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Stereo Vision processing is a critical component of Augmented Reality systems that rely on the precise depth map of a scene to properly place computer generated objects with real life video. Important aspects of the stereo processing are the creation of a dense depth map, high boundary precision, low latency and low power. We present an embedded system for Stereo Vision Processing based on a custom...
This paper presents an on-the-move pedestrian detection system that utilizes multiple sensor modalities to improve detection rates at deployable computational loads. The system was developed for a vehicle moving up to 40 kph that can detect moving pedestrians up to a distance of 50m, with support for both day and night operations. In the day, 3D pointclouds obtained from an 8-layer LIDAR sensor are...
High-performance dense stereo is a critical component of computer vision applications like 3D reconstruction, robot navigation, and augmented reality. In this paper, we present a low-power, high performance FPGA implementation of a stereo algorithm suitable for embedded real-time platforms. The design is sca lable for higher resolution images and frame rates and supporting different cameras and application...
We present a novel computationally efficient approach to obstacle detection that is applicable to both structured (e.g. indoor, road) and unstructured (e.g. off-road, grassy terrain) environments. In contrast to previous works that attempt to explicitly identify obstacles, we explicitly detect scene regions that are traversable - safe for the robot to go to - from its current position. Traversability...
We present a novel computationally efficient approach to obstacle detection that is applicable to both structured (e.g. indoor, road) and unstructured (e.g. off-road, grassy terrain) environments. In contrast to previous works that attempt to explicitly identify obstacles, we explicitly detect scene regions that are traversable - safe for the robot to go to - from its current position. Traversability...
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 different time instants for interval changes indicative of glaucoma. Due to the clinical procedures in capturing optic disc images, these stereo pairs are usually...
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 diseases of the optic disk region, like glaucoma, small field of view images have to be analyzed. One needs to identify not only the centerlines, but also...
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