Geometrical and statistical feature extraction of images for rotation invariant classification systems based on industrial devices

In this work, the problem of recognition of objects using images extracted from a 3D industrial sensor is discussed. We focus in 7 feature extractors based on invariant moments and 2 based on independent component analysis, as well as on 3 classifiers (k-Nearest Neighbor, Support Vector Machine and Artificial Neural Network-Multi-Layer Perceptron). To choose the best feature extractor, their performance was compared in terms of classification accuracy rate and extraction time by the k-nearest neighbors classifier using euclidean distance. For what concerns the feature extraction, descriptors based on sorted-Independent Component Analysis and on Zernike moments performed better, leading to accuracy rates over 90.00 % and requiring relatively low time feature extraction (about half-second), whereas among the different classifiers used in the experiments, the suport vector machine outperformed when the Zernike moments were adopted as feature descriptor.