Alireza Memarmoghadam

Saed Moradi, Alireza Memarmoghadam, Denis Laurendeau

This paper proposes a fast and accurate surface normal estimation method which can be directly used on depth maps (organized point clouds). The surface normal estimation process is formulated as a closed-form expression. In order to reduce the effect of measurement noise, the averaging operation is utilized in multi-direction manner. The multi-direction normal estimation process is reformulated in the next step to be implemented efficiently. Finally, a simple yet effective method is proposed to remove erroneous normal estimation at depth discontinuities. The proposed method is compared to well-known surface normal estimation algorithms. The results show that the proposed algorithm not only outperforms the baseline algorithms in term of accuracy, but also is fast enough to be used in real-time applications.

Saed Moradi, Alireza Memarmoghadam, Payman Moallem et al.

Infrared small target detection (IRSTD) poses a significant challenge in the field of computer vision. While substantial efforts have been made over the past two decades to improve the detection capabilities of IRSTD algorithms, there has been a lack of extensive investigation into the evaluation metrics used for assessing their performance. In this paper, we employ a systematic approach to address this issue by first evaluating the effectiveness of existing metrics and then proposing new metrics to overcome the limitations of conventional ones. To achieve this, we carefully analyze the necessary conditions for successful detection and identify the shortcomings of current evaluation metrics, including both pre-thresholding and post-thresholding metrics. We then introduce new metrics that are designed to align with the requirements of real-world systems. Furthermore, we utilize these newly proposed metrics to compare and evaluate the performance of five widely recognized small infrared target detection algorithms. The results demonstrate that the new metrics provide consistent and meaningful quantitative assessments, aligning with qualitative observations.