COMPUTER VISION BASED MODEL FOR ART SKILLS ASSESSMENT
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- NGN 3000
ABSTRACT
Drawing is assumed to be a talent in people, which makes drawing a natural process. Based on the scenario in consideration, there are times when it is necessary to assess drawing skills. In this thesis, I intend to develop an algorithm that will measure the skill of drawing by matching the hand-drawn image with the original template. The techniques that are already available make use of a complicated process. Notably, computers can be trained to identify the match at a human level which will resolve the tedious and overwhelming traditional process. Image similarity involves identifying the level of similarities in an image using a reference image; computer vision applications are used. The SIFT method and Siamese Network are analyzed and implemented to measure image similarity. The results show that it is possible to measure the skill level of an art. Via the analysis of the features, SIFT-based implementation was able to detect better than VGG16.
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