A flexible similarity measure for 3D shapes recognition

Abstract

This paper is devoted to presenting a new strategy for 3D objects recognition using a flexible similarity measure based on the recent Modeling Wave (MW) topology in spherical models. MW topology allows us to establish an n-connectivity relationship in 3D objects modeling meshes. Using the complete object model, a study on considering different partial information of the model has been carried out to recognize an object. For this, we have introduced a new feature called Cone-Curvature (CC), which originates from the MW concept. CC gives an extended geometrical surroundings knowledge for every node of the mesh model and allows us to define a robust and adaptable similarity measure between objects for a specific model database. The defined similarity metric has been successfully tested in our lab using range data of a wide variety of 3D shapes. Finally, we show the applicability of our method presenting experimentation for recognition on noise and occlusion conditions in complex scenes.

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

“…This is also our case since our basis will be different measurements on the meshes or on the range data of the objects, calculated from different points of view. In this particular direction is addressed the problem in (Adan & Adan, 2004) where a shape similarity measure is introduced and applied. Nevertheless, this solution does not solve satisfactorily the object recognition problem from real partial views, which is the main purpose of our method.…”

“…Nevertheless, some preliminary concepts must be introduced to present and explain the power of this feature. First of all it must be said that the WCC feature is derived from the previous concept of Cone-Curvature (CC) defined in (Adan & Adan, 2004). But CC concept was originally introduced to represent complete models of objects and it is not directly applicable to partial modeling.…”

Piecewise-linear approximation of non-linear models based on probabilistically/possibilistically interpreted intervals’ numbers (INs)

“…1 A map w : ðP; 6Þ ! ðQ ; 6Þ is called (order) isomorphism if and only if both ''x 6 y () wðxÞ 6 wðyÞ" and ''w is onto Q".…”

Using Non Local Features for 3D Shape Grouping

“…Note that M CC is invariant, unless row permutations, to changes in the pose of the object. A wider explanation of CC concept can be found in [15].…”