Ó sistema capitalista, quanto do teu Portugal são lavagens de capital?

Ouvimos também muitos a falar em branqueamento de capitais, crimes financeiros, abuso de informação, manipulação do mercado, etc.. Salvo o devido respeito, contudo, poucos sabem o quão difícil é a prova destes crimes! Sabiam que p.e. até meados de 2008, crimes como o abuso de informação ou a manipulação do mercado eram insusceptíveis de interferência nas comunicações?! Sabiam que até hoje, o Código dos Valores Mobiliários não prevê a criminalização das pessoas colectivas e, portanto, dos próprios bancos?! Abstract: We hear too many to talk about money laundering, financial crimes, insider trading, market manipulation, etc .. Unless all due respect, however, few know how hard it is to prove that the crimes! Standing knew that by mid-2008, crimes such as dealing or market manipulation were incapable of interference in communications ?! They knew that to date, the Securities Code does not provide for the criminalization of legal persons and therefore the banks themselves ?!

Computer-aided recognition of dental implants in X-ray images

Dental implant recognition in patients without available records is a time-consuming and not straightforward task. The traditional method is a complete user-dependent process, where the expert compares a 2D X-ray image of the dental implant with a generic database. Due to the high number of implants available and the similarity between them, automatic/semi-automatic frameworks to aide implant model detection are essential. In this study, a novel computer-aided framework for dental implant recognition is suggested. The proposed method relies on image processing concepts, namely: (i) a segmentation strategy for semi-automatic implant delineation; and (ii) a machine learning approach for implant model recognition. Although the segmentation technique is the main focus of the current study, preliminary details of the machine learning approach are also reported. Two different scenarios are used to validate the framework: (1) comparison of the semi-automatic contours against implant’s manual contours of 125 X-ray images; and (2) classification of 11 known implants using a large reference database of 601 implants. Regarding experiment 1, 0.97±0.01, 2.24±0.85 pixels and 11.12±6 pixels of dice metric, mean absolute distance and Hausdorff distance were obtained, respectively. In experiment 2, 91% of the implants were successfully recognized while reducing the reference database to 5% of its original size. Overall, the segmentation technique achieved accurate implant contours. Although the preliminary classification results prove the concept of the current work, more features and an extended database should be used in a future work.