Discriminant Feature Trace Transform for Predictive Object Rotation
International journal of intelligent engineering and systems · 2022
This paper presents how to modify the proposed Discriminant Feature Trace Transform (DFTF) algorithm for object rotation.This proposed DFTF algorithm based on the Trace Transform (TF) domain can collect data for the formation of distinctive features and is suitable for predicting the rotational direction of an object with the supervised machine learning.TF domain is used after removing the background image that transformation properties can be generated a feature referred to the rotation features of objects inside.After that, the proposed DFTF algorithm transforms the two-dimensional data of each image from the TF domain into the one-dimensional data that can indicate the direction of object rotation within the image.The results of the DFTF algorithm, which is a 1D data vector from each image, are then generated into the labeled datasets for machine learning algorithms including with the Naïve Bayes (NB) for predicting the rotation direction and Random Forest (RF) to reinforce the predicted values from the NB in the form of quadrant in which the interested object is rotated.The simulation experiments are conducted with two types of databases.The first experiment combines with the three different datasets which the results are very effective and can provide the average accuracy rate for all databases up to 99.8%.The second experiment is derived from the visualization of the water bottle production line.It measures the accuracy of the bottled water transfer to the second station.The proposed approach is an accuracy rate up to 92.2%.