3D Object Localization Research Articles

3D object localization using EIT measurements at two levels

Electrical impedance tomography (EIT) is designed essentially for two-dimensional imaging, but current flow in the third dimension causes images to be formed for objects in 3D. The present work has shown that the image of an object is shifted in position towards the centre almost linearly with the 3D distance from the electrode plane and that the slope of this linear variation depends on the radial distance of the object. An empirical curve has been fitted to this dependence, based on which a method has been developed to locate 3D point objects from EIT measurements in only two planes. This will be useful in clinical and other applications in which 3D objects are few and widely separated. This new methodology may be the basis for 3D imaging in the future. Keywords: electrical impedance tomography, 3D imaging, 3D object localization

Interactive Learning of a Multiple-Attribute Hash Table Classifier for Fast Object Recognition

Multiple-attribute hashing is now considered to be a powerful approach for the recognition and localization of 3D objects on the basis of their invariant properties. In the systems developed to date, the structure of the hash table is fixed and must be created by the system developer—an onerous task especially when the number of attributes is large, as it must in systems that use both geometric and nongeometric attributes. Another deficiency of previous systems is that uncertainty is treated as a fixed value and not modeled. In this paper, we will present a system, named MULTI-HASH, which uses the tools of decision trees and uncertainty modeling for the automatic construction of hash tables. The decision-tree framework in MULTI-HASH is based on a hybrid method that uses both qualitative attributes, such as the shape of a surface, and quantitative attributes such as color, dihedral angles, etc. The human trainer shows objects to the vision system and, in an interactive mode, tells the system the model identities of the various segmented regions, etc. Subsequently, the decision-tree-based framework learns the structure of the hash table.