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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

Access type: Metadata onlyAuthor: search.filters.author.Lai, Yu-Chun

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Now showing 1 - 4 of 4
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    Video scene extraction using mosaic technique
    (2006)
    Chen, Liang-Hua
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    Lai, Yu-Chun
    ;
    Liao, Hong-Yuan
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    資訊工程學系
    ;
    Department of Computer Science
    Scene extraction is the first step toward semantic understanding of the video. This paper presents an effective approach to video scene extraction based on the analysis of background images. Our approach exploits the fact that shots belonging to one particular scene often have similar background. Although part of the video frame is covered by foreground objects, background scene still can be reconstructed by mosaic technique. The proposed scene extraction algorithm consists of two main components: the determination of shot similarity measure and shot grouping process. In our approach, several low-level visual features are integrated to compute the similarity measure between two shots. On the other hand, high-level knowledge in cinematography is used to guide the shot grouping process. Experimental results show that our approach is promising and outperforms some existing technique.
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    Human motion recognition using clay representation of trajectories
    (2006)
    Lai, Yu-Chun
    ;
    Liao, Hong-Yuan Mark
    ;
    資訊工程學系
    ;
    Department of Computer Science
    In this paper, we propose a novel human motion recognition approach that incorporates two fundamental concepts. First, the Shape Context and a clustering method are used to extract moving articulated parts from a video sequence. Then, we represent the moving articulated parts by trajectories. Our trajectory extraction approach provides good tolerance under various background and lighting conditions. Significantly, landmark point selection is not necessary in our approach, since trajectory generation is based on the extraction of moving articulated parts. The second concept is that the extracted trajectories are seen as forces pushing the articulated parts. Those forces are applied to a claylike deformed material, which is used to represent the trajectories' behavior. The experiment results show that the proposed approach is robust against noise and incorrectly extracted trajectories, such as redundant and missing trajectories.
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    A Local Feature-based Human Motion Recognition Framework
    (2009)
    Lai, Yu-Chun
    ;
    Liao, Hong-Yuan Mark
    ;
    Lin, Cheng-Chung
    ;
    Chen, Jian-Ren
    ;
    Luo, Y. -F. Peter
    In this paper, we propose a local feature-based human motion analysis framework Instead of using traditional analysis methods to characterize the global structure of human motion, we extract features directly from local regions that contain motion. To implement the above concept, we adopt the rules of visual attention theory, which assert that a human motion can be described simply by a set of local features comprised of spatial relationships rather than human postures. We select two kinds of features to represent the local variation of a human motion. First, we extract the long-term movement trend of the motion. The second feature is actually a set of rough features derived by sampling multi-scale moving edges. The two types of features are considered together during the recognition process. Our experiments demonstrate that the proposed approach can achieve very good recognition results.
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    Segmentation of human body parts in video frames based on intrinsic distance
    (2007)
    Lai, Yu-Chun
    ;
    Liao, Hong-Yuan Mark
    ;
    Lin, Cheng-Chung
    ;
    資訊工程學系
    ;
    Department of Computer Science
    We propose an intrinsic-distance based segmentation approach for segmenting human body parts in video frames. First, since the human body can be seen as a set of articulated parts, we utilize the moving articulated attributes to identify body part candidate regions automatically. The candidate regions and the background candidate regions are generated by voting and assigned to the spatiotemporal volume, which is comprised of frames of the video. Then, the intrinsic distance is used to estimate the boundaries of each body part. Our intrinsic distance-based segmentation technique is applied in the spatiotemporal volume to extract the optimal boundaries of the intrinsic distance in a video and obtain segmented frames from the segmented volume. The segmented results show that the proposed approach can tolerate incomplete and imprecise candidate regions because it provides temporal continuity. Furthermore, it can reduce over growing in the original intrinsic distance-based algorithm, since it can handle ambiguous pixels. We expect that this research can provide an alternative to segmenting a sequence of body parts in a video.