International Journal of Pattern Recognition and Artificial Intelligence

Face detection is integral to any automatic face recognition system. The goal of this research is to develop a system that performs the task of human face detection automatically in a scene. A system to correctly locate and identify human faces will find several applications, some examples are criminal identification and authentication in secure systems. This work presents a new approach based on principal component analysis. Face silhouettes instead of intensity images are used for this research. It results in reduction in both space and processing time. A set of basis face silhouettes are obtained using principal component analysis. These are then used with a Hough-like technique to detect faces. The results show that the approach is robust, accurate and reasonably fast.

A surface feature hypergraph (SFAHG) representation is proposed for the recognition and localization of three-dimensional objects. The hypergraph representation is shown to be viewpoint independent thus resulting in substantial savings in terms of memory for the object model database. The resulting hypergraph matching algorithm integrates both, relational and the rigid pose constraint in a consistent unified manner. The matching algorithm is also shown to have a polynomial order of complexity even in multiple-object scenes with instances of objects partially occluding each other. An algorithm for incrementally constructing the hypergraph representation of an object model from range images of the object taken from different viewpoints is also presented. The hypergraph matching and the hypergraph construction algorithms are shown to be capable of correcting errors in the initial segmentation of the range image. The hypergraph construction algorithm and the matching algorithm are tested on range images of scenes containing multiple three-dimensional objects with partial occlusion.

This paper describes a new automatic spelling correction program to deal with OCR generated errors. The method used here is based on three principles:

1. Approximate string matching between the misspellings and the terms occuring in the database as opposed to the entire dictionary

2. Local information obtained from the individual documents

3. The use of a confusion matrix, which contains information inherently specific to the nature of errors caused by the particular OCR device

This system is then utilized to process approximately 10,000 pages of OCR generated documents. Among the misspellings discovered by this algorithm, about 87% were corrected.

A multi-level multifont character recognition is presented. The system proceeds by first delimiting the context of the characters. As a way of enhancing system performance, typographical information is extracted and used for font identification before actual character recognition is performed. This has the advantage of sure character identification as well as text reproduction in its original form. The font identification is based on decision trees where the characters are automatically arranged differently in confusion classes according to the physical characteristics of fonts.

The character recognizers are built around the first and second order hidden Markov models (HMM) as well as Euclidean distance measures. The HMMs use the Viterbi and the Extended Viterbi algorithms to which enhancements were made. Also present is a majority-vote system that polls the other systems for “advice” before deciding on the identity of a character. Among other things, this last system is shown to give better results than each of the other systems applied individually.

The system finally uses combinations of stochastic and dictionary verification methods for word recognition and error-correction.

A new approach to object recognition is presented, in which secondary representations of 3-D models are synthesized/derived (in various forms) and subsequently compared in order to invoke views of models, tune model pose and verify recognition hypotheses. The use of these secondary representations allows complex models (e.g. surface-based or volumetric models) to be compared implicitly (rather than explicitly comparing the component primitives of the models). This in turn overcomes the problem of the stability of the model primitives, and provides independence between the complex 3-D representations and the recognition strategy (i.e. the invocation, matching and verification techniques). The secondary representations employed are Extended Gaussian Images, directional histograms, needle diagrams, depth maps and boundary curvature signatures.

The technique is demonstrated using models, of reasonably complex objects, derived from actively sensed range data.

Evolutions of closed planar polygons are studied in this work. In the first part of the paper, the general theory of linear polygon evolutions is presented, and two specific problems are analyzed. The first one is a polygonal analog of a novel affine-invariant differential curve evolution, for which the convergence of planar curves to ellipses was proved. In the polygon case, convergence to polygonal approximation of ellipses, polygo nal ellipses, is proven. The second one is related to cyclic pursuit problems, and convergence, either to polygonal ellipses or to polygonal circles, is proven. In the second part, two possible polygonal analogues of the well-known Euclidean curve shortening flow are presented. The models follow from geometric considerations. Experimental results show that an arbitrary initial polygon converges to either regular or irregular polygonal approximations of circles when evolving according to the proposed Euclidean flows.

A formalism for lexical projection in a Lexical Functional Grammar based syntactic processing environment, where lexical items may consist of more than one word, has been discussed. It is an extension of an earlier formalism that assumed single-worded lexical entities. It has been shown that traditional approaches of handling multi-worded lexical entities in an LFG environment are not quite suitable for Bangla, the language under study, because these approaches assume configurationality whereas Bangla is non-configurational. A “Supra-Lexical” level of analysis has been proposed and a formalism for such analyses introduced. The proposed formalism consists of two phases—an off-line specification phase and an implementation phase. Some tools that are required, along with the syntax for supra-lexical specification has been introduced with examples. Compilation of the specifications has been discussed.

The aim of this paper is to elaborate the power of cooperation in generating pictures by array grammars. As it is expected, the generative capacity of cooperating array grammar systems (with a fixed number, with a number greater than a given threshold, or with the maximal number of derivation steps in each component when it is enabled) is strictly greater than that of context-free array grammars. Yet the same result is also obtained in the case of systems with regular components, which contradicts the corresponding result for string grammar systems. In fact, some more results for array grammar systems are obtained which either contradict the results for the corresponding string grammar systems or are not even known for these string grammar systems. Various non-context-free sets of arrays which can be generated in a simple way by cooperating array grammar systems are presented and show the power of the mechanism of cooperation for picture descritpion.