We address the problem of matching images with disparate appearance arising from factors like dramatic illumination (day vs. night), age (historic vs. new) and rendering style differences. The lack of local intensity or gradient patterns in these …
We propose an adaptive rendering approach for large-scale skyline characterization and matching with applications to automated geo-tagging of photos and images. Given an image, our system automatically extracts the skyline and then matches it to a …
Matching street-level images to a database of airborne images is hard because of extreme viewpoint and illumination differences. Color/gradient distributions or local descriptors fail to match forcing us to rely on the structure of self-similarity of …
Analyzing change in the 3D structure of the optic disc over time has long been recognized as central to the diagnosis of glaucoma but has been inadequately addressed by computer vision methods. Currently, clinicians examine stereo pairs from …
We present a novel LIDAR streaming architecture for real-time, on-board processing using unmanned robots. We propose a two-level 3D data structure that allows pipelined and streaming processing of the 3D data as it arrives from a moving robot: (i) at …
We study the feasibility of solving the challenging problem of geolocalizing ground level images in urban areas with respect to a database of images captured from the air such as satellite and oblique aerial images. We observe that comprehensive …
We present a real-time pedestrian detection system based on structure and appearance classification. We discuss several novel ideas that contribute to having low-false alarms and high detection rates, while at the same time achieving computational …
Localizing blood vessels in eye images is a crucial step in the automated and objective diagnosis of eye diseases. Most previous research has focused on extracting the centerlines of vessels in large field of view images. However, for diagnosing …