Large Object Databases Research Articles

Recognition of partially occluded objects using neural network based indexing

In this paper, a new neural network based indexing scheme has been proposed for recognition of planar shapes. Local contour segment-based-invariants have been used for indexing. Object contours have been obtained using a new algorithm which combines advantages of region growing and edge detection. Neighbourhood constraints have been applied on the results of indexing for combining hypotheses generated through the indexing scheme. Composite hypotheses have been verified using a distance transform based algorithm. Experimental results, on real images of varying complexity of a reasonably large database of objects have established the robustness of the method.

Polyhedral object recognition by indexing

In computer vision, the indexing problem is the problem of recognizing a few objects in a large database of objects while avoiding the help of the classical image-feature-to-object-feature matching paradigm. In this paper we address the problem of recognizing three-dimensional (3-D) polyhedral objects from 2-D images by indexing. Both the objects to be recognized and the images are represented by weighted graphs. The indexing problem is therefore the problem of determining whether a graph extracted from the image is present or absent in a database of model graphs. We introduce a novel method for performing this graph indexing process which is based both on polynomial characterization of binary and weighted graphs and on hashing. We describe in detail this polynomial characterization and then we show how it can be used in the context of polyhedral object recognition. Next we describe a practical recognition-by-indexing system that includes the organization of the database, the representation of polyhedral objects in terms of 2-D characteristic views, the representation of this views in terms of weighted graphs and the associated image processing. Finally, some experimental results allow the evaluation of the system performance.

Integration of Multiple Feature Groups and Multiple Views into a 3D Object Recognition System

This paper proposes two approaches for utilizing the information in multiple entity groups and multiple views to reduce the number of hypotheses passed to the verification stage in a model-based object recognition system employing invariant feature indexing (P. J. Flynn and A. K. Jain, CVGIP: Image Understand. 55(2), 1992, 119-129). The first approach is based on a majority voting scheme that keeps track of the number of consistent votes cast by prototype hypotheses for particular object models. The second approach examines the consistency of estimated object pose from multiple groups of entities (surfaces) in one or more views. A salient feature of our system and experiment design compared to most existing 3D object recognition systems is our use of a large object database and a large number of test images. Monte Carlo experiments employing 585 single-view synthetic range images and 117 pairs of synthetic range images with a large CAD-based 3D object database (P. J. Flynn and A. K. Jain, IEEE Trans. Pattern Anal. Mach. Intell. 13(2), 1991, 114-132) show that a large number of hypotheses (about 60% for single views and 90% for multiple views on average) can be eliminated through use of these approaches. The techniques have also been tested on several real 3D objects sensed by a Technical Arts 100X range scanner to demonstrate a substantial improvement in recognition time.

Hybrid optical digital computation of global chord functions

Chord functions have attractive properties for automatic shape recognition as, to a significant extent, they are invariant to object position, rotation and scaling. They are very time consuming to compute digitally and consequently have not found much use in operational pattern recognition systems. This paper describes how to calculate them rapidly in a hybrid optical-digital computer. This will allow realistic assessment to be made by their usefulness in a practical system as chord functions for a large database of objects can now be determined.