III WVC 2007 - Iris.sel.eesc.sc.usp.br - USP

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WVC'2007 - III Workshop de Visão Computacional, 22 a 24 de Outubro de 2007, São José do Rio Preto, SP.Comparing the results with those obtained from [8] [9], our implementation presentedimprovements in fitness, processing time and programming flexibility.Table 1 summarizes all the results obtained.Table 1. Summary of results.ExampleHead extractionGenerationschromosomes Tree Cross Mut Repr MAE Training Executiondepth rate rate rate error time time50 25 6 90% 20% 20% 0,7% 71s 0,02s4 ConclusionsIn this paper a method for automatic pattern recognition of binary image objects usingmorphological operators and a linear genetic programming approach was presented.An application example has been presented where the solution has been expressed interms of the basic morphological operators, dilation and erosion, in conjunction withother instructions. Comparing with other methods described in the literature, the developedmethodology presents many advantages such as an improvement in processingtime, fitness and flexibility in relation to program size (variable) and types of operators.The developed method can be used as a guide to morphological design.References1. E. R. Dougherty, An introduction to Morphological Image Processing, SPIE, Washington,1992.2. J. Serra, Image Analysis and Mathematical Morphology, Academic Press Inc, California,1982.3. S. Marshall, N. R. Harvey, D. Greenhalgh, Design of Morphological Filters usingGenetic Algorithms, EUSIPCO, Tampere, Finland, September, 2000.4. I. Yoda, K. Yamamoto, H. Yamada, Automatic acquisition of hierarchical mathematicalmorphology procedures by genetic algorithms, Image and Vision Computing,17(10) (1999) 749-760.5. J. Koza, Genetic Programming, MIT Press, 1992.6. J. Holland, Adaptation in Natural and Artificial Systems, MIT Press, 1975.7. M. I. Quintana, R. Poli, E. Claridge, Genetic programming for mathematical morphologyalgorithm design on binary images, In M. Proceedings of the InternationalConference KBCS, 2002, pp. 161-171.8. N. R. Harvey, S. Marshall, The use of genetic algorithms in morphological filterDesign, Signal Processing: Image Communication 8(1) (1996) 55-72.9. H. Miyao, Y. Nakano, Head and stem extraction from printed music scores using aneural network approach, ICDAR, 1995, pp. 1074-1079.185
WVC'2007 - III Workshop de Visão Computacional, 22 a 24 de Outubro de 2007, São José do Rio Preto, SP.Digital Image Detection of Brazilian Car License Plates Based on MatchedFiltersB.B. Leite 1 , J.M. de Seixas 21 INdT – Nokia Technology Institute2 LPS/COPPE/EP – Signal Processing Laboratorybruno.leite@indt.org.br, seixas@lps.ufrj.brAbstractMachine vision techniques play an increasing rolein the development of automating systems. Theconstant growth of vehicle flow in the cities makes itsmonitoring and identification an important issue. Thiswork aims at developing the first step in an automaticidentification system for Brazilian private car licenseplates. This is performed by detecting their imagesfrom car digital images in which license plate imagesare randomly located. For this, line spectral analysisand matched filters are employed. The detectionefficiency was found to be above 81% for good qualityimages acquired in practical conditions.1. IntroductionThe constant growth of vehicle flow in the citiesmakes its monitoring to be an important issue. Themain monitoring objectives are: flow control, scrutinyand vehicle identification.So far, vehicle identification has mainly beenperformed using electronic tags. Computer vision,signal/image processing and pattern recognitiontechniques are still not widely used but it can be seen asa different way to solve the problem. In some cases,where the use of electronic tags is impossible,automatic vehicle identification can be achievedthrough image processing techniques. Although theapproach based on signal processing is more sensitiveto climatic conditions, such as fog, rain and luminosityvariation along the day, its acceptance is beginning toincrease among urban traffic control technicians andauthorities and also among the public securitydepartments [1].Presently, some systems address the vehicle flowcontrol by using digitized image processing in a varietyof ways. However, the efficiencies reached are so farstill not completely satisfactory, which avoids the wideuse of such approach.A broad range of applications can be identified forthis type of system, such as: detect offenders, controltoll units and parking areas, vehicle origin/destinationstudy, traffic flow planning and measurement, stolencar location, access control to restrict areas, etc [2].1.1. State of the artBasically there are two different types of vehicleidentification systems: RF systems and image detectionsystems. The RF systems are composed by RF ID’sstuck on the vehicle front window and detectors thatare usually placed in controlled areas (parking places,toll areas). When the RF ID passes through thedetectors, it is identified and, in the case of controlledareas, the vehicle is authorized or not to get into theseareas. However, this kind of system has a securityproblem: the RF ID can be moved from one vehicle toanother, which makes a loose association in thedatabase with the correct vehicle. Thus, it is notpossible to fully assign the RF ID to the vehicle licenseplate.On the other hand, image-based systems do not needany device installed in the vehicles or any additionalvehicle identifier. It simply recognizes the vehicle byits license plate, a mandatory item in every registeredvehicle.The minimum configuration normally used in anautomatic image processing or RF vehicle license platerecognition system (see Figure 1.1) comprises anelectric or inductive sensor whose objective is to detectthe vehicle (system trigger); a camera, responsible foracquiring the image; a DAQ board, which acquires thesignals from the camera; and a digital processor(usually a computer) that works as a traffic analyzer,once it is responsible for locating the license plate andalso for recognizing its characters.186
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