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Gliwice, Poland

Boryczka U.,University of Silesia | Dworak K.,Future Processing | Dworak K.,University of Silesia
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

This paper presents how techniques such as evolutionary algorithms (EAs) can optimize complex cryptanalysis processes. The main goal of this article is to introduce a special algorithm, which allows executing an effective cryptanalysis attack on a ciphertext encoded with a classic transposition cipher. In this type of cipher, the plaintext letters are modified by permutation. The most well-known problem, which is often solved with optimization techniques operating on a set of permutations, is the Travelling Salesman Problem (TSP). The mentioned algorithm uses a specially prepared function of assessment of the individuals with a set of genetic operators, used in the case of TSP problem. © Springer International Publishing Switzerland 2014.


Nalepa J.,Future Processing | Nalepa J.,Silesian University of Technology | Kawulok M.,Silesian University of Technology
Proceedings - 2013 IEEE International Symposium on Multimedia, ISM 2013 | Year: 2013

This paper introduces a new parallel algorithm (PA) for fast hand shape classification. This problem is challenging as a hand is characterized by a high number of degrees of freedom. Our objective is to design and implement a robust algorithm suitable for real-time applications. We show how the analysis time can be decreased, together with the increase of the classification accuracy, by the means of parallelization. Also, we propose to combine the shape contexts approach with the appearance-based techniques to increase the efficacy of the PA. An extensive experimental study confirms the effectiveness of the proposed PA compared with other state-of-the-art methods. © 2013 IEEE.


Radlak K.,Silesian University of Technology | Frackiewicz M.,Silesian University of Technology | Szczepanski M.,Silesian University of Technology | Kawulok M.,Silesian University of Technology | Czardybon M.,Future Processing
Proceedings - Frontiers in Education Conference, FIE | Year: 2015

In this paper, we present Adaptive Vision Studio (AVS) - a novel tool for creating image processing and analysis algorithms. AVS has been applied in post-graduate computer vision course for students of Automatic Control and Biotechnology at Silesian University of Technology. This software is a powerful environment with ready-for-use image analysis filters for computer vision experts as well as for engineers, who are beginners in this field. AVS has been published as a freeware version for noncommercial and educational purposes recommended for students and engineers, who want to learn how to develop complex image processing algorithms. Lite version of AVS is freely available at https://adaptive-vision.com. © 2015 IEEE.


Pawelczyk K.,Future Processing | Kawulok M.,Future Processing | Kawulok M.,Silesian University of Technology
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

In this paper we explore the possibilities of recognizing head orientation based on the appearance of the nose. We demonstrate that the features extracted from that region possess high discriminating power with regards to the head orientation. Extensive experimental validation study, performed using the benchmark data, confirmed high effectiveness of the proposed approach compared with the baseline techniques that rely on the analysis of the entire facial region. © Springer International Publishing Switzerland 2014.


Cwiek M.,Future Processing | Nalepa J.,Silesian University of Technology
GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference | Year: 2014

This paper presents a fast genetic algorithm (GA) for solving the flexible job shob scheduling problem (FJSP). The FJSP is an extension of a classical NP-hard job shop scheduling problem. Here, we combine the active schedule constructive crossover (ASCX) with the generalized order crossover (GOX). Also, we show how to divide a population of solutions in the high-low fit selection scheme in order to guide the search efficiently. An initial experimental study indicates high convergence capabilities of the proposed GA.

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