Em muitas aplicações de processamento de imagens é desejável converter imagens que estão em escala de cinza para imagens binarias, ou seja, em apenas dois níveis de intensidade. Para realizar essa tarefa de separação entre dois níveis é necessário que se calcule um valor de limiar, pois a partir dele determinamos quais pixels irão pertencer a um nível, normalmente o objeto de interesse, e quais pertencerão ao outro nível, ou ao fundo da imagem. Algumas aplicações exigem que se calcule esse valor de limiar em um tempo muito curto em relação à aquisição de imagem, principalmente quando ocorre uma variação muito alta de luminosidade na aquisição de uma imagem. Para suprir essa dificuldade de velocidade nas aplicações de processamento de imagem, uma alternativa seria o desenvolvimento de uma arquitetura dedicada que realize o cálculo do valor de limiar e binarize a imagem adquirida. O presente trabalho apresenta o desenvolvimento de uma arquitetura que realiza estas tarefas, implementada em circuitos reconfiguráveis do tipo FPGA. A validação da arquitetura foi obtida por meio da comparação dos resultados obtidos com a simulação da mesma na ferramenta Matlab. A arquitetura permite uma frequência máxima de clock de 84,52 MHz, o que permite a utilização da arquitetura em sistemas de tempo real, utilizando como fonte de imagem um vídeo composto ou uma câmera comum. / In many imaging applications it is desirable that images are converted to grayscale images to binary, ie with only two intensity levels. To accomplish this task separation between two levels is necessary to calculate a threshold value as determined from it which pixels will belong to a level generally the object of interest, and which belong to another level, or to the background image . Some applications require you to calculate this threshold value in a very short time in relation to image acquisition, especially when a very high brightness variation in the acquisition of an image. To meet this difficulty in the speed image processing applications, an alternative would be to develop an architecture dedicated to perform the calculation of the value of threshold and binarize the image acquired. This paper proposes the development of an architecture that performs these tasks by implementing reconfigurable circuits like FPGA. Making a comparison of results obtained with algorithms developed in Matlab, thus performing a validation of the proposed architecture. The developed architecture has reached the maximum frequency of 84.52 MHz, and the architecture can be operated in real-time system, using an image as a source of composite video or a regular camera.

Em muitas aplicações de processamento de imagens é desejável converter imagens que estão em escala de cinza para imagens binarias, ou seja, em apenas dois níveis de intensidade. Para realizar essa tarefa de separação entre dois níveis é necessário que se calcule um valor de limiar, pois a partir dele determinamos quais pixels irão pertencer a um nível, normalmente o objeto de interesse, e quais pertencerão ao outro nível, ou ao fundo da imagem. Algumas aplicações exigem que se calcule esse valor de limiar em um tempo muito curto em relação à aquisição de imagem, principalmente quando ocorre uma variação muito alta de luminosidade na aquisição de uma imagem. Para suprir essa dificuldade de velocidade nas aplicações de processamento de imagem, uma alternativa seria o desenvolvimento de uma arquitetura dedicada que realize o cálculo do valor de limiar e binarize a imagem adquirida. O presente trabalho apresenta o desenvolvimento de uma arquitetura que realiza estas tarefas, implementada em circuitos reconfiguráveis do tipo FPGA. A validação da arquitetura foi obtida por meio da comparação dos resultados obtidos com a simulação da mesma na ferramenta Matlab. A arquitetura permite uma frequência máxima de clock de 84,52 MHz, o que permite a utilização da arquitetura em sistemas de tempo real, utilizando como fonte de imagem um vídeo composto ou uma câmera comum. / In many imaging applications it is desirable that images are converted to grayscale images to binary, ie with only two intensity levels. To accomplish this task separation between two levels is necessary to calculate a threshold value as determined from it which pixels will belong to a level generally the object of interest, and which belong to another level, or to the background image . Some applications require you to calculate this threshold value in a very short time in relation to image acquisition, especially when a very high brightness variation in the acquisition of an image. To meet this difficulty in the speed image processing applications, an alternative would be to develop an architecture dedicated to perform the calculation of the value of threshold and binarize the image acquired. This paper proposes the development of an architecture that performs these tasks by implementing reconfigurable circuits like FPGA. Making a comparison of results obtained with algorithms developed in Matlab, thus performing a validation of the proposed architecture. The developed architecture has reached the maximum frequency of 84.52 MHz, and the architecture can be operated in real-time system, using an image as a source of composite video or a regular camera.

No description available.

The current state of the art optical character recognition (OCR) algorithms are capable of extracting text from images in predefined conditions. OCR is extremely reliable for interpreting machine-written text with minimal distortions, but images taken in a natural scene are still challenging. In recent years the topic of improving recognition rates in natural images has gained interest because more powerful handheld devices are used. The main problem faced dealing with recognition in natural images are distortions like illuminations, font textures, and complex backgrounds. Different preprocessing approaches to separate text from its background have been researched lately. In our study, we assess the improvement reached by two of these preprocessing methods called k-means and Otsu by comparing their results from an OCR algorithm. The study showed that the preprocessing made some improvement on special occasions, but overall gained worse accuracy compared to the unaltered images. / Dagens optisk teckeninläsnings (OCR) algoritmer är kapabla av att extrahera text från bilder inom fördefinierade förhållanden. De moderna metoderna har uppnått en hög träffsäkerhet för maskinskriven text med minimala förvrängningar, men bilder tagna i en naturlig scen är fortfarande svåra att hantera. De senaste åren har ett stort intresse för att förbättra tecken igenkännings algoritmerna uppstått, eftersom fler kraftfulla och handhållna enheter används. Det huvudsakliga problemet när det kommer till igenkänning i naturliga bilder är olika förvrängningar som infallande ljus, textens textur och komplicerade bakgrunder. Olika metoder för förbehandling och därmed separation av texten och dess bakgrund har studerats under den senaste tiden. I våran studie bedömer vi förbättringen som uppnås vid förbehandlingen med två metoder som kallas för k-means och Otsu genom att jämföra svaren från en OCR algoritm. Studien visar att Otsu och k-means kan förbättra träffsäkerheten i vissa förhållanden men generellt sett ger det ett sämre resultat än de oförändrade bilderna.

The dissertation describes the method of inserting polar-axis system into the video recording of cataract operation at the Geminy Eye Surgery, Zlin. At the theoretical part, it discusses requirements for inserting the polar-axis system including elimination of eye rotary movements captured by slit lamp. Then the emphasis is also on the speed of data processing. The practical part of the dissertation, focuses on the creating the method for detection of centers at the slit lamp picture, as well as video sequence and a method that eliminates potential eye rotation. For designing the program solution, following methods were used: Otsu method, Hough transformation method, method of two vertical projections, and crosscorrelation method. In total, the program solution was tested and analyzed in Matlab program on anonymous data set of 22 eyes.

In this thesis, a new algorithm has been proposed to segment the foreground of the fingerprint from the image under consideration. The algorithm uses three features, mean, variance and coherence. Based on these features, a rule system is built to help the algorithm to efficiently segment the image. In addition, the proposed algorithm combine split and merge with modified Otsu. Both enhancements techniques such as Gaussian filter and histogram equalization are applied to enhance and improve the quality of the image. Finally, a post processing technique is implemented to counter the undesirable effect in the segmented image. Fingerprint recognition system is one of the oldest recognition systems in biometrics techniques. Everyone have a unique and unchangeable fingerprint. Based on this uniqueness and distinctness, fingerprint identification has been used in many applications for a long period. A fingerprint image is a pattern which consists of two regions, foreground and background. The foreground contains all important information needed in the automatic fingerprint recognition systems. However, the background is a noisy region that contributes to the extraction of false minutiae in the system. To avoid the extraction of false minutiae, there are many steps which should be followed such as preprocessing and enhancement. One of these steps is the transformation of the fingerprint image from gray-scale image to black and white image. This transformation is called segmentation or binarization. The aim for fingerprint segmentation is to separate the foreground from the background. Due to the nature of fingerprint image, the segmentation becomes an important and challenging task. The proposed algorithm is applied on FVC2000 database. Manual examinations from human experts show that the proposed algorithm provides an efficient segmentation results. These improved results are demonstrating in diverse experiments.

Submitted by Eduarda Figueiredo (eduarda.ffigueiredo@ufpe.br) on 2015-03-06T15:24:45Z No. of bitstreams: 2 Dissertacao_Cordeiro Jr,M.M.pdf: 3941475 bytes, checksum: 87847586f1b95455343538f32a029b91 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Made available in DSpace on 2015-03-06T15:24:45Z (GMT). No. of bitstreams: 2 Dissertacao_Cordeiro Jr,M.M.pdf: 3941475 bytes, checksum: 87847586f1b95455343538f32a029b91 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2011-09-02 / CAPES / Nesta dissertação é proposto um novo procedimento para identificação de notas e acordes em registros de piano. O método apresentado utiliza técnicas de processamento de sinais de áudio e de imagem e pode ser dividido em cinco etapas, a saber: decomposição do sinal de áudio em componentes de energia utilizando bancos de filtros digitais por janelas Kaiser; conversão matriz-imagem seguida de binarização por limiar de Otsu; ajuste a partir de técnicas de conectividade entre os pixels; sobreposição temporal e composição dos resultados. O desafio consiste em conceber um aplicativo capaz de dispor de forma amigável o conteúdo musical (notas e acordes) contido num arquivo de áudio gravado via sintetizador ou pelo próprio instrumento a partir da implementação de técnicas especificas que usam processamento de sinais de áudio e imagem. A implementação é realizada a partir de diversos algoritmos desenvolvidos no Matlab® . Testes de funcionamento usando registros de piano (gravados via intetizador) foram realizados, obtendo taxas de acerto superiores a 88 %.

No description available.

This study was done on 86 images from Landsat 5 and 8 from the years 1990 to 2023, and emphasized the spatial change of Birendra Lake. The Otsu method was used on the normalized difference water index (NDWI), modified normalized difference water index 1 and 2 (MNDWI), and automated water extraction index (AWEI) for this study. The area of Birendra Glacial Lake area grew significantly between 1990 and 2023 (94,500 m2 to 222,300 m2) because of climate change. The mean annual temperature was increasing at a rate of 0.024 °C every year and the total precipitation was increasing at a rate of 14.531 mm every year. The increase in the snow depth from 1999 to 2007 was observed and the snow cover from 2011 to 2020 was also found to be increasing. The lake area was validated by using the KNN method. The lake area from Google Earth was found to be 246,060 m2 on 10/07/2013 while from the Otsu method, the lake area for the date 10/24/2013 was 222300 m2. A relative error of 10.69% was found when the result obtained from the Otsu method was compared with the result from the KNN method. Furthermore, a comprehensive study including a field survey of the lake is suggested for more accuracy.

No description available.