A contribution to semantic description of images and videos: an application of soft biometrics

Humans have a high ability to extract visual data information acquired by sight. Trought a learning process, which starts at birth and continues throughout life, image interpretation becomes almost instinctively. At a glance, one can easily describe a scene with reasonable precision, naming its main components. Usually, this is done by extracting low-level features such as edges, shapes and textures, and associanting them to high level meanings. In this way, a semantic description of the scene is done. An example of this, is the human capacity to recognize and describe other people physical and behavioral characteristics, or biometrics. Soft-biometrics also represents inherent characteristics of human body and behaviour, but do not allow unique person identification. Computer vision area aims to develop methods capable of performing visual interpretation with performance similar to humans. This thesis aims to propose computer vison methods which allows high level information extraction from images in the form of soft biometrics. This problem is approached in two ways, unsupervised and supervised learning methods. The first seeks to group images via an automatic feature extraction learning , using both convolution techniques, evolutionary computing and clustering. In this approach employed images contains faces and people. Second approach employs convolutional neural networks, which have the ability to operate on raw images, learning both feature extraction and classification processes. Here, images are classified according to gender and clothes, divided into upper and lower parts of human body. First approach, when tested with different image datasets obtained an accuracy of approximately 80% for faces and non-faces and 70% for people and non-person. The second tested using images and videos, obtained an accuracy of about 70% for gender, 80% to the upper clothes and 90% to lower clothes. The results of these case studies, show that proposed methods are promising, allowing the realization of automatic high level information image annotation. This opens possibilities for development of applications in diverse areas such as content-based image and video search and automatica video survaillance, reducing human effort in the task of manual annotation and monitoring.

Adaptação do paradigma orientado a notificações para desenvolvimento de sistemas fuzzy

This work proposes to adjust the Notification Oriented Paradigm (NOP) so that it provides support to fuzzy concepts. NOP is inspired by elements of imperative and declarative paradigms, seeking to solve some of the drawbacks of both. By decomposing an application into a network of smaller computational entities that are executed only when necessary, NOP eliminates the need to perform unnecessary computations and helps to achieve better logical-causal uncoupling, facilitating code reuse and application distribution over multiple processors or machines. In addition, NOP allows to express the logical-causal knowledge at a high level of abstraction, through rules in IF-THEN format. Fuzzy systems, in turn, perform logical inferences on causal knowledge bases (IF-THEN rules) that can deal with problems involving uncertainty. Since PON uses IF-THEN rules in an alternative way, reducing redundant evaluations and providing better decoupling, this research has been carried out to identify, propose and evaluate the necessary changes to be made on NOP allowing to be used in the development of fuzzy systems. After that, two fully usable materializations were created: a C++ framework, and a complete programming language (LingPONFuzzy) that provide support to fuzzy inference systems. From there study cases have been created and several tests cases were conducted, in order to validate the proposed solution. The test results have shown a significant reduction in the number of rules evaluated in comparison to a fuzzy system developed using conventional tools (frameworks), which could represent an improvement in performance of the applications.