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A novel method is proposed to detect multi-part objects of unknown specific shape and appearance in natural images. It consists in first extracting a strictly over-segmented map of circular arcs and straight-line segments from an edge map. Each obtained constant-curvature contour primitive has an unknown origin which may be the external boundary of an interesting object, the textured or marked region enclosed by that boundary, or the external background region. The following processing steps identify, in a systematic yet efficient way, which groups of ordered contour primitives form a complete boundary of proper multi-part shape. Multiple detections are ranked with the top boundaries best satisfying a combination of global shape grouping criteria. Experimental results confirm the unique potential of the method to identify, in images of variable complexity, actual boundaries of multi-part objects as diverse as an airplane, a stool, a bicycle, a fish, and a toy truck.  相似文献
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图聚集技术旨在获取能够涵盖原图大部分信息的简洁超图,用于提炼概要信息、解决存储消耗和社交隐私保护等问题.对当前的图聚集技术进行研究,综述了现有图聚集技术中的分组方法并对其进行分类,将分组标准划分为基于属性一致性、基于邻接分组一致性、基于关联强度一致性、基于邻接顶点一致性和基于零重建误差这5类;在高层次上将各分组标准概括为基于属性、基于结构和同时基于属性和结构的图聚集.较为全面地总结和分析了当前图聚集技术的研究现状和进展,并探讨了未来研究的方向.  相似文献
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