Nikos Papamarkos

Hand gesture recognition using a neural network shape fitting technique

A new method for hand gesture recognition that is based on a hand gesture fitting procedure via a new Self-Growing and Self-Organized Neural Gas (SGONG) network is proposed. Initially, the region of the hand is detected by applying a color segmentation technique based on a skin color filtering procedure in the YCbCr color space. Then, the SGONG network is applied on the hand area so as to approach its shape. Based on the output grid of neurons produced by the neural network, palm morphologic characteristics are extracted. These characteristics, in accordance with powerful finger features, allow the identification of the raised fingers. Finally, the hand gesture recognition is accomplished through a likelihood-based classification technique. The proposed system has been extensively tested with success.

A new signature verification technique based on a two-stage neural network classifier

Abstract This paper presents a new technique for off-line signature recognition and verification. The proposed system is based on global, grid and texture features. For each one of these feature sets a special two stage Perceptron OCON (one-class-one-network) classification structure has been implemented. In the first stage, the classifier combines the decision results of the neural networks and the Euclidean distance obtained using the three feature sets. The results of the first-stage classifier feed a second-stage radial base function (RBF) neural network structure, which makes the final decision. The entire system was extensively tested and yielded high recognition and verification rates.

Adaptive color reduction

The paper proposes an algorithm for reducing the number of colors in an image. The proposed adaptive color reduction (ACR) technique achieves color reduction using a tree clustering procedure. In each node of the tree, a self-organized neural network classifier (NNC) is used which is fed by image color values and additional local spatial features. The NNC consists of a principal component analyzer (PCA) and a Kohonen self-organized feature map (SOFM) neural network (NN). The output neurons of the NNC define the color classes for each node. The final image not only has the dominant image colors, but its texture also approaches the image local characteristics used. Using the adaptive procedure and different local features for each level of the tree, the initial color classes can be split even more. For better classification, split and merging conditions are used in order to define whether color classes must be split or merged. To speed up the entire algorithm and reduce memory requirements, a fractal scanning subsampling technique is used. The method is independent of the color scheme, it is applicable to any type of color images, and it can be easily modified to accommodate any type of spatial features and any type of tree structure. Several experimental and comparative results, exhibiting the performance of the proposed technique, are presented.

A new approach for multilevel threshold selection

This paper describes a new method for multilevel threshold selection of gray level images. The proposed method includes three main stages. First, a hill-clustering technique is applied to the image histogram in order to approximately determine the peak locations of the histogram. Then, the histogram segments between the peaks are approximated by rational functions using a linear minimax approximation algorithm. Finally, the application of the one-dimensional Golden search minimization algorithm gives the global minimum of each rational function, which corresponds to a multilevel threshold value. Experimental results for histograms with two or more peaks are presented.

Skew detection and text line position determination in digitized documents

This paper proposes a computationally efficient procedure for skew detection and text line position determination in digitized documents, which is based on the cross-correlation between the pixels of vertical lines in a document. The determination of the skew angle in documents is essential in optical character recognition systems. Due to the text skew, each horizontal text line intersects a predefined set of vertical lines at non-horizontal positions. Using only the pixels on these vertical lines we construct a correlation matrix and evaluate the skew angle of the document with high accuracy. In addition, using the same matrix, we compute the positions of text lines in the document. The proposed method is tested on a variety of mixed-type documents and it provides good and accurate results while it requires only a short computational time. We illustrate the effectiveness of the algorithm by presenting four characteristic examples.

Multithresholding of color and gray-level images through a neural network technique

One of the most frequently used methods in image processing is thresholding. This can be a highly efficient means of aiding the interpretation of images. A new technique suitable for segmenting both gray-level and color images is presented in this paper. The proposed approach is a multithresholding technique implemented by a Principal Component Analyzer (PCA) and a Kohonen Self-Organized Feature Map (SOFM) neural network. To speedup the entire multithresholding algorithm and reduce the memory requirements, a sub-sampling technique can be used. Several experimental and comparative results exhibiting the performance of the proposed technique are presented. q 2000 Elsevier Science B.V. All rights reserved.

Segmentation of historical machine-printed documents using Adaptive Run Length Smoothing and skeleton segmentation paths

In this paper, we strive towards the development of efficient techniques in order to segment document pages resulting from the digitization of historical machine-printed sources. This kind of documents often suffer from low quality and local skew, several degradations due to the old printing matrix quality or ink diffusion, and exhibit complex and dense layout. To face these problems, we introduce the following innovative aspects: (i) use of a novel Adaptive Run Length Smoothing Algorithm (ARLSA) in order to face the problem of complex and dense document layout, (ii) detection of noisy areas and punctuation marks that are usual in historical machine-printed documents, (iii) detection of possible obstacles formed from background areas in order to separate neighboring text columns or text lines, and (iv) use of skeleton segmentation paths in order to isolate possible connected characters. Comparative experiments using several historical machine-printed documents prove the efficiency of the proposed technique.

Text extraction in complex color documents

Text extraction in mixed-type documents is a pre-processing and necessary stage for many document applications. In mixed-type color documents, text, drawings and graphics appear with millions of different colors. In many cases, text regions are overlaid onto drawings or graphics. In this paper, a new method to automatically detect and extract text in mixed-type color documents is presented. The proposed method is based on a combination of an adaptive color reduction (ACR) technique and a page layout analysis (PLA) approach. The ACR technique is used to obtain the optimal number of colors and to convert the document into the principal of them. Then, using the principal colors, the document image is split into the separable color plains. Thus, binary images are obtained, each one corresponding to a principal color. The PLA technique is applied independently to each of the color plains and identifies the text regions. A merging procedure is applied in the final stage to merge the text regions derived from the color plains and to produce the final document. Several experimental and comparative results, exhibiting the performance of the proposed technique, are also presented.

Color Reduction Using Local Features and a Kohonen Self-Organized Feature Map Neural Network

This paper proposes a new method for reducing the number of colors in an image. The proposed approach uses both the image color components and local image characteristics to feed a Kohonen self‐organized feature map (SOFM) neural network. After training, the neurons of the output competition layer define the proper color classes. The final image has the dominant image colors and its texture approaches the image local characteristics used. To speed up the entire algorithm and reduce memory requirements, a fractal scanning subsampling technique can be used. The method is applicable to all types of color images and can be easily extended to accommodate any type of spatial characteristics. Several experimental and comparative results are presented.

A new approach for the design of digital integrators

A new method is proposed for the design of digital integrators. The new method is based upon the formulation of an appropriate linear programming problem which assures a satisfactory minimax approximation error for the magnitude response in a predefined frequency range. In comparison with existing methods the new design approach constructs a novel class of digital integrators by optimal determining more than one independent coefficients. Their capability to approximate in the minimax sense the ideal integrator with a good accuracy is shown. Well-known integrators can be obtained as special cases of the proposed methodology. Appropriate constraints can be introduced to accommodate signals with low frequencies.