Keiji Yamanaka

A novel adaptive morphological approach for degraded character image segmentation

This work proposes a novel adaptive approach for character segmentation and feature vector extraction from seriously degraded images. An algorithm based on the histogram automatically detects fragments and merges these fragments before segmenting the fragmented characters. A morphological thickening algorithm automatically locates reference lines for separating the overlapped characters. A morphological thinning algorithm and the segmentation cost calculation automatically determine the baseline for segmenting the connected characters. Basically, our approach can detect fragmented, overlapped, or connected character and adaptively apply for one of three algorithms without manual fine-tuning. Seriously degraded images as license plate images taken from real world are used in the experiments to evaluate the robustness, the flexibility and the effectiveness of our approach. The system approach output data as feature vectors keep useful information more accurately to be used as input data in an automatic pattern recognition system.

Parameters Estimation of a Horizontal Multilayer Soil Using Genetic Algorithm

This paper presents an optimization methodology by using a genetic algorithm (GA) to obtain the parameters of a soil that can be represented in a multilayer structure. The method uses a curve of experimental apparent resistivity obtained from measurements made in the soil. This experimental curve is compared with another curve of apparent resistivity of the soil, theoretical, produced by the GA. The theoretical curve is based on Sundes Algorithm and is exactly the inverse process used for the horizontal stratification of the soil in multilayers. With both curves in hand, the error produced in the process of soil stratification can be estimated. From the estimated errors, the parameters are optimized. The main difference of this method, comparing the already developed methods is just that, besides optimizing the resistivities ρi and the thicknesses hi of each soil layer, the proposed method also optimizes the layers quantity, seeking the best soil stratification. The obtained results of this paper are compared with other soil stratification methods.

Morphological preprocessing method to thresholding degraded word images

This paper presents a novel preprocessing method based on mathematical morphology techniques to improve the subsequent thresholding quality of raw degraded word images. The raw degraded word images contain undesirable shapes called critical shadows on the background that cause noise in binary images. This noise constitutes obstacles to posterior segmentation of characters. Direct application of a thresholding method produces inadequate binary versions of these degraded word images. Our preprocessing method called Shadow Location and Lightening (SL*L) adaptively, accurately and without manual fine-tuning of parameters locates these critical shadows on grayscale degraded images using morphological operations, and lightens them before applying eventual thresholding process. In this way, enhanced binary images without unpredictable and inappropriate noise can be provided to subsequent segmentation of characters. Then, adequate binary characters can be segmented and extracted as input data to optical character recognition (OCR) applications saving computational effort and increasing recognition rate. The proposed method is experimentally tested with a set of several raw degraded images extracted from real photos acquired by unsophisticated imaging systems. A qualitative analysis of experimental results led to conclusions that the thresholding result quality was significantly improved with the proposed preprocessing method. Also, a quantitative evaluation using a testing data of 1194 degraded word images showed the essentiality and effectiveness of the proposed preprocessing method to increase segmentation and recognition rates of their characters. Furthermore, an advantage of the proposed method is that Otsus method as a simple and easily implementable global thresholding technique can be sufficient to reducing computational load.

Exterior lighting computer-automated design based on multi-criteria parallel evolutionary algorithm

Multi-objective evolutionary algorithm to computer-automated exterior lighting design.Web client integrated to a cluster of computers to provide lighting design service.Solution to optimize both illumination quality and energy efficiency.Case study solution presents - 37.5% power consumption and +227.3% global uniformity. A proper professional lighting design implies in a continuous search for the best compromise between both low power consumption and better lighting quality. This search converts this design into a hard to solve multi-objective optimization problem. Evolutionary algorithms are widely used to attack that type of hard optimization problems. However, professionals could not benefit from that kind of assistance since evolutionary algorithms have been unexplored by several commercial lighting design computer-aided softwares. This work proposes a system based on evolutionary algorithms which implement a computer-automated exterior lighting design both adequate to irregular shaped areas and able to respect lighting pole positioning constraints. The desired lighting design is constructed using a cluster of computers supported by a web client, turning this application into an efficient and easy tool to reduce project cycles, increase quality of results and decrease calculation times. This ELCAutoD-EA system consists in a proposal for a parallel multi-objective evolutionary algorithm to be executed in a cluster of computers with a Java remote client. User must choose lighting pole heights, allowed lamps and fixtures, as well as the simplified blue print of the area to be illuminated, marking the sub-areas with restrictions to pole positioning. The desired average illuminance must also be informed as well as the accepted tolerance. Based on user informed data, the developed application uses a dynamic representation of variable size as a chromosome and the cluster executes the evolutionary algorithm using the Island model paradigm. Achieved solutions comply with the illumination standards requirements and have a strong commitment to lighting quality and power consumption. In the present case study, the evolved design used 37.5% less power than the reference lighting design provided by a professional and at the same time ensured a 227.3% better global lighting uniformity. A better lighting quality is achieved because the proposed system solves multi-objective optimization problems by avoiding power wastes which are often unclear to a professional lighting engineer in charge of a given project.

Rigorous Experimental Performance Analysis of Parallel Evolutionary Algorithms on Multicore Platforms

As multicore processors become ubiquitous, the improved performance available to parallel programs is a great motivation to computationally demanding evolutionary algorithms (EAs) to turn into parallel EAs (PEAs) and to be able to exploit the power of multicores. Parallel computing is a powerful way to reduce the computation time and to improve the quality of EAs solutions. To the stochastic nature of EAs, the known variability of the parallel programs execution times on multicores adds more complexity on PEAs performance evaluations. Performance evaluation methodologies need to adequately deal with the non-determinism in the experimental set. To obtain correct conclusions it is necessary to apply rigorous statistical procedures. The usual estimation of the speedup of a parallel program as the ratio of the sequential execution time and the parallel execution time may not be appropriated if some care is not taken. A correct estimation of the speedup as a performance measure is presented. A method based on the factorial experimental design is proposed to identify which are the significant factors on the performance of a PEA executed on a multicore processor. A case study of the performance analysis of a PEA solving a benchmark test function is presented.

Factorial design analysis applied to the performance of parallel evolutionary algorithms

BackgroundParallel computing is a powerful way to reduce computation time and to improve the quality of solutions of evolutionary algorithms (EAs). At first, parallel EAs (PEAs) ran on very expensive and not easily available parallel machines. As multicore processors become ubiquitous, the improved performance available to parallel programs is a great motivation to computationally demanding EAs to turn into parallel programs and exploit the power of multicores. The parallel implementation brings more factors to influence performance and consequently adds more complexity on PEA evaluations. Statistics can help in this task and can guarantee the significance and correct conclusions with minimum tests, provided that the correct design of experiments is applied.MethodsWe show how to guarantee the correct estimation of speedups and how to apply a factorial design on the analysis of PEA performance.ResultsThe performance and the factor effects were not the same for the two benchmark functions studied in this work. The Rastrigin function presented a higher coefficient of variation than the Rosenbrock function, and the factor and interaction effects on the speedup of the parallel genetic algorithm I (PGA-I) were different in both.ConclusionsAs a case study, we evaluate the influence of migration related to parameters on the performance of the parallel evolutionary algorithm solving two benchmark problems executed on a multicore processor. We made a particular effort in carefully applying the statistical concepts in the development of our analysis.

Development of a Genetic Algorithm to Improve a UAV Route Tracer Applied to a Man-in-the-Loop Flight Simulator

This paper aims to present applications of Computational, which uses the method of genetic algorithms to improve the flight paths of UAVs, applied in a simulator with a hardware Man-in-the-loop.

Green public lighting design solved by a remote Genetic Algorithm application

Lighting Design is a field of engineering that often misses artificial intelligence tools and computational approaches to help designers. Genetic Algorithm (GA) is a widely used heuristic for search and optimization. This work presents results from applying GA and Web services to develop a computer-generated public lighting design remote application. This application is hosted in a cluster computing environment that supports Web services. A case study is also presented: the achieved solution shows a superior uniformity of illumination with almost 20% of economy on monthly power consumption when compared to the previous edified one.

Improving iris recognition through new target vectors in MLP artificial neural networks

This paper compares the performance of multilayer perceptron (MLP) networks trained with conventional bipolar target vectors (CBVs) and orthogonal bipolar new target vectors (OBVs) for biometric pattern recognition. The experimental analysis consisted of using biometric patterns from CASIA Iris Image Database developed by Chinese Academy of Sciences - Institute of Automation. The experiments were performed in order to obtain the best recognition rates, leading to the comparison of results from both conventional and new target vectors. The experimental results have shown that MLPs trained with OBVs can better recognize the patterns of iris images than MLPs trained with CBVs.

Novel nonspeech tones for conceptualizing spatial information

We propose a novel concept toward interfaces that can provide visually impaired persons with the opportunity to recover the freedom to conceptualize their environment without depending on conventional voice synthesizer systems. Fourteen subjects participated in ten experiments to provide results that evaluated their performances to conceptualize spatial information based on cues in “artificial-sounding” (AS) and “natural-sounding” (NS) tones. The source of AS tones was the digitized sound used by the vOICe Learning Edition, and the source of NS tones was fan noise with analogs in everyday listening. Experimental results revealed that the use of NS tones was essential for improving the conceptualization performance of subjects as the eventual users of novel human–environment interfaces.