3-D Object Recognition using MEGI Model from Range Data
نویسندگان
چکیده
Description and recognition of objects is the central considerations of research on computer vision. The key issue is how to represent 3-D objects on a machine for recognizing them. Researchers of computer vision commonly employ the EGI model, but it is not able to express concave objects. In this paper, the MEGI model and coe cient of extended spherical correlation have been proposed. The MEGI model is an extended EGI modeling which is able to represent concave objects. Extended spherical correlation is the measure for recognizing objects using the MEGI model. It has been demonstrated that this model is able to recognize 3-D objects, including concave ones, and to distinguish objects using a part of MEGI from range data.
منابع مشابه
3-D object recognition using adaptive scale MEGI
We propose a method for recognition of a 3-D object using multi scale description of the object and adaptive matching. MEGI model is a description model to represent arbitrary shapes. However, many MEGI elements are necessary to represent uneven or curved surfaces with accuracy, so it is di cult to use them for recognition. As a solution, we make a tree which corresponds multi scale description...
متن کاملHuman Face Identi cation by Multi Scale MEGI
We proposed MEGI model for description and recognition of concave objects. The set of MEGI data consists of position vector and normal vector. No surface shape is needed, and the connectivity of each neighboring surface is not required. Using these features, elements uni cation procedure \multi scale MEGI" is proposed in this paper. Furthermore, Human face identi cation is also performed by mul...
متن کاملUrban Vegetation Recognition Based on the Decision Level Fusion of Hyperspectral and Lidar Data
Introduction: Information about vegetation cover and their health has always been interesting to ecologists due to its importance in terms of habitat, energy production and other important characteristics of plants on the earth planet. Nowadays, developments in remote sensing technologies caused more remotely sensed data accessible to researchers. The combination of these data improves the obje...
متن کاملRecognizing Objects by Matching Oriented Points
By combining techniques from geometric hashing and structural indexing, we have developed a new representation for recognition of free-form objects from three dimensional data. The representation comprises descriptive spin-images associated with each oriented point on the surface of an object. Constructed using single point bases, spin-images are data level shape descriptions that are used for ...
متن کامل3-D Cueing: A Data Filter for Object Recognition
This paper presents a novel method for quickly filtering range data points to make object recognition in large 3D data sets feasible. The general approach, called " 3D cueing, " uses shape signatures from object models as the basis for a fast, probabilistic classification system which rates scene points in terms of their likelihood of belonging to a model. This algorithm, which could be used as...
متن کامل