Shape Representation and Recognition from Multiscale Curvature
Shape Representation and Recognition from Multiscale Curvature
复制标题
多尺度曲率的形状表示和识别
DOI：
10.1006/cviu.1997.0533
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发表时间：
1997
期刊：
影响因子：
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通讯作者：
John K. Tsotsos
中科院分区：
文献类型：
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作者：
G. Dudek;John K. Tsotsos
我们提出了一种基于多尺度曲率信息的形状表示和物体识别技术。它为三维空间中平面曲线和曲面的分解和识别提供了一个单一的框架。分解操作同时执行数据插值、数据平滑和分割。这三个阶段的统一导致了与描述中使用的基元相耦合的平滑操作。每个最小化算子除了具有曲率调谐之外，还具有不同的空间灵敏度函数。因此，不同的可能描述在多个空间尺度上捕获信息。这允许在适当的情况下以多种方式描述对象的单个区域。平面曲线的识别证明了随后的表示的实用性。采用基于动态规划的匹配策略。这些结果说明了可以定义类似物体的连续光谱的方式，范围从与目标非常相似的物体到与目标非常不同的物体。
We present a technique for shape representation and the recognition of objects based on multiscale curvature information. It provides a single framework for both the decomposition and recognition of both planar curves as well as surfaces in three-dimensional space. The decomposition operation simultaneously performs data interpolation, data smoothing, and segmentation. The unification of these three stages results in a smoothing operation that is coupled with the primitives to be used in description. Each of the minimization operators, in addition to having a curvature tuning, also has a different spatial sensitivity function. As a result, the different possible descriptions capture information at multiple spatial scales. This allows a single region of an object to be described in more than one way, when appropriate. The practicality of the ensuing representation is demonstrated by the recognition of planar curves. A matching strategy based on dynamic programming is used. The results illustrate the manner in which a continuous spectrum of similar objects can be defined, ranging from those that are very similar to a target to those that are very different from it.