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Experiences in Recognizing Free-Shaped Objects from Partial Views by Using Weighted Cone Curvatures

The recognition problem tries to identify an object, called unknown or scene object, from a set of objects in an object database, generally called models. The problem of positioning or alignment solves the localization of an object in a scene with respect to a reference system linked to the model of this object. One of the most common ways of solving this problem is matching the unknown object on the corresponding object in the object database. Although conceptually recognition and positioning are two different problems, in practice, especially when only part of the object is available (i.e. partial view), they are closely related. If we can align the unknown object precisely on one of the different objects in the database, we will have solved not only positioning but also recognition. In a general sense, an object recognition system finds objects in the real world from an image of the world, using object models which are known a priori. There is a wide variety of approaches that propose different solutions to recognize 3D objects. Most of them use representation models based on geometrical features where the solution depends on the feature matching procedure. The method we present in this work is concerned with the case when only range data from a partial view of a given free-shaped object is available. Recognition must be carried out by matching this range data of the partial view with of all the possible views of every complete object in the stored object database. The resolution of the problem is of real practical interest since it can be used in tasks with industrial robots, mobile robot navigation, visual inspection, etc. Regarding the kind of models used in the different approaches there are two fundamental 3D representation categories: object-based representation and view-centred representation. The first tries to generate the model according to the appearance of the object from different points of view, while the second creates models based on representative characteristics of the objects. More detailed information on them will be included in next section. The structure of this chapter is as follows. Section 2 shows a short survey of recent and more important works that could be considered closer to our work. Section 3 is devoted to make a general description of the stages in which the proposed method is decomposed. The first stage and the WCC feature, which is the key of our method, are studied in detail in section