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In the research on computer vision, object tracking has encountered various challenges, such as occlusion and scale variation. In recent years, tracking-by-detection methods have performed competitively. Some of these methods have focused on solving the problem of scale variation. Regardless, these algorithms perform poorly in real time. Recently, correlation filters have been widely used in object...
in this paper, we address the problem of tracking drift and failure due to background clustering, illumination and scale changes. To resolve these problems, we propose an efficient model that project original RGB color space to a more robust color space—Color Names feature space. Furthermore, we represent objects by background weighted histograms, and thus suppress the similar background around. Moreover,...
One of the main problems relating RANSAC estimation is to determine the inlier threshold adaptively depending on the variance of inliers. In this paper, we propose a novel method that estimates the inlier threshold adaptively from the observations, giving a threshold-free RANSAC. A minimum assumption of our method is that the lower bound of inlier ratio is known in advance and the variance of inliers...
In this study, we propose to simulate the dynamics of surface flow using a Topological Flow-path Network model (TFN) based on scale-adaptive DEMs (S-DEMs). This model makes full use of the high accuracy DEM data to extract topographical and hydrological characteristics from the terrain surface. It also assigns these characteristics with scale properties, thus building a stream-network-bounded scale-adaptive...
The SIFT algorithm had a profound impact on the area of image local feature extraction and computer vision, since its inception. Because of its robustness and anti-interference performance, it has been widely applied in engineering. However, in practical usage of SIFT, there are actually no sufficient feature points that can be extracted to perform the image registration. For tiny scale targets, this...
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