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.

Técnicas de manejo agropecuário sustentável

The competitiveness in the rural sector and the need to make viable and sustainable property, direct the farmer to seek new production strategies. In this sense, the book Techniques of sustainable agricultural management has as objective contributed information on concepts, management practices, technological innovations, which are applicable in the agricultural production. The same is composed of 13 chapters, topics covered in aquaculture production, management and dairy production, as general aspects of hematology fish; dynamics of decision-making and adaptive flow dairy production systems; importance of performance measures and body biometrics in small ruminants; milk production in beef cows; parasitism in beef cattle; performance of dairy cows in production; efficiency of cross beef cattle in finishing phase; development of Marchangus: five years; and, bovine growth efficiency. In vegetable production area are addressed matters relating on management and olive cultivation, species of great economic importance and diversification as alternative on the property; functional foods in fruit and vegetables; influence of environmental factors, harvesting and drying in the production and composition of essential oils of Mentha spp; and, implication of the contamination of corn grain by mycotoxins in livestock production. At the end of the book, the expectation of the authors is to have contributed with relevant themes of Brazilian agriculture, which could reflect positively on knowledge, values and quality of available material.