Attributed Relational Graph Research Articles

Pattern recognition by graph matching using the Potts MFT neural networks

This paper is concerned with programming of the Potts mean field theory neural networks for pattern recognition by homomorphic mapping of the attributed relational graphs (ARG). In order to generate the homomorphic mapping from the scene relational graph to the model graph, we make use of the recently introduced [Suganthan, Technical Report, Nanyang Technical University (1994)] compatibility functions in relation to the Hopfield network. An efficient pose clustering algorithm is used to separate and localize different occurrences of any particular object model in the scene. The pose clustering algorithm also eliminates spurious hypotheses generated by the network and resolves ambiguities in the final interpretation. The performance of the proposed approach to pattern recognition by homomorphism is demonstrated using a number of line patterns, silhouette images and circle patterns.

Self-organizing Hopfield network for attributed relational graph matching

Although the analogue Hopfield model has been shown to be a plausible approach for solving combinatorial optimization problems such as the travelling salesman problem (TSP), it has not been effective in solving the object recognition problem by attributed relational graph matching, for many reasons. However, we 1 recently enhanced the performance of the Hopfield network in attributed relational graph (ARG) matching by employing suitable energy and compatibility functions, a biased network initialization scheme and a hypothesis interpretation scheme using an efficient pose clustering algorithm. However, to generate the desired mapping, there is a need to fine tune many parameters that are highly dependent upon the model and scene under consideration. In this paper, a self-organizing Hopfield network is introduced that learns most of the network parameters and eliminates the need for specifying them a priori. To adaptively estimate the energy function parameter, a Liapunov indirect method based learning approach is employed. Other variables, such as the temperature parameter and the convergence criterion, are heuristically determined. The proposed self-organizing network is applied to solve problems such as line patterns, silhouette images and circle pattern recognition. Its superior performance over the fixed weight model is also demonstrated.

Pattern recognition by homomorphic graph matching using Hopfield neural networks

The application of the Hopfield neural network as a constraint satisfaction network for pattern recognition is investigated in this paper. Suitable energy and compatibility functions are introduced for pattern recognition by homomorphic attributed relational graph (ARG) matching. Although many computer vision problems have been traditionally formulated as combinatorial optimization problems, most of them can be reduced to that of finding the nearest local minimum of an objective function. In this paper, a novel network initialization strategy is applied to achieve the desired complexity reduction. Further, a method to verify and localize the hypotheses generated by the Hopfield network is also presented using an efficient pose clustering algorithm. The performance of the connectionist approach to pattern recognition by homomorphic relational graph matching is demonstrated using a number of line patterns, silhouette images and circle patterns.

Automatic registration of aerial photographs and digitized maps

We have developed a method of matching and recognizing aerial road network images based on road network models. The input is a list of line segments of an image obtained from a preprocessing stage, which is usually fragmentary and contains extraneous noisy segments. The output is the correspondences between the image line segments and model line segments. We use attributed relational graphs (ARG) to describe images and models. An ARG consists of a set of nodes, each node representing a line segment, and attributed relations between nodes. The task of matching is to find the best correspondences between the image ARG and the model ARG. The correspondences are found using a relaxation labeling algorithm, which optimizes a criterion of similarity. The algorithm is capable of subgraph matching of an image road structure to a map road model covering an area 10 times larger than the area imaged by the sensor, provided that the image distortion due to perspective imaging geometry has been corrected during preprocessing stages. We present matching experiments and demonstrate the stability of the matching method to extraneous line segments, missing line segments, and errors in scaling.

Object recognition from range data prior to segmentation

An unconventional approach to range image understanding is proposed, unconventional in the sense that object matching and recognition is performed before the image is segmented. The motivation for this is our own perception of problems, which we analyse in the paper, arising from the conventional paradigm of recognition after segmentation. The proposal consists of two levels: the representation and the computational levels. We illustrate how to represent scenes and models using attributed relational graphs (ARG). The ARG representation describes surface points, rather than segmented primitives, as its nodes, their invariant properties and invariant relations between them. Object matching and recognition, as well as image segmentation, is determined by the optimal point-wise mapping from the image ARG and the model ARG. We formulate the problem of finding such mapping as one of constrained optimization. The solution is computed using the relaxation labelling method, and some experiments of such optimal mapping are presented.

A graph distance measure for image analysis

Attributed relational graphs (ARGs) have shown superior qualities when used for image representation and analysis in computer vision systems. A new, efficient approach for calculating a global distance measure between attributed relational graphs is proposed, and its applications in computer vision are discussed. The distance measure is calculated by a global optimization algorithm that is shown to be very efficient for this problem. The approach shows good results for practical size ARGs. The technique is also suitable for parallel processing implementation.