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This paper presents a novel method to recover 3D structure of the optic disc in the retina from two uncalibrated fundus images. Retinal images are commonly uncalibrated when acquired clinically, creating rectification challenges as well as significant radiometric and blur differences within the stereo pair. By exploiting structural peculiarities of the retina, we modified the Graph Cuts computational...
This paper provides a comprehensive quantitative comparison of metrics for detecting visual anomalies between two videos that are recorded along same path but at different times by a camera on a patrolling platform. The metrics used in this paper are histogram based metrics, statistic based metrics and pixel differences based metrics. We test the metrics for the detection of mobile and stationary...
This paper addresses the frame-to-frame data association and state estimation problems in localization of a pedestrian relative to a moving vehicle from a far infra-red video sequence. In a novel application of the hierarchical model-based motion estimation framework, we are able to solve the frame-to-frame data association problem as well as estimate a sub-pixel accurate height ratio for a pedestrian...
This paper addresses the frame-to-frame data association and state estimation problems in localization of a pedestrian relative to a moving vehicle from a monocular far infra-red video sequence. Using a novel application of the hierarchical model-based motion estimation framework, we are able to use the image appearance information to solve the frame-to-frame data association problem and estimate...
We present a new multi-stage algorithm for car and truck detection from a moving vehicle. The algorithm performs a search for pertinent features in three dimensions, guided by a ground plane and lane boundary estimation sub-system, and assembles these features into vehicle hypotheses. A number of classifiers are applied to the hypotheses in order to remove false detections. Quantitative analysis on...
To take advantage of both stereo cameras and radar, this paper proposes a fusion approach to accurately estimate the location, size, pose, and motion information of a threat vehicle with respect to a host one from observations that are obtained by both sensors. To do that, we first fit the contour of a threat vehicle from stereo depth information and find the closest point on the contour from the...
We describe a low-cost vision-based sensing and positioning system that enables intelligent vehicles of the future to autonomously drive in an urban environment with traffic. The system was built by integrating Sarnoff's algorithms for driver awareness and vehicle safety with commercial off-the-shelf hardware on a robot vehicle. We implemented a modular and parallelized software architecture that...
To take the advantages of both stereo cameras and radar, this paper proposes a fusion approach to accurately estimate the location, size, pose and motion information of a threat vehicle with respect to the host from observations obtained by both sensors. To do that, we first fit the contour of a threat vehicle from stereo depth information, and find the closest point on the contour from the vision...
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