Programa Etnomatemática: ponderações da prática pedagógica

The aim of this present work is investigating the interest and motivation for learning, awakened in pupils when the educator practice is guided by the ethnomathematics perspective. The main question is: Can an ethnomathematic approach awaken enthusiasm in pupils, causing it to become more critic and active in building their knowledge? The methodology that guides the investigation is qualitative, based on technical arising of the ethnographic case study. Theoretical contributions that support the investigation are from the scientific methodology and from ethnomathematics. The research material is composed by: researcher’s field diary, audio recording of participant observation, interviews reports of community residents and students parents, highlighting the material produced by students. This study was developed on an 8º year of high school of rural community. During the work were prioritized the ethnomathematics concepts of the Ethnomathematic Program, which establish a link exchange, where the lecturers inserts themselves on the reality of pupils in a way that promote an appreciation of their identity and a commitment to their learning. The educator investigates and values the ideas of pupils throughout dialogues. There are challenges for the application of education with ethno mathematic perspective, pointed out by authors, listed and supplemented in the research. In this context, it is believed that the socio-cultural knowledge must be respect, and as they are understood their specialties, capabilities and characteristics, this can guide teaching practice, making significant process for pupils, providing appropriation of scientific knowledge. Analysis of research practice indicated that students, research subjects, when they decided contextual issues, with their way of life, felt appreciated. The conclusion is that, with continuous action of contextualized of school mathematics, from the recognition of the environment and of cultural identity, the educator has the opportunity of review their own participant condition, and therefore promote an enthusiasm for learning. Because a motivated pupil becomes active, since that the all project is guided in a significant theme.

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.