mammograms into benign and malignant categories. This system is designed to help radiologists to reduce the number of benign breast cancer biopsies. Once a mass abnormality is detected and marked on a mammogram by a radiologist, two textural features, named denseness and architectural distortion, are extracted from the marked area. The denseness feature provides a measure of radiographic denseness of the marked area, whereas the architectural distortion feature provides a measure of its irregularity. These features are then fed into a neural network classifier. Receiver operating characteristic (ROC) analysis was conducted to evaluate the system performance. The area under the ROC curve reached 0.90 for the DDSM database consisting of 404 biopsy proven masses. A sensitivity analysis was also performed to examine the robustness of the introduced texture features to variations in sizes of abnormality markings