We present a novel automatic supervised object recognition algorithm based on a scale and rotation invariant Fourier descriptors algorithm. The algorithm is hierarchical in nature to capture the inherent intra-contour spatial relationships between the parent and child contours of an object. A set of distance metrics are introduced to go along with the hierarchical model. To test the algorithm, a diverse database of shapes is created and used to train standard classification algorithms, for shape-labeling. The implemented algorithm takes advantage of the multi-threaded architecture and GPU efficient image-processing functions present in OpenCV wherever possible, speeding up the running time and making it efficient for use in real-time applications. The technique is successfully tested on common traffic and road signs of real-world images, with excellent overall performance that is robust to moderate noise levels.
M. K. Quweider, B. Arshad, H. Lei, L. Zhang and F. Khan, "An Accelerated Hierarchical Approach for Object Shape Extraction and Recognition," 2019 2nd International Conference on Data Intelligence and Security (ICDIS), South Padre Island, TX, USA, 2019, pp. 152-159, doi: 10.1109/ICDIS.2019.00030.
2019 2nd International Conference on Data Intelligence and Security (ICDIS)