Dariush Amirkhani

Dariush Amirkhani, Junfeng Zhang

We propose a novel iterative process to establish the minimum separation between two ellipsoids. The method maintains one point on each surface and updates their locations in the theta-phi parametric space. The tension along the connecting segment between the two surface points serves as the guidance for the sliding direction, and the distance between them decreases gradually. The minimum distance is established when the connecting segment becomes perpendicular to the ellipsoid surfaces, at which point the net effect of the segment tension disappears and the surface points no longer move. Demonstration examples are carefully designed, and excellent numerical performance is observed, including accuracy, consistency, stability, and robustness. Furthermore, compared to other existing techniques, this surface-sliding approach has several attractive features, such as clear geometric representation, concise formulation, a simple algorithm, and the potential to be extended straightforwardly to other situations. This method is expected to be useful for future studies in computer graphics, engineering design, material modeling, and scientific simulations.

Loucif Hebbache, Dariush Amirkhani, Mohand Saïd Allili et al.

Anomaly object detection and classification are one of the main challenging tasks in computer vision and pattern recognition. In this paper, we propose a new method to automatically detect, localize and classify defects in concrete bridge structures using drone imagery. This framework is constituted of two main stages. The first stage uses saliency for defect region proposals where defects often exhibit local discontinuities in the normal surface patterns with regard to their surrounding. The second stage employs a YOLOX-based deep learning detector that operates on saliency-enhanced images obtained by applying bounding-box level brightness augmentation to salient defect regions. Experimental results on standard datasets confirm the performance of our framework and its suitability in terms of accuracy and computational efficiency, which give a huge potential to be implemented in a self-powered inspection system.