Catálogo de publicaciones - libros

In this paper a new adaptive correlation filter based on synthetic discriminant functions (SDF) for reliable pattern recognition is proposed. The information about an object to be recognized and false objects as well as background to be rejected is used in an iterative procedure to design the adaptive correlation filter with a given discrimination capability. Computer simulation results obtained with the proposed filter in test scenes are compared with those of various correlation filters in terms of discrimination capability.

Globally exponential stability of non-autonomous delayed neural networks is considered in this paper. By utilizing delay differential inequalities, a new sufficient condition ensuring globally exponential stability for non-autonomous delayed neural networks is presented. The condition does not require that the delay function be differentiable or the coefficients be bounded. Due to this reason, the condition improves and extends those given in the previous literature.

This paper is concerned with time series of graphs and compares two novel schemes that are able to predict the presence or absence of nodes in a graph. Our work is motivated by applications in computer network monitoring. However, the proposed prediction methods are generic and can be used in other applications as well. Experimental results with graphs derived from real computer networks indicate that a correct prediction rate of up to 97% can be achieved.

Motivated by claims to ‘bridge the representational gap between image and model features’ and by the growing importance of topological properties we discuss several extensions to dual graph pyramids: structural simplification should preserve important topological properties and content abstraction could be guided by an external knowledge base. We review multilevel graph hierarchies under the special aspect of their potential for abstraction and grouping.

The recent advances in sketch-based applications and digital-pen protocols make visual languages useful tools for Human Computer Interaction. Graphical symbols are the core elements of a sketch and, hence a visual language. Thus, symbol recognition approaches are the basis for visual language parsing. In this paper we propose an adjacency grammar to represent graphical symbols in a sketchy framework. Adjacency grammars represent the visual syntax in terms of adjacency relations between primitives. Graphical symbols may be either diagram components or gestures. An on-line parsing method is also proposed. The performance of the recognition is evaluated using a benchmarking database of 5000 on-line symbols. Finally, an application framework for sketching architectural floor plans is described.

In this paper, we investigate the use of invariants derived from the heat kernel as a means of clustering graphs. We turn to the heat-content, i.e. the sum of the elements of the heat kernel. The heat content can be expanded as a polynomial in time, and the co-efficients of the polynomial are known to be permutation invariants. We demonstrate how the polynomial co-efficients can be computed from the Laplacian eigensystem. Graph-clustering is performed by applying principal components analysis to vectors constructed from the polynomial co-efficients. We experiment with the resulting algorithm on the COIL database, where it is demonstrated to outperform the use of Laplacian eigenvalues.

A branch-and-bound algorithm for matching Attributed Graphs (AGs) with Second-Order Random Graphs (SORGs) is presented. We show that the search space explored by this algorithm is drastically reduced by using the information of the 2^nd-order joint probabilities of vertices of the SORGs. A SORG is a model graph, described elsewhere, that contains 1^st and 2^nd-order order probabilities of attribute relations between elements for representing a set of AGs compactly. In this work, we have applied SORGs and the reported algorithm to the recognition of real-life objects on images and the results show that the use of 2^nd-order relations between vertices is not only useful to decrease the run time but also to increase the correct classification ratio.

In pattern recognition, median computation is an important technique for capturing the important information of a given set of patterns but it has the main drawback of its exponential complexity. Moreover, the Spectral Graph techniques can be used for the fast computation of the approximate graph matching error, with a considerably reduced execution complexity. In this paper, we merge both methods to define the Median Spectral Graphs. With the use of the Spectral Graph theories, we find good approximations of median graph. Experiments on randomly generated graphs demonstrate that this method works well and it is robust against noise.

The interpretation of natural scenes, generally so obvious and effortless for humans, still remains a challenge in computer vision. We propose in this article to design binary classifiers capable to recognise some generic image categories. Images are represented by graphs of regions and we define a graph edit distance to measure the dissimilarity between them. Furthermore a feature selection step is used to pick in the image the most meaningful regions for a given category and thus have a compact and appropriate graph representation.

The aim in this paper is to show how the problem of learning the class-structure and modes of structural variation in sets of graphs can be solved by converting the graphs to strings. We commence by showing how the problem of converting graphs to strings, or seriation, can be solved using semi-definite programming (SDP). This is a convex optimisation procedure that has recently found widespread use in computer vision for problems including image segmentation and relaxation labelling. We detail the representation needed to cast the graph-seriation problem in a matrix setting so that it can be solved using SDP. We show how the strings delivered by our method can be used for graph-clustering and the construction of graph eigenspaces.