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Extracting building rooftops in satellite/aerial images is one of the most challenging problems in the application of computer vision for remote sensing. In this paper a new contour propagation model for rooftop boundary detection is proposed. It includes developing contour models that evolve by leaping on image corners and edge points while minimizing an energy function based on image corner responses,...
Road detection is a vital task for the development of autonomous vehicles. The knowledge of the free road surface ahead of the target vehicle can be used for autonomous driving, road departure warning, as well as to support advanced driver assistance systems like vehicle or pedestrian detection. Using vision to detect the road has several advantages in front of other sensors: richness of features,...
This paper proposes a novel approach to boost a set of Associated Pairing Comparison Features (APCFs) in Granular Space for pedestrian detection, in which Pairing Comparison of Color (PCC) and Pairing Comparison of Gradient (PCG) are two kinds of essential elements. A PCC is a Boolean color comparison of two granules and a PCG is a Boolean gradient comparison of two granules, which is motivated by...
In the light of the problem of monitoring forest fire, the design strategy and practical implementation of establishing the monitor system based on digital image information are proposed. The system is based on the CCD configuration characteristics and color information to detect and locate fire. Manned lookout posts are commonly installed in the forests all around the world. In this project, a system...
In this paper, we present a semi supervised approach to space carving. We do this by casting the recovery of volumetric data from multiple views into an evidence combining setting. The method presented here is statistical in nature and employs, as a starting point, a manually obtained contour. By making use of this user-provided information, we learn a prior distribution that is then used to compute...
Robust and real time moving object tracking is a tricky job in computer vision problems. Particle filtering has been proven very successful for non-Gaussian and non-linear estimation problems. In this paper, we first try to develop a color based particle filter. In this approach, the object tracking system relies on the deterministic search of window, whose color content matches a reference histogram...
We present a novel object localization approach based on the global structure constraint model (GSC) and optimal algorithm. In GSC, Objects are described as constellations of points satisfied with their specific global structure constraints. The spatial relations among all the patches having stable color information and their representative color information around patches are encoded. Then, the searching...
Complex motion makes consecutive frames experience dramatic change, and thus becomes a barrier to object-tracking. Three factors contribute to more complexity of motion: longer sampling period,an moving object with complex appearance and nonrestraint movement, occlusion, which causes mean shift algorithm losing its target due to too low a Bhattacharyya coefficient. To treat it, mean shift algorithm...
In this paper, we propose a new method for detecting and extracting moving objects from moving stereo camera. Our purpose is not only detecting objects but also extracting shapes and colors of detected moving objects from stereo video streams. First, we estimate the camera motion from three-dimensional optical flow based on RANSAC. Secondly we detect moving objects from the scene by inter-frame subtraction...
This article presents a laboratory computer vision system for vehicle detection and tracking. Vehicles are marked with colored circles, that are detected by the presented computer vision system. Detection of targets is performed by their shape using circular Hough transform. Once the locations of the targets are known, their colors are acquired. Tracking of the targets through video frames is then...
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