This paper describes experiments with PHOC (Pyramid Histogram of Color) features descriptor in terms of capacity for representing features presented in breast radiograph (also known as mammography). Patches were taken from regions in digital mammographies, representing benign, cancerous, normal tissues and image’s background. The motivation is to evaluate the proposal in perspective of using it for execution in an inexpensive ordinary desktop computer in places located far from medical experts. The images were obtained from DDSM database and processed producing the feature-dataset used for training an Artificial Neural Network, the results were evaluated by analysis of the learning rate curve and ROC curves, besides these graphical analytical tools the confusion matrix and other quantitative metrics (TPR, FPR and Accuracy) were also extracted and analyzed. The average accuracy ≈ 0.8 and the other metrics extracted from results demonstrate that the proposal presents potential for further developments. At the best effort, PHOC was not found in literature for applications in mammographies such as it is proposed here.
RESUMO: Este texto relata a experiência de disseminação do Software Livre, realizada pelo grupo de extensão Colmeia do Departamento de Ciência da Computação da Universidade do Estado de Santa Catarina (UDESC). O conceito de Software Livre é brevemente discutido, bem como as atividades do grupo Colmeia, de onde são destacados os relatos das oficinas para divulgação do Software Livre educacional, denominado GCompris. Essas oficinas ocorreram durante o ano de 2012, nas dependências da Universidade do Estado de Santa Catarina, em Joinville, e tiveram como público alvo as crianças em reabilitação cognitiva, atendidas pela Associação de Assistência à Criança Deficiente (AACD) de Joinville-SC, bem como seus pais e cuidadores. PALAVRAS-CHAVE: Software livre. Hardware livre. Organização social. The dissemination of the free software GCompris in workshops for children in cognitive rehabilitation ABSTRACT: This paper reports the dissemination of a free software experienced by the group of extension research called Colméia, from the Science Computer Department of the University of the State of Santa Catarina (UDESC). The free software concept is briefly discussed, as well as the activities done by the group, from where the reports of workshops done for dissemination of the educational free software, GCompris, are taken. These workshops took place during 2012 at the University of the State of Santa Catarina, in Joinville, Brazil. They aimed to reach children in cognitive rehabilitation assisted by the Disabled Children Assistance Association (Associação de Assistência à Criança Deficiente-AACD) from the same city cited above, as well as their parents and caregivers.
It is proposed a novel approach based on a three-dimensional active contour (3D-snake), for the tracking and measurement of the length of artificially generated electrical arcs captured by a calibrated pair of cameras. The 3D-snake is represented by a B-spline which evolves in 3D space constrained by internal and external forces. The external forces come from pairs of images of the arc in evolution. In this work the electrical arcs present significant elongation and are produced in a high voltage laboratory in an open sky experiment with real scale power towers. This is an unfavorable situation for the adaptation of other works that apply techniques of image analysis of electric arcs, which are not aimed at the measurement in question and apply strategies rather tied to devices and environments used for arcs generation. On its turn, the strategy presented in this paper is distinguished by greater adaptability specially about the cameras positioning, additionally, it is practically independent on methods for the homologous points determination. For evaluation the proposed approach it was done experiments using synthetic images obtained from analytical curves as also using real images of the electrical arcs artificially generated. The results were considered acceptable verifying the potential of the proposed approach.
The coffee capsules brought practicality and speed in the preparation of the drink. However, with its popularization came a major environmental problem, the generation of a large amount of garbage, which for 2021 has an estimated 14 thousand tons of garbage, only coming from the capsules. To avoid this disposal it is necessary to recycle them, however it is not a trivial job, since they are composed of various materials, as well as the collection of these capsules presents challenges. Therefore, a collection system is of great value, which, in addition to being automated, generates bonuses proportional to the quantity of discarded capsules. This work is dedicated preliminary tests on the development of such a system using a convolutional neural network for the detection of coffee capsules. This algorithm was trained with two image sets, one containing images with reflection and the other without, which presented an accuracy of approximately 97%.
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