Face Centered Image Analysis Using Saliency and Deep Learning Based Techniques

Image analysis starts with the purpose of configuring vision machines that can perceive like human to intelligently infer general principles and sense the surrounding situations from imagery. This dissertation studies the face centered image analysis as the core problem in high level computer vision research and addresses the problem by tackling three challenging subjects: Are there anything interesting in the image? If there is, what is/are that/they? If there is a person presenting, who is he/she? What kind of expression he/she is performing? Can we know his/her age? Answering these problems results in the saliency-based object detection, deep learning structured objects categorization and recognition, human facial landmark detection and multitask biometrics. To implement object detection, a three-level saliency detection based on the self-similarity technique (SMAP) is firstly proposed in the work. The first level of SMAP accommodates statistical methods to generate proto-background patches, followed by the second level that implements local contrast computation based on image self-similarity characteristics. At last, the spatial color distribution constraint is considered to realize the saliency detection. The outcome of the algorithm is a full resolution image with highlighted saliency objects and well-defined edges. In object recognition, the Adaptive Deconvolution Network (ADN) is implemented to categorize the objects extracted from saliency detection. To improve the system performance, L1/2 norm regularized ADN has been proposed and tested in different applications. The results demonstrate the efficiency and significance of the new structure. To fully understand the facial biometrics related activity contained in the image, the low rank matrix decomposition is introduced to help locate the landmark points on the face images. The natural extension of this work is beneficial in human facial expression recognition and facial feature parsing research. To facilitate the understanding of the detected facial image, the automatic facial image analysis becomes essential. We present a novel deeply learnt tree-structured face representation to uniformly model the human face with different semantic meanings. We show that the proposed feature yields unified representation in multi-task facial biometrics and the multi-task learning framework is applicable to many other computer vision tasks

Reconstrução e reconhecimento de imagens binárias utilizando o algoritmo Máquina de Boltzmann

The objective of this work is to use algorithms known as Boltzmann Machine to rebuild and classify patterns as images. This algorithm has a similar structure to that of an Artificial Neural Network but network nodes have stochastic and probabilistic decisions. This work presents the theoretical framework of the main Artificial Neural Networks, General Boltzmann Machine algorithm and a variation of this algorithm known as Restricted Boltzmann Machine. Computer simulations are performed comparing algorithms Artificial Neural Network Backpropagation with these algorithms Boltzmann General Machine and Machine Restricted Boltzmann. Through computer simulations are analyzed executions times of the different described algorithms and bit hit percentage of trained patterns that are later reconstructed. Finally, they used binary images with and without noise in training Restricted Boltzmann Machine algorithm, these images are reconstructed and classified according to the bit hit percentage in the reconstruction of the images. The Boltzmann machine algorithms were able to classify patterns trained and showed excellent results in the reconstruction of the standards code faster runtime and thus can be used in applications such as image recognition.Dissertação (Mestrado)O objetivo deste trabalho é utilizar algoritmos conhecidos como Máquina de Boltzmann para reconstruir e classificar padrões como de imagens. Este algoritmo possui uma estrutura parecida com a de uma Rede Neural Artificial porém os nós da rede possuem decisões estocásticas e probabilísticas. Neste trabalho é apresentado o referencial teórico das principais Redes Neurais Artificiais, do algoritmo Máquina de Boltzmann Geral e uma variação deste algoritmo conhecida como Máquina de Boltzmann Restrita. São realizadas simulações computacionais comparando os algoritmos Rede Neural Artificial Backpropagation com estes algoritmos Máquina de Boltzmann Geral e Máquina de Boltzmann Restrita. Através de simulações computacionais são analisados os tempos de execuções dos diferentes algoritmos descritos e os percentuais de acerto de bits dos padrões treinados que são posteriormente reconstruídos. Por fim, são utilizadas imagens binárias com e sem ruído no treinamento do algoritmo Máquina de Boltzmann Restrita, estas imagens são reconstruídas e classificadas de acordo com o percentual de acerto de bits na reconstrução das imagens. Os algoritmos Máquina de Boltzmann foram capazes de classificar padrões treinados e apresentaram ótimos resultados na reconstrução dos padrões com um rápido tempo de execução do código podendo assim ser utilizado em aplicações como de reconhecimento de imagens