Electronics Laboratory, University of Clermont II, Les Cezeaux, BP 45, 63170 Aubiere, France.
IEEE Trans Pattern Anal Mach Intell. 1987 Mar;9(3):429-38. doi: 10.1109/tpami.1987.4767924.
A new method is presented for the recognition of polyhedra in range data. The method is based on a hypothesis accumulation scheme which allows parallel implementations. The different objects to be recognized are modeled by a set of local geometrical patterns. Local patterns of the same nature are extracted from the scene. For the recognition of an object, local scene and model patterns having the same geometrical characteristics are matched. For each of the possible matches, the geometric transformations (i.e., rotations and translations) are computed, which allows the overlapping of the model elements with those from the scene. This transformation permits the establishment of a hypothesis on the location of the object in the scene and the determination of a point in the transformation space. The presence of an object similar to a model involves the generation of several compatible hypotheses and creates a compact cluster in the transformation space. The recognition of the object is based on the detection of this cluster. The cluster coordinates give the values of the rotations and the translations to be applied to the model such that it corresponds to the object in the scene. The exact location of this object is given by the transformed model.
提出了一种用于识别距离数据中多面体的新方法。该方法基于假设积累方案，允许并行实现。要识别的不同对象由一组局部几何模式建模。从场景中提取具有相同性质的局部模式。对于对象的识别，将场景和模型中具有相同几何特征的局部模式进行匹配。对于每个可能的匹配，计算几何变换（即旋转和平移），这允许模型元素与场景中的元素重叠。该变换允许在场景中建立对象位置的假设，并确定变换空间中的一个点。存在类似于模型的物体涉及生成几个兼容的假设，并在变换空间中创建一个紧凑的簇。对象的识别基于检测这个簇。簇坐标给出了要应用于模型的旋转和平移的值，以使模型与场景中的对象相对应。该对象的确切位置由转换后的模型给出。
