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. As a result, some critical disadvantages tightly coupled with those methods following the routine polynomial representation can be well overcome. To demonstrate this, a set of carefully designed comparison experiments is conducted to show the superiority of the newly proposed method to a representative method using the polynomial representation. Additionally, the practical effectiveness of the proposed method is further manifested using a set of real image data with a promising accuracy.

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. As glaucoma progresses, neural tissue dies, the nerve fiber layer thins, and the cup-to-disk ratio increases. Unfortunately, most current measurement techniques are subjective and inherently unreliable, making it difficult to monitor small changes in the nervehead geometry. To our knowledge, this paper presents the first published results on optic nervehead segmentation and geometric characterization from OCT data. We develop complete, autonomous algorithms based on a parabolic model of cup geometry and an extension of the Markov model introduced by Koozekanani, et al. to segment the retinal-nervehead surface, identify the choroid-nervehead boundary, and identify the extent of the optic cup. We present thorough experimental results from both normal and pathological eyes, and compare our results against those of an experienced, expert ophthalmologist, reporting a correlation coefficient for cup diameter above 0.8 and above 0.9 for the disk diameter.

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. This paper proposes a new approach that avoids these problems. To that end, there are two key, novel contributions in this work: a new, hybrid genetic algorithm (GA) technique, including hillclimbing and parallel-migration, combined with a new, robust evaluation metric based on surface interpenetration. Up to now, interpenetration has been evaluated only qualitatively; we define the first quantitative measure for it. Because they search in a space of transformations, GAs are capable of registering surfaces even when there is low overlap between them and without need for prealignment. The novel GA search algorithm we present offers much faster convergence than prior GA methods, while the new robust evaluation metric ensures more precise alignments, even in the presence of significant noise, than mean squared error or other well-known robust cost functions. The paper presents thorough experimental results to show the improvements realized by these two contributions.

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. MATERIALS AND METHODS: We developed an original and noninvasive method to help determination of gestational age based on information supplied by plantar surface images. These images present many details and patterns, but, to date, have not received attention from the image-processing community. We provide a computational tool with suitable facilities to allow the image analysis, either automatically or user driven. This image-processing tool is presented here. RESULTS: The image-processing tool was developed on a user-driven basis. However, as a quantitative experiment, 186 images were processed without user intervention to observe tool behavior in performing different tasks. Although preliminary, experimental results confirm the relationship between plantar surface features and gestational age. CONCLUSION: A prototype of the FootScanAge System is being used and evaluated by experts in neonatology. By means of digital processing of plantar surface images, some characteristics may be shown. Some hypotheses regarding the method have already been confirmed. Also, we show that some well-known image-processing techniques, if appropriately adapted, lead to suitable results when applied to plantar surface images.

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. These fine classifications assist government agencies in many ways including urban planning, transportation management, and rescue operations. In a step toward the automation of the fine classification process, this paper explores graph theoretical measures over grayscale images. The graphs are constructed by assigning photometric straight line segments to vertices, while graph edges encode their spatial relationships. We then introduce a set of measures based on various properties of the graph. These measures are nearly monotonic (positively correlated) with increasing structure (organization) in the image. Thus, increased cultural activity and land development are indicated by increases in these measures-without explicit extraction of road networks, buildings, residences, etc. These latter, time consuming (and still only partially automated) tasks can be restricted only to "promising" image regions, according to our measures. In some applications our measures may suffice. We present a theoretical basis for the measures followed by extensive experimental results in which the measures are first compared to manual evaluations of land development. We then present and test a method to focus on, and (pre)extract, suburban-style residential areas. These are of particular importance in many applications, and are especially difficult to extract. In this work, we consider commercial IKONOS data. These images are orthorectified to provide a fixed resolution of 1 meter per pixel on the ground. They are, therefore, metric in the sense that ground distance is fixed in scale to pixel distance. Our data set is large and diverse, including sea and coastline, rural, forest, residential, industrial, and urban areas.

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. Additionally, a genetic algorithm was specifically designed to accelerate the optimization process of surface extraction, while avoiding premature convergence. We present thorough experimental results with quantitative evaluation against ground truth. The segmentation algorithm was applied to three real range image databases and competes favorably against eleven other segmenters using the most popular evaluation framework in the literature. Our approach lends itself naturally to parallel implementation and application in real-time tasks. The method fits well, into several of today's applications in man-made environments, such as target detection and autonomous navigation, for which obstacle detection, but not description or reconstruction, is required. It can also be extended to process point clouds resulting from range image registration.

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. Involuntary eye motions frequently cause incorrect locations to be imaged. This motion may leave no obvious artifacts in the scan data and can easily go undetected. For glaucoma monitoring especially, knowing the measurement path, typically a circle concentric with the nerve head, is crucial. The commercially available OCT system displays a near-infrared video of the retina showing the SLD beam. This paper presents a prototype system to detect the nerve head and SLD beam in the video, and report the true scan path relative to the nerve head. Low image contrast and limited resolution make the reliable detection of retinal features difficult. In an adaptive model construction phase, the system directly detects retinal vasculature and the nerve head and incrementally builds a model of the current subject's vascular pattern relative to the optic disk. The nerve head identification is multitiered, using a novel dual eigenspace technique and a geometric comparison of detected vessel positions and nerve head hypotheses. In its operational phase, a correspondence is achieved between the currently detected vasculature and the model. Using subjects not included in training, the system located the optic nerve head to within 5 pixels (0.07 optic disk diameters, an error well below clinical significance) in 99.75% of 2800 video fields. In current clinical practice, motions as large as 1-2 disc diameters may go undetected, so this is a vast improvement.