The Determination Of Rotational Object Using Discriminant Feature Trace Transform Domain
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Abstract
This paper describes how to enhance the algorithm for determining the orientation of an object by predicting the orientation based on trace transform domain data. The image data trained by machine learning are transformed by the trace transform algorithm rather than being explicitly learned. The output of the trace transform algorithm is 2D data, which is then reduced to 1D data via the DFTF process. The 1D data is then further processed by machine learning. In the experiment, it was determined that the proposed system has three machine learning algorithms with the highest test accuracy from the database of water bottles and various produce databases. The accuracy of the following algorithms, Naïve Bay, Random Forest, and Support Vector Machine, is 98.99%, 95.63%, and 93.2%, respectively
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