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Spatial matching for object retrieval is often time-consuming and susceptible to viewpoint changes. To address this problem, we propose a novel spatial matching method and implement it on modern GPU in parallel. Unlike previous spatial matching methods, in which the affine transformation estimation is based on the gravity vector assumption, our method abandons this strong assumption by matching the...
Spatial matching for visual words based object retrieval often involves generating affine transformation hypotheses and then choosing the best hypothesis to measure the spatial consistency. In existing methods, generating an affine transformation hypothesis either requires three correspondences or assumes images are taken in restricted range of viewpoints in using a single correspondence. In this...
This paper proposes a novel content-based copy retrieval scheme for video copy identification. Its goal is to detect matches between a doubtful video and the ones stored in the database of the legal holders of the videos. Due to various transformations the copy may has, we use visual words vector as a representation of a frame which is based on SIFT descriptor. Unlike traditional bag-of-words (BoW)...
Logo detection is important for brand advertising and surveillance applications. The central issues of this technology are fast localization and accurate matching. Based on key traits analysis of common logos, this paper presents a two-stage detection scheme based on spatialspectral saliency (SSS) and partial spatial context (PSC). SSS speeds up logo location and avoid the impact of cluttered background...
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