A crucial component of artificial intelligence and image processing is 3D object classification. It helps to achieve significant and complex changes in performance through feature representation and processing of the images. Feature extraction plays a significant step in machine learning as it facilitates the feeding of insightful and non-redundant values to the machine learning algorithms. In this paper, we will present a framework to construct a 3D object classification system using several machine learning classifiers, and features were extracted using a local object structure descriptor called the 3D Voxel histogram of oriented gradient. We say that incorporating 3D classification tasks is a powerful strategy. This means enhancing the performance, precision, and efficiency of learning. The system contributed to increase efficiency and produced impressive results of 88 and 89% accuracy using Support vector machine and extreme Gradient Boosting, respectively. The results will be discussed and evaluated.
3D Object Classification Using HOG3D
Published in:
Springer
ISBN: