Representation and Recognition of Free-Form Surfaces
نویسندگان
چکیده
We introduce a new surface representation for recognizing curved objects. Our approach begins by representing an object by a discrete mesh of points built from range data or from a geometric model of the object The mesh is computed from the data by deforming a standard shaped mesh, for example, an ellipsoid, until it fits the surface of the object. We define local regularity constraints that the mesh must satisfy. We then define a canonical mapping between the mesh describing the object and a standard spherical mesh. A surface curvature index that is pose-invariant is stored at every node of the mesh. We use this object representation for recognition by comparing the spherical model of a reference object with the model extracted from a new observed scene. We show how the similarity between reference model and observed data can be evaluated and we show how the pose of the reference object in the observed scene can be easily computed using this representation. We present results on real range images which show that this approach to modelling and recognizing three-dimensional objects has three main advantages: First, it is applicable to complex curved surfaces that cannot be handled by conventional techniques. Second, it reduces the recognition problem to the computation of similarity between spherical dismbutions; in particular, the recognition algorithm does not require any combinatorial search. Finally, even though it is based on a spherical mapping, the approach can handle occlusions and partial views.
منابع مشابه
Finding Surface Correspondance for Object Recognition and Registration Using Pairwise Geometric Histograms
Pairwise geometric histograms have been demonstrated as an eeective descriptor of arbitrary 2-dimensional shape which enable robust and eecient object recognition in complex scenes. In this paper we describe how the approach can be extended to allow the representation and classiication of arbitrary 2 1 2-and 3-dimensional surface shape. This novel representation can be used in important vision ...
متن کاملCOSMOS - A Representation Scheme for 3D Free-Form Objects
We address the problem of representing and recognizing 3D free-form objects when 1) the object viewpoint is arbitrary, 2) the objects may vary in shape and complexity, and 3) no restrictive assumptions are made about the types of surfaces on the object. We assume that a range image of a scene is available, containing a view of a rigid 3D object without occlusion. We propose a new and general su...
متن کاملطراحی الگوریتم بازشناسی واجها با به کارگیری همبسته های آکوستیکی مشخصه های واجی
In the present paper, the phonological feature geometry of the Persian phonemes is analyzed in the form of articulate-free and articulate-bound features based on the articulator model of the nonlinear phonology. Then, the reference phonetic pattern of each feature that consists of one or a set of acoustic correlates, characterized by the quantitative or qualitative values in its phonological re...
متن کاملHandwritten Character Recognition using Modified Gradient Descent Technique of Neural Networks and Representation of Conjugate Descent for Training Patterns
The purpose of this study is to analyze the performance of Back propagation algorithm with changing training patterns and the second momentum term in feed forward neural networks. This analysis is conducted on 250 different words of three small letters from the English alphabet. These words are presented to two vertical segmentation programs which are designed in MATLAB and based on portions (1...
متن کاملRepresentation of Adsorption Data for the Case of Energetically Heterogeneous Solid Surfaces Using Artificial Neural Network
متن کامل
A New IRIS Segmentation Method Based on Sparse Representation
Iris recognition is one of the most reliable methods for identification. In general, itconsists of image acquisition, iris segmentation, feature extraction and matching. Among them, iris segmentation has an important role on the performance of any iris recognition system. Eyes nonlinear movement, occlusion, and specular reflection are main challenges for any iris segmentation method. In thi...
متن کامل