PG2014short

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https://diglib.eg.org/handle/10.2312/7750

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Browsing PG2014short by Subject "General"

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    Album Quickview in Comic-like Layout via Quartet Analysis
    (The Eurographics Association, 2014) Zheng, Zhibin; Zhang, Yan; Miao, Zheng; Sun, Zhengxing; John Keyser and Young J. Kim and Peter Wonka
    For clear summary and efficient search of images for album, which carries a story of life record, we propose a new approach for quickview of album in comic-like layout via quartet analysis. How to organize the images in album and in what way to display images in collage are two key problems for album quickview. For the first problem, we take the idea of model organization method based on quartet analysis to construct categorization tree to organize the images; while for the second problem, we utilize the topological structure of categorization tree to decompose it into multiple groups of images and extract representative image from each group for subsequent collage. For the collage part, we choose comic-like layout to present collage because comic provides a concise way for storytelling and it has variablitiy in layout styles, which is suitable for album summary. Experiments demonstrate that our method could organize the images effectively and present images in collage with diverse styles as well.
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    Scene Segmentation and Understanding for Context-Free Point Clouds
    (The Eurographics Association, 2014) Spina, Sandro; Debattista, Kurt; Bugeja, Keith; Chalmers, Alan; John Keyser and Young J. Kim and Peter Wonka
    The continuous development of new commodity hardware intended to capture the surface structure of objects is quickly making point cloud data ubiquitous. Scene understanding methods address the problem of determining the objects present in a point cloud which, dependant on sensor capabilities and object occlusions, is normally noisy and incomplete. In this paper, we propose a novel technique which enables automatic identification of semantically meaningful structures within point clouds acquired using different sensors on a variety of scenes. The representation model, namely the structure graph, with nodes representing planar surface segments, is computed over these point clouds to help with the identification task. In order to accommodate for more complex objects (e.g. chair, couch, cabinet, table), a training process is used to determine and concisely describe, within each object's structure graph, its important shape characteristics. Results on a variety of point clouds show how our method can quickly discern certain object types.