International Journal of Pattern Recognition and Artificial Intelligence

Shape recognition is an important research area in pattern recognition. It also has wide practical applications in many fields. An attribute grammar approach to shape recognition combines both the advantages of syntactic and statistical methods and makes shape recognition more accurate and efficient. However, the time complexity of a sequential shape recognition algorithm using attribute grammar is O(n³) where n is the length of an input string. When the problem size is very large it needs much more computing time, therefore a high speed parallel shape recognition is necessary to meet the demands of some real-time applications. This paper presents a parallel shape recognition algorithm and also discusses the algorithm partition problem as well as its implementation on a fixed-size VLSI architecture. The proposed algorithm has time complexity O(n³/k²) if using k×k processing elements. When k=n, its time complexity is O(n). The experiment has been conducted to verify the performance of the proposed algorithm. The correctness of the algorithm partition and the behavior of the proposed VLSI architecture have also been proved through the experiment. The results indicate that the proposed algorithm and the VLSI architecture could be very useful to imaging processing, pattern recognition and related areas, especially for real-time applications.

The task of object identification is fundamental to the operations of an autonomous vehicle. It can be accomplished by using techniques based on a Multisensor Fusion framework, which allows the integration of data coming from different sensors. In this paper, an approach to the synergic interpretation of data provided by thermal and visual sensors is proposed. Such integration is justified by the necessity for solving the ambiguities that may arise from separate data interpretations.

The architecture of a distributed Knowledge-Based system is described. It performs an Intelligent Data Fusion process by integrating, in an opportunistic way, data acquired with a thermal and a video (b/w) camera. Data integration is performed at various architecture levels in order to increase the robustness of the whole recognition process. A priori models allow the system to obtain interesting data from both sensors; to transform such data into intermediate symbolic objects; and, finally, to recognize environmental situations on which to perform further processing. Some results are reported for different environmental conditions (i.e. a road scene by day and by night, with and without the presence of obstacles).

We have developed a generalized alphanumeric character extraction algorithm that can efficiently and accurately locate and extract characters from complex scene images. A scene image may be complex due to the following reasons: (1) the characters are embedded in an image with other objects, such as structural bars, company logos and smears; (2) the characters may be painted or printed in any color including white, and the background color may differ only slightly from that of the characters; (3) the font, size and format of the characters may be different; and (4) the lighting may be uneven.

The main contribution of this research is that it permits the quick and accurate extraction of characters in a complex scene. A coarse search technique is used to locate potential characters, and then a fine grouping technique is used to extract characters accurately. Several additional techniques in the postprocessing phase eliminate spurious as well as overlapping characters. Experimental results of segmenting characters written on cargo container surfaces show that the system is feasible under real-life constraints. The program has been installed as part of a vision system which verifies container codes on vehicles passing through the Port of Singapore.

This paper deals with the representation and acquisition of knowledge extracted during a real-time path planning process in an unknown space. In particular, the extracted knowledge from each current free navigation space is represented by attributed graphs. The graph knowledge representation forms are “processed and combined” appropriately by generating a scheme which acquires the extracted knowledge during the navigation. Illustrated examples are provided for simple navigation cases.

This paper proposes a new structural technique for Arabic text recognition. The technique can be divided into five major steps: (1) preprocessing and binarization; (2) thinning; (3) binary tree construction; (4) segmentation; and (5) recognition. The advantage of this technique is that its execution does not depend on either the font or size of character. Thus, this same technique might be utilized for the recognition of machine or hand printed text.

The relevant algorithm is implemented on a microcomputer. Experiments were conducted to verify the accuracy and the speed of this algorithm using about 20,000 subwords each with an average length of 3 characters. The subwords used were written using different fonts. The recognition rate obtained in the experiments indicated an accuracy of 93.38 % with a speed of 2.7 characters per second.

This work describes a new adaptive rank-order filter, which can be useful for image enhancement or as an early processing stage for segmentation purposes. The filter output is selected among the rank-ordered grey values of the observation window, and the output’s rank depends adaptively on a local measure of the spatial order, to be defined suitably. The aim of this adaptation is to allow the filter to meet conflicting requirements, namely noise and texture smoothing, edge sharpening and enhancement of the fine-structured detail. The definition of a measure of the spatial order is discussed, and experimental results, obtained with natural images of different types, are displayed.

This paper describes a new lexicon organization intended to provide high speed string correction. This method links two different approaches: code based partition of the lexicon using a hierarchical file for the first part,²¹ and generation of neighboring strings or code for the second.^3,18 The lexicon is preprocessed using a hierarchy of codes. This preprocessing can be done incrementally as each new word is entered. The resulting size of the lexicon remains proportional to the number of words entered with a rather high proportionality factor. Given a new string, it is then possible to find a set of candidate corrections in constant time. This set contains at least all the desired neighbors (in a sense defined by the user) of the given string. This method is therefore well-suited for a small to medium sized lexicon in applications where the correction speed is crucial (the method has been successfully tested with a French lexicon of about 10,000 words collected from scientific texts).

In the previous paper on a tree-to-tree editing problem some errors were included. This letter describes the corrected definition of a structure preserving mapping between rooted and ordered trees and a computing method of the tree distance based on the mapping.