This paper presents a new approach for the automatic license plate recognition, which includes the SIFT algorithm in step to locate the plate in the input image. In this new approach, besides the comparison of the features obtained with the SIFT algorithm, the correspondence between the spatial orientations and the positioning associated with the keypoints is also observed. Afterwards, an algorithm is used for the character recognition of the plates, very fast, which makes it possible its application in real time. The results obtained with the proposed approach presented very good success rates, so much for locating the characters in the input image, as for their recognition.

The availability of low cost microcomputers and the evolution of computer networks have increased the development of distributed systems. In order to get a better process allocation on distributed environments, several load balancing algorithms have been proposed. Generally, these algorithms consider as the information policy's load index the length of the CPU's process waiting queue. This paper modifies the Server-Initiated Lowest algorithm by using a load index based on the resource occupation. Using this load index the Server-Initiated Lowest algorithm is compared to the Stable symmetrically initiated, which nowadays is defined as the best choice. The comparisons are made by using simulations. The simulations showed that the modified Server-Initiated Lowest algorithm had better results than the Symmetrically Initiated one.

Resumo: Este capítulo trata do reconhecimento de caracteres impressos e manuscritos, apresentando um algoritmo totalmente baseado na análise do comportamento das transições entre os pixels vizinhos nas imagens dos caracteres. A partir desta análise, são definidas regras que determinam em qual classe cada caractere deve ser colocado, caracterizando uma classificação supervisionada. A baixa complexidade deste algoritmo tem tornado possível o seu uso em aplicações onde o tempo de reconhecimentoé bastante crítico, comoé o caso de sistemas de reconhecimento em tempo real, usados em sistemas de visão computacional, como robôs e veículos não tripulados. Palavras-chave: Reconhecimento de caracteres, Classificação supervisionada, Análise de transições entre pixels, Processamento de vídeo.

Abstract-Super-resolution techniques allow co mbine mu ltip le images of the same scene to obtain an image with increased geometric and rad io metric resolution, called super-resolution image. In this image are enhanced features allowing to recover important details and informat ion. The object ive of this work is to develop efficient algorith m, robust and automated fusion image frames to obtain a super-resolution image. Image registration is a fundamental step in comb ining several images that make up the scene. Our research is based on the determination and extraction of characteristics defined by the SIFT and RA NSAC algorith ms for automat ic image registration. We use images containing characters and perform recognition of these characters to validate and show the effectiveness of our proposed method. The d istinction of this work is the way to get the matching and merging of images because it occurs dynamically between elements common images that are stored in a dynamic matrix.

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