Slawomir Skoneczny

Mixed neural-traditional classifier for character recognition

In this paper, we present the efficient voting classifier for the recognition of handwritten and printed characters. This system consists of three voting nonlinear classifiers: two of them based on the multilayer perceptron, and one uses the moments method. The combination of these kinds of systems shows superiority of neural techniques applied with classical against exclusive traditional approach and results in high percentage of correctly recognized characters. Also, we present a comparison of the recognition results.

Image feature extraction using mathematical morphology

Mathematical morphology (MM) is a very efficient tool for image processing, based on non- linear operators.In this paper MM is applied to extract the images features. As a feature we understand specific information about the image i.e. location, size, orientation of certain image elements. Morphological operators are applied to find and measure objects on the images surface. Two practical examples are considered. First is devoted to analysis of binary images, containing printed characters. Characters are separated and MM is used to extract some information from each character. These features are later measured and included in a feature vector. It contains the special kind of information - the number of elements of the character with its shape modified in different ways. Second examples shows how feature extraction by MM works on graytone images. Images for analysis contain human faces. Morphological operators extract some important elements of human face. This information is very important to identify the human face. Experiments show us how the morphological operators can be applied to the feature detection. The simplest operators as erosion and dilation, as well as more sophisticated tools like: morphological filtering, geodesic transformations are used for that purpose. Also directional operations are applied to extract some areas. This paper includes algorithms for feature extraction by MM, as well as the brief description of morphological tools, explication of experiments and the results of them.

Object Localization and Detection Using Variance Filter

In this paper different variance filters for rejecting image regions that do not contain interesting object are tested. In our case the processed scenes have equally depth of focus, which makes difficult to distinguish objects from the background. In order to locate the object, the algorithm based on the sliding windows approach has been used. In case of using this type of algorithm a cascade of filters designed to reject windows that do not contain searched objects are applied. In this paper the authors put emphasis on elimination of redundant windows, from equally depth colour scenes, using various variance filters. Also a formula, based on the integral images, which can improve the efficiency of using directional variance filters, is proposed. All types of variance filters are tested and compared.

Classical and neural methods of image sequence interpolation

An image interpolation problem is often encountered in many areas. Some examples are interpolation for coding/decoding process for transmission purposes, reconstruction a full frame from two interlaced sub-frames in normal TV or HDTV, or reconstruction of missing frames in old destroyed cinematic sequences. In this paper an overview of interframe interpolation methods is presented. Both direct as well as motion compensated interpolation techniques are given by examples. The used methodology can also be either classical or based on neural networks depending on demand of a specific interpolation problem solving person.

Weighted Order Statistic Filters for Pattern Detection

In this paper we propose a method of using Weighted Order Statistic (WOS) filters for the task of pattern detection. Usually WOS filters are applied to noise removal. An efficient algorithm for pattern detection is described in details with emphasis put on the problem of a proper choice of filter windows. Also practical results of different pattern detection cases are presented.

Mathematical morphology and higher-order neural networks

Mathematical morphology (MM) is one of the most efficient tools in advanced digital image processing. Morphological techniques have been successfully applied in such cases as: image analysis, smoothing, enhancement, edge detection, skeletonization, filtering, and segmentation (watershed algorithms). Two essential operations of MM are dilation and erosion and can be implemented in several different ways. In our paper we propose their effective implementation by using higher order neural network approach (functional-link network). The novel structure and its learning method is presented. Some other neural network methods for MM operations are shown and compared with our approach.

Advanced Neural Networks Methods for Recognition of Handwritten Characters

In this paper, we present the efficient voting classifier for the recognition of handwritten characters. This system consists of three voting nonlinear classifiers: two of them base on the multilayer perceptron, and one uses the moments method. The combination of these kinds of systems showed superiority of neural techniques applied with classical against exclusive traditional approach and resulted in high percentage of correctly recognized characters. Also, we present a comparison of the recognition results.

Fractional order method of image keypoints detection

In this paper we propose a novel efficient method of characteristic image point detection based on the fractional order derivative. The concept of this approach called (FSIFT: Fractional-SIFT) is inspired by the Scale-Invariant Feature Transform (SIFT) proposed by Lowe and can be viewed as a certain generalization of this formula. The classical SIFT detector is implemented efficiently by using a difference of Gaussian (DoG) functions applied to image, in order to identify potential interest points. This difference is an approximation of the LoG (Laplacian of Gaussians) operator, which can be treated as the sum of the second order derivatives of the Gaussian image. In our method we take advantage of the fractional order derivative performed on the Gaussian images. In order to extract distinctive invariant features we have omitted the step of calculating DoG images. Instead of it, we have applied the fractional derivatives of different orders not far from the values of two. We have chosen the robust and efficient method of calculating the fractional order derivative using the Fourier domain. Many practical experiments have been performed. The promising results of our approach have been compared with the results of application of the well known algorithms like SURF and SIFT.

On Restoration of Degraded Cinematic Sequences by Means of Digital Image Processing

There are thousands of old black and white movies that are the cultural heritage of nations. These films are quite often seriously degraded. This is a problem of significant importance especially in Poland, where most of cinematic heritage was damaged during and after World War II. There is a wide spectrum of defects of different kinds and various complexity, which is a serious challenge for image processing scientists. In this paper a systematic methodology for solving these difficult problems is proposed. It contains an analysis of most common defects and introduces their taxonomy. The most important part of the work is devoted to the detection and removal of degradations. For this purpose different tools of image processing are applied, especially based on mathematical morphology. Considering the diversity and complexity of the defects one can easily observe that there is no uniform methodology that could be successfully applied to all degradation types. Unfortunately it does not seem to be possible to detect and remove all of them completely automatically. Therefore the whole system for semi-automatic treatment (with limited human interaction) is proposed.

Scanner Sequence Compensation

Professional film scanners acting in real time (24 frames per second) are still very expensive. In most cases using a slide scanner of medium resolution equipped with additional device for transporting a film reel would be a reasonable solution. The main problem, however is a lack of accurate positioning mechanism in such sort of scanners. Therefore the position of each frame could be to some extent accidental. If frames are scanned separately from each other and this process is performed for all the frames of a movie there is usually a significant jitter in this sequence. This paper presents an efficient and simple method of obtaining jitter-free sequence i.e. finding the precise cinematic frame location in a picture that is the output of the scanning process. The procedure consists of two steps: rough estimation and the fine one. During the rough step the borders of the frame can be detected based on finding area of maximal brightness. In the second step the displacements among frame backgrounds are calculated. Additionally in order to avoid the fixed background problem the local constant component is eliminated in the postprocessing phase. As a final result a jitter is removed almost completely.