Detecção de região saliente em imagens usando dissimilaridade de cor e amostragem por pixeis aleatórios

The purpose of this work is to demonstrate and to assess a simple algorithm for automatic estimation of the most salient region in an image, that have possible application in computer vision. The algorithm uses the connection between color dissimilarities in the image and the image’s most salient region. The algorithm also avoids using image priors. Pixel dissimilarity is an informal function of the distance of a specific pixel’s color to other pixels’ colors in an image. We examine the relation between pixel color dissimilarity and salient region detection on the MSRA1K image dataset. We propose a simple algorithm for salient region detection through random pixel color dissimilarity. We define dissimilarity by accumulating the distance between each pixel and a sample of n other random pixels, in the CIELAB color space. An important result is that random dissimilarity between each pixel and just another pixel (n = 1) is enough to create adequate saliency maps when combined with median filter, with competitive average performance if compared with other related methods in the saliency detection research field. The assessment was performed by means of precision-recall curves. This idea is inspired on the human attention mechanism that is able to choose few specific regions to focus on, a biological system that the computer vision community aims to emulate. We also review some of the history on this topic of selective attention.

Estudo empírico de antecedentes do sucesso de sistemas de informação: análise de um sistema de apoio à decisão em uma universidade pública

Universities are institutions that generate and manipulate large amounts of data as a result of the multiple functions they perform, of the amount of involved professionals and students they attend. Information gathered from these data is used, for example, for operational activities and to support decision-making by managers. To assist managers in accomplishing their tasks, the Information Systems (IS) are presented as tools that offer features aiming to improve the performance of its users, assist with routine tasks and provide support to decision-making. The purpose of this research is to evaluate the influence of the users features and of the task in the success of IS. The study is of a descriptive-exploratory nature, therefore, the constructs used to define the conceptual model of the research are known and previously validated. However, individual features of users and of the task are IS success antecedents. In order to test the influence of these antecedents, it was developed a decision support IS that uses the Multicriteria Decision Aid Constructivist (MCDA-C) methodology with the participation and involvement of users. The sample consisted of managers and former managers of UTFPR Campus Pato Branco who work or have worked in teaching activities, research, extension and management. For data collection an experiment was conducted in the computer lab of the Campus Pato Branco in order to verify the hypotheses of the research. The experiment consisted of performing a distribution task of teaching positions between the academic departments using the IS developed. The task involved decision-making related to management activities. The data that fed the system used were real, from the Campus itself. A questionnaire was answered by the participants of the experiment in order to obtain data to verify the research hypotheses. The results obtained from the data analysis partially confirmed the influence of the individual features in IS success and fully confirmed the influence of task features. The data collected failed to support significant ratio between the individual features and the individual impact. For many of the participants the first contact with the IS was during the experiment, which indicates the lack of experience with the system. Regarding the success of IS, the data revealed that there is no significance in the relationship between Information Quality (IQ) and Individual Impact (II). It is noteworthy that the IS used in the experiment is to support decision-making and the information provided by this system are strictly quantitative, which may have caused some conflict in the analysis of the criteria involved in the decision-making process. This is because the criteria of teaching, research, extension and management are interconnected such that one reflects on another. Thus, the opinion of the managers does not depend exclusively on quantitative data, but also of knowledge and value judgment that each manager has about the problem to be solved.