Abstract: The most important functions in objects classification and recognition system are to segment the objects from the input image, extract common features from the objects, and classify these objects as a member of one of the considered object classes. In this paper, we present a new approach for feature-based objects classification. The main idea of the new approach is the fusion of two different feature vectors that are calculated using Fourier descriptors and moment invariants. The fused moment-Fourier feature vector is invariant to image scaling, rotation, and translation. The fused feature vector for a reference object is used for training feed-forward neural network classifier. Classification of some hand tools is used to evaluate the performance of the proposed classification approach. The results show an appreciable increase in the classification accuracy rate with a considerable decrease in the classifier learning time.

Mostafa Ibrahim and Alaa Ahmed, “A Feature Fusion Approach for Hand Tools Classification” International Journal of Advanced Computer Science and Applications(IJACSA), 8(10), 2017. http://dx.doi.org/10.14569/IJACSA.2017.081013

@article{Ibrahim2017,
title = {A Feature Fusion Approach for Hand Tools Classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.081013},
url = {http://dx.doi.org/10.14569/IJACSA.2017.081013},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {10},
author = {Mostafa Ibrahim and Alaa Ahmed}
}