Optimal implementation of morphological operations on neighborhood-connected parallel computers

To efficiently perform morphological operations on neighborhood-processing-based parallel image computers, we need to decompose structuring elements larger than the neighborhood that can be directly handled into neighborhood subsets. In the special case that the structuring element is a convex polygon, there are known decomposition algorithms in the literature. In this paper, we give an algorithm for the optimal decomposition of arbitrarily shaped structuring elements, enabling an optimal implementation of morphological operations on neighborhood-connected parallel computers in the general case.     

Approximation of graph edit distance based on Hausdorff matching

Graph edit distance is a powerful and flexible method for error-tolerant graph matching. Yet it can only be calculated for small graphs in practice due to its exponential time complexity when considering unconstrained graphs. In this paper we propose a quadratic time approximation of graph edit distance based on Hausdorff matching. In a series of experiments we analyze the performance of the proposed Hausdorff edit distance in the context of graph classification and compare it with a cubic time algorithm based on the assignment problem. Investigated applications include nearest neighbor classification of graphs representing letter drawings, fingerprints, and molecular compounds as well as hidden Markov model classification of vector space embedded graphs representing handwriting. In many cases, a substantial speedup is achieved with only a minor loss in accuracy or, in one case, even with a gain in accuracy. Overall, the proposed Hausdorff edit distance shows a promising potential in terms of flexibility, efficiency, and accuracy.     

Hidden Markov model-based ensemble methods for offline handwritten text line recognition

This paper investigates various ensemble methods for offline handwritten text line recognition. To obtain ensembles of recognisers, we implement bagging, random feature subspace, and language model variation methods. For the combination, the word sequences returned by the individual ensemble members are first aligned. Then a confidence-based voting strategy determines the final word sequence. A number of confidence measures based on normalised likelihoods and alternative candidates are evaluated. Experiments show that the proposed ensemble methods can improve the recognition accuracy over an optimised single reference recogniser.     

A generic framework for median graph computation based on a recursive embedding approach

The median graph has been shown to be a good choice to obtain a representative of a set of graphs. However, its computation is a complex problem. Recently, graph embedding into vector spaces has been proposed to obtain approximations of the median graph. The problem with such an approach is how to go from a point in the vector space back to a graph in the graph space. The main contribution of this paper is the generalization of this previous method, proposing a generic recursive procedure that permits to recover the graph corresponding to a point in the vector space, introducing only the amount of approximation inherent to the use of graph matching algorithms. In order to evaluate the proposed method, we compare it with the set median and with the other state-of-the-art embedding-based methods for the median graph computation. The experiments are carried out using four different databases (one semi-artificial and three containing real-world data). Results show that with the proposed approach we can obtain better medians, in terms of the sum of distances to the training graphs, than with the previous existing methods.     

Text extraction from colored book and journal covers

The automatic retrieval of indexing information from colored paper documents is a challenging problem. In order to build up bibliographic databases, editing by humans is usually necessary to provide information about title, authors and keywords. For automating the indexing process, the identification of text elements is essential. In this article an approach to automatic text extraction from colored book and journal covers is proposed. Two methods have been developed for extracting text hypotheses. The results of both methods are combined to robustly distinguish between text and non-text elements.     

Off-line, handwritten numeral recognition by perturbation method

This paper presents a new approach to off-line, handwritten numeral recognition. From the concept of perturbation due to writing habits and instruments, we propose a recognition method which is able to account for a variety of distortions due to eccentric handwriting. We tested our method on two worldwide standard databases of isolated numerals, namely CEDAR and NIST, and obtained 99.09 percent and 99.54 percent correct recognition rates at no-rejection level respectively. The latter result was obtained by testing on more than 170000 numerals     

Achieving more efficient control of boilers by taking technological constraints into account

The structure of a nonlinear system for control the superheated steam temperature taking technological constraints into account is considered. A solution is proposed that allows a larger stability margin of the automatic closed-loop control system to be obtained. The influence of additional tuning parameters on the quality of control is shown. The use of proposed solution makes it possible to make the control process more accurate and smooth, to take into account technological constraints, and achieve robustness of the control system with respect to coordinate and parametric disturbances.     

The IAM-database: an English sentence database for offline handwriting recognition

In this paper we describe a database that consists of handwritten English sentences. It is based on the Lancaster-Oslo/Bergen (LOB) corpus. This corpus is a collection of texts that comprise about one million word instances. The database includes 1,066 forms produced by approximately 400 different writers. A total of 82,227 word instances out of a vocabulary of 10,841 words occur in the collection. The database consists of full English sentences. It can serve as a basis for a variety of handwriting recognition tasks. However, it is expected that the database would be particularly useful for recognition tasks where linguistic knowledge beyond the lexicon level is used, because this knowledge can be automatically derived from the underlying corpus. The database also includes a few image-processing procedures for extracting the handwritten text from the forms and the segmentation of the text into lines and words. Received September 28, 2001 / Revised October 10, 2001