A Rigid Image Registration Based on the Nonsubsampled Contourlet Transform and Genetic Algorithms

Image registration is a fundamental task used in image processing to match two or more images taken at different times, from different sensors or from different viewpoints. The objective is to find in a huge search space of geometric transformations, an acceptable accurate solution in a reasonable time to provide better registered images. Exhaustive search is computationally expensive and the computational cost increases exponentially with the number of transformation parameters and the size of the data set. In this work, we present an efficient image registration algorithm that uses genetic algorithms within a multi-resolution framework based on the Non-Subsampled Contourlet Transform (NSCT). An adaptable genetic algorithm for registration is adopted in order to minimize the search space. This approach is used within a hybrid scheme applying the two techniques fitness sharing and elitism. Two NSCT based methods are proposed for registration. A comparative study is established between these methods and a wavelet based one. Because the NSCT is a shift-invariant multidirectional transform, the second method is adopted for its search speeding up property. Simulation results clearly show that both proposed techniques are really promising methods for image registration compared to the wavelet approach, while the second technique has led to the best performance results of all. Moreover, to demonstrate the effectiveness of these methods, these registration techniques have been successfully applied to register SPOT, IKONOS and Synthetic Aperture Radar (SAR) images. The algorithm has been shown to work perfectly well for multi-temporal satellite images as well, even in the presence of noise

Computer Vision and Graphics for Heritage Preservation and Digital Archaeology

The goal of this work is to provide attendees with a survey of topics related to Heritage Preservation and Digital Archeology, which are challenging and motivating subjects to both computer vision and graphics community. These issues have been gaining increasing attention and priority within the scientific scenario and among funding agencies and development organizations over the last years. Motivations to this work are the recent efforts in the digital preservation of cultural heritage objects and sites before degradation or damage caused by environmental factors or human development. One of the main focuses of these researches is the development of new techniques for realistic 3D model building from images, preserving as much information as possible. We intend to introduce and discuss several emerging topics in computer vision and graphics related to the proposed theme while highlighting the major contributions and advances in these fields

Reconhecimento facil 3D usando Simulated Annealing e a Medida de Interpenetração de Superfícies

Resumo: Este trabalho apresenta um novo sistema automático para reconhecimento facial usando imagens 3D. O método proposto é baseado no algoritmo Simulated Annealing (SA) para o alinhamento de imagens de profundidade, sendo usada a Surface Interpenetration Measure (SIM) como medida de similaridade entre duas imagens. A medida de autenticação é obtida combinando a SIM calculada para quatro regiões da face: áreas circular e elíptica ao redor do nariz, parte superior da face e a face inteira. Além disso, uma abordagem modificada do SA é usada para minimizar os efeitos decorrentes das expressões faciais durante o alinhamento. Uma série de experimentos foram realizados na base de dados Face Recognition Grand Challenge (FRGC) v2, que é a maior base de faces 3D disponível atualmente, composta por 4.007 imagens com diferentes expressões faciais. Os experimentos realizados simularam sistemas de verificação e identificação, e os resultados obtidos foram comparados com os trabalhos estado-da-arte presentes na literatura. Usando todas as imagens da base FRGC v2 foi obtida uma taxa de verificação de 96,5%, com uma Taxa de Falsa Aceitação (False Acceptance Rate - FAR) de 0,1%. No cenário de identificação foi obtido um rank-one de 98,4%. Pelo nosso conhecimento, estes são os melhores resultados de identificação já apresentados usando a base FRGC v2, quando comparados com outros resultados apresentados na literatura