Que coisa é essa, Yôyô?: cor e raça na imprensa ilustrada da Bahia (1897-1904)

From techniques such as lithography and woodcut, it was possible to create and reproduce daily images in the newspapers of the Empire and the Republic of Brazil. The purpose of this study is to make a historiographic report, derived from a multidisciplinary theoretical analysis to which several printed visual documents were selected from the newspaper A Coisa from Salvador, in Bahia. The weekly news, edited in the capital and distributed also in the countryside of Bahia by the end of 1897 and the beginning of 1904 is rich for its illustrations and the satirical, humorous and critical content, signed by its editors. The images in A Coisa are appealing for their content filled with tensions inherent to the time of the First Republic in Brazil, such as issues regarding ones skin color, phenotypes, race, gender, the value and the social ranking of the black population. The paper, in its gathering of texts and images, is the main basis of this research corpus, in which a dialogue with other papers from other places and times is proposed so that it becomes evident the historical process that marks the ideal of nation and the construction of a body and an identity for the people of African Descent in Brazil. The observation and analysis of the selected images from the newspaper allow the identification of its way of production, the orientation of a reality in function of its target consumers, their authorship and the objectives to which it was created. Therefore, this work aims to critically analyze the representations given to the black body and skin, in order to problematize the memories of these bodies and their sociocultural meanings and, thus, question, through a methodology aimed to the description and analysis of images united to texts, these bodies visual representations possible contribution to the formation of an idea of black people unified identity, and their social alterity in deference to the memories given to the white society in the historical and social context of that time.

Algorithms for super-resolution of images based on Sparse Representation and Manifolds

lmage super-resolution is defined as a class of techniques that enhance the spatial resolution of images. Super-resolution methods can be subdivided in single and multi image methods. This thesis focuses on developing algorithms based on mathematical theories for single image super­ resolution problems. lndeed, in arder to estimate an output image, we adopta mixed approach: i.e., we use both a dictionary of patches with sparsity constraints (typical of learning-based methods) and regularization terms (typical of reconstruction-based methods). Although the existing methods already per- form well, they do not take into account the geometry of the data to: regularize the solution, cluster data samples (samples are often clustered using algorithms with the Euclidean distance as a dissimilarity metric), learn dictionaries (they are often learned using PCA or K-SVD). Thus, state-of-the-art methods still suffer from shortcomings. In this work, we proposed three new methods to overcome these deficiencies. First, we developed SE-ASDS (a structure tensor based regularization term) in arder to improve the sharpness of edges. SE-ASDS achieves much better results than many state-of-the- art algorithms. Then, we proposed AGNN and GOC algorithms for determining a local subset of training samples from which a good local model can be computed for recon- structing a given input test sample, where we take into account the underlying geometry of the data. AGNN and GOC methods outperform spectral clustering, soft clustering, and geodesic distance based subset selection in most settings. Next, we proposed aSOB strategy which takes into account the geometry of the data and the dictionary size. The aSOB strategy outperforms both PCA and PGA methods. Finally, we combine all our methods in a unique algorithm, named G2SR. Our proposed G2SR algorithm shows better visual and quantitative results when compared to the results of state-of-the-art methods.