![]() ![]() ![]() The algorithm was then extended to a client-server architecture using SIFT and SURF features reduced by Bag of Words and high correlation-based HOG vectors. In our second research problem, we developed a mobile app for banknote identification and counterfeit detection using the Unity 3D software and evaluated its performance based on a Cascaded Ensemble approach. We further experimented on computing HOG descriptors from cells created from image patch vertices of SURF points and designed a feature reduction approach based on a high correlation and low variance filter. In our first research problem, we proposed a new banknote recognition approach that classifies the principal components of extracted HOG features. Thus, this thesis is organized around three such problems related to Banknote Authentication and Medical Image Diagnosis. Computer-aided diagnosis is vital to improvements in medical analysis, as they facilitate the identification of findings that need treatment and assist the expert’s workflow. But image analysis by humans is susceptible to error due to wide variations across interpreters, lethargy, and human subjectivity. Similarly, many physicians must interpret medical images. As counterfeiters have taken advantage of the innovation in print media technologies for reproducing fake monies, hence the need to design systems which can reassure and protect citizens of the authenticity of banknotes in circulation. Banknote recognition and medical image analysis have been the foci of image processing and pattern recognition research. ![]()
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