International Journal of Pattern Recognition and Artificial Intelligence

This paper proposes a robust method for tracking an object contour in a sequence of images. In this method, both object extraction and tracking problems can be solved simultaneously. Furthermore, it is applicable to the tracking of arbitrary shapes since it does not need a priori knowledge about the object shapes. In the contour tracking, energy-minimizing elastic contour models are utilized, which is newly presented in this paper. The contour tracking is formulated as an optimization problem to find the position that minimizes both the elastic energy of its model and the potential energy derived from the edge potential image that includes a target object contour. We also present an algorithm which efficiently solves energy minimization problems within a dynamic programming framework. The algorithm enables us to obtain optimal solution even when the variables to be optimized are not ordered. We show the validity and usefulness of the proposed method with some experimental results.

This paper presents a methodology for integrating connectionist and symbolic approaches to 2D image recognition. The proposed integration paradigm exploits the synergy of the two approaches for both the training and the recognition phases of an image recognition system. In the training phase, a symbolic module provides an approximate solution to a given image-recognition problem in terms of symbolic models. Such models are hierarchically organized into different abstraction levels, and include contextual descriptions. After mapping such models into a complex neural architecture, a neural training process is carried out to optimize the solution of the recognition problem. The so-obtained neural networks are used during the recognition phase for pattern classification. In this phase, the role of symbolic modules consists of managing complex aspects of information processing: abstraction levels, contextual information, and global recognition hypotheses. A hybrid system implementing the proposed integration paradigm is presented, and its advantages over single approaches are assessed. Results on Magnetic Resonance image recognition are reported, and comparisons with some well-known classifiers are made.

The objective of this paper is to propose a syntactic formalism for space representation, which besides the well known advantages of hierarchical data structure, has the additional strength of self-adapting to a spatial structure at hand. The formalism is called puzzletree because its generation results in a number of blocks which in a certain order — like a puzzle — reconstruct the original space. The approach may be applied to any higher-dimensioned space (e.g. images, volumes). The paper concentrates on the principles of puzzletrees by explaining the underlying heuristic for their generation with respect to 2D spaces, i.e. images.

In this paper, we proposed an approach for segmentation and recognition of unconstrained handwritten English words with character and ligature modeling. Viewing a handwritten word as an alternating sequence of characters and ligatures, a network of circularly interconnected hidden Markov models is constructed to model handwritten English words of indefinite length. Then the recognition problem is regarded as finding the maximal probability path in the network for given input sequence. From the path, optimal segmentation and associated character labels are obtained simultaneously without any explicit segmentation.

A questionnaire, designed to assess bleeding predispositions in tonsillectomy and/or adenoidectomy patients, was administered to 236 otherwise healthy children. For comparative purposes, 114 patients with bleeding disorders were also studied. An unsupervised non-metric clustering technique was used in an attempt to classify bleeders against non-bleeders based solely on the responses to the questionnaire. Non-metric techniques are essential for the classification process because of the large number of missing attribute values in the patient data set. As a benchmark, a supervised inductive machine learning strategy was also used to classify the patients.

Performance results are compared and contrasted between the techniques across different subsets of the patient data. These techniques are also evaluated as a methodology for determining the relative significance of attributes vis-à-vis the reduction of the dimensionality of a large medical data set. In this investigation, the classification rate achieved using the non-metric technique (73%) was only marginally poorer than the rate using the supervised technique (76%). Moreover, these results were obtained with an accompanying 80% reduction in the number of attributes used to perform the analysis.

Occlusion detection is an important problem in 3D computer vision which uses multiple views, such as stereo vision. The presence of occlusion complicates the problem of vergence and the subsequent stereo matching in the generation of 3D data. This paper presents an approach which detects the presence of occlusion concurrently during the vergence process. The main limitation of the approach where the maximum correlation coefficient can be very high even when a significant amount of occlusions is present in the stereo images is shown. This paper presents an adaptive method of adjusting the correlation threshold with respect to the contrast-levels of the image being analyzed to alleviate this limitation. The proposed adaptive threshold method ensures that the sensitivity of detecting mismatches is less dependent upon the contrast-levels of the image being analyzed. The computational advantage of the proposed adaptive threshold method over the fixed threshold method is also presented. Experimental results which show the strengths of the proposed adaptive threshold method over the fixed threshold method on real scenes are given.

Due to different writing styles and various kinds of noise, the recognition of handwritten numerals is an extremely complicated problem. Recently, a new trend has emerged to tackle this problem by the use of multiple classifiers. This method combines individual classification decisions to derive the final decisions. This is called "Combination of Multiple Classifiers" (CME). In this paper, a novel approach to CME is developed and discussed in detail. It contains two steps: data transformation and data classification. In data transformation, the output values of each classifier are first transformed into a form of likeness measurement. The larger a likeness measurement is, the more probable the corresponding class has the input. In data classification, neural networks have been found very suitable to aggregate the transformed output to produce the final classification decisions. Some strategies for further improving the performance of neural networks have also been proposed in this paper. Experiments with several data transformation functions and data classification approaches have been performed on a large number of handwritten samples. The best result among them is achieved by using both the proposed data transformation function and the multi-layer perceptron neural net, which increased the recognition rate of three individual classifications considerably.