A new model-based estimation of ellipses for object representation.

Fitting geometric models to objects of interest in images is one of the most classical problems studied in computer vision field. As a result of its strong representation power and flexibility, conic is one of the geometric primitives widely used in a large number of image analysis applications, in practice. As opposed to most existing conic fitting methods minimizing the fitting error with the use of the second order polynomial representation, in this paper, we propose a new method that formulates the geometric fitting problem as a process of seeking for the optimal mapping to a bivariate normal distribution model.

Automatic recovery of the optic nervehead geometry in optical coherence tomography.

Optical coherence tomography (OCT) uses retroreflected light to provide micrometer-resolution, cross-sectional scans of biological tissues. OCT's first application was in ophthalmic imaging where it has proven particularly useful in diagnosing, monitoring, and studying glaucoma. Diagnosing glaucoma is difficult and it often goes undetected until significant damage to the subject's visual field has occurred.

Precision range image registration using a robust surface interpenetration measure and enhanced genetic algorithms.

This paper addresses the range image registration problem for views having low overlap and which may include substantial noise. The current state of the art in range image registration is best represented by the well-known iterative closest point (ICP) algorithm and numerous variations on it. Although this method is effective in many domains, it nevertheless suffers from two key limitations: It requires prealignment of the range surfaces to a reasonable starting point and it is not robust to outliers arising either from noise or low surface overlap.

Using computer vision to help the determination of the gestational age of newborns.

Rationale And Objectives: This report presents a computational approach to help the gestational age determination of newborns. Gestational age knowledge is fundamental to guide postnatal treatment and increase survival chances of newborns. However, current methods are invasive and do not generate precise results, mainly because they were developed based on nonpremature populations.

A theoretical and experimental investigation of graph theoretical measures for land development in satellite imagery.

Today's commercial satellite images enable experts to classify region types in great detail. In previous work, we considered discriminating rural and urban regions [23]. However, a more detailed classification is required for many purposes.

Range image segmentation into planar and quadric surfaces using an improved robust estimator and genetic algorithm.

This paper presents a novel range image segmentation method employing an improved robust estimator to iteratively detect and extract distinct planar and quadric surfaces. Our robust estimator extends M-estimator Sample Consensus/Random Sample Consensus (MSAC/RANSAC) to use local surface orientation information, enhancing the accuracy of inlier/outlier classification when processing noisy range data describing multiple structures. An efficient approximation to the true geometric distance between a point and a quadric surface also contributes to effectively reject weak surface hypotheses and avoid the extraction of false surface components.

Tracking the optic nervehead in OCT video using dual eigenspaces and an adaptive vascular distribution model.

Optical coherence tomography (OCT) is a new ophthalmic imaging modality generating cross sectional views of the retina. OCT systems are essentially Michelson interferometers that form images in 1.5 s by directing a superluminescent diode (SLD) beam over the retinal surface.