Saed Moradi

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.

Jingru Zhang, Saed Moradi, Ashirbani Saha

Multi-task learning can suffer from destructive task interference, where jointly trained models underperform single-task baselines and limit generalization. To improve generalization performance in breast ultrasound-based tumor segmentation via multi-task learning, we propose a novel consistency regularization approach that mitigates destructive interference between segmentation and classification. The consistency regularization approach is composed of differentiable BI-RADS-inspired morphological features. We validated this approach by training all models on the BrEaST dataset (Poland) and evaluating them on three external datasets: UDIAT (Spain), BUSI (Egypt), and BUS-UCLM (Spain). Our comprehensive analysis demonstrates statistically significant (p<0.001) improvements in generalization for segmentation task of the proposed multi-task approach vs. the baseline one: UDIAT, BUSI, BUS-UCLM (Dice coefficient=0.81 vs 0.59, 0.66 vs 0.56, 0.69 vs 0.49, resp.). The proposed approach also achieves state-of-the-art segmentation performance under rigorous external validation on the UDIAT dataset.

Saed Moradi, Payman Moallem, Mohamad Farzan Sabahi

Infrared small target detection in an infrared search and track (IRST) system is a challenging task. This situation becomes more complicated when high gray-intensity structural backgrounds appear in the field of view (FoV) of the infrared seeker. While the majority of the infrared small target detection algorithms neglect directional information, in this paper, a directional approach is presented to suppress structural backgrounds and develop a more effective detection algorithm. To this end, a similar concept to the average absolute gray difference (AAGD) is utilized to construct a novel directional small target detection algorithm called absolute directional mean difference (ADMD). Also, an efficient implementation procedure is presented for the proposed algorithm. The proposed algorithm effectively enhances the target area and eliminates background clutter. Simulation results on real infrared images prove the significant effectiveness of the proposed algorithm.