A high performance neural network and fuzzy logic based edge corner detector (NF-ECD)

Abstract

In recent years, Keypoints-based image detection algorithms have become essential to many image processing applications. They should be stable and invariant especially against image distortion and noise caused by different illumination conditions. Thus, the challenge is to design a faster and more robust detector in terms of accuracy and saliency of the detected keypoints. Toward this objective, the flexibility of artificial intelligence (AI) and its ability to learn and adapt has made it the primary choice to achieve this goal. In this paper, we propose a novel detector that combines the power of neural networks to detect robust feature points and fuzzy logic to select among them only the most significant. A neural network is implemented as a supervised machine learning technique. It is trained on a predefined database of straight edges (SEs) with different patterns representing a set of flow directions. The aim is to decompose a given contour into a set of connected straight edges (SEs) and estimate the flow direction for each. The transition points between nonlinear SEs are classified as edge corners (ECs). Finally, the set of these ECs is pruned by a fuzzy logic system to keep only the significant ones based on key corner parameters that can highly contribute in the matching process. Experimental results demonstrate clearly the robustness and saliency of our newly proposed NF-ECD in extracting keypoints. In addition, the NF-ECD achieves the best performance as compared to the state of the art keypoints detection algorithms. Using experiments conducted on the illumination set of the HPatches dataset, the repeatability score reaches 72.6%. On the other hand, the average computational time complexity obtained using the Object Recognition Dataset reaches 2.18 s which is the lowest among other similar detectors. In addition, NF-ECD shows an effective reduction in the matching runtime.