no code implementations • 25 Jan 2024 • Aidan Blair, Amirali Khodadadian Gostar, Alireza Bab-Hadiashar, XiaoDong Li, Reza Hoseinnezhad
Tracking multiple targets in dynamic environments using distributed sensor networks is a challenging problem that has received significant attention in recent years.
no code implementations • 12 Jul 2023 • WeiQin Chuah, Ruwan Tennakoon, Reza Hoseinnezhad, David Suter, Alireza Bab-Hadiashar
This approach is invariant to both affine shifts and changes in energy within a local feature patch and eliminates the need for commonly used non-linear activation functions.
no code implementations • 24 Oct 2022 • Shima Rashidi, Ruwan Tennakoon, Aref Miri Rekavandi, Papangkorn Jessadatavornwong, Amanda Freis, Garret Huff, Mark Easton, Adrian Mouritz, Reza Hoseinnezhad, Alireza Bab-Hadiashar
Distribution shift between train (source) and test (target) datasets is a common problem encountered in machine learning applications.
no code implementations • 19 Apr 2022 • Nida Ishtiaq, Amirali Khodadadian Gostar, Alireza Bab-Hadiashar, Reza Hoseinnezhad
But in many real-world applications, target objects interact with one another and the environment.
no code implementations • CVPR 2022 • WeiQin Chuah, Ruwan Tennakoon, Reza Hoseinnezhad, Alireza Bab-Hadiashar, David Suter
State-of-the-art stereo matching networks trained only on synthetic data often fail to generalize to more challenging real data domains.
1 code implementation • 27 Aug 2021 • Ammar Mansoor Kamoona, Amirali Khodadadian Gostar, Alireza Bab-Hadiashar, Reza Hoseinnezhad
Experimental results show the outstanding performance of our proposed approach compared to the state-of-the-art methods, and the proposed RFS energy outperforms the state-of-the-art in the few shot learning settings.
Ranked #63 on Anomaly Detection on MVTec AD
no code implementations • 21 Jun 2021 • Ayman Mukhaimar, Ruwan Tennakoon, Chow Yin Lai, Reza Hoseinnezhad, AlirezaBab-Hadiashar
The proposed pooling layer looks for data a mode/cluster using two methods, RANSAC, and histogram, as clusters are indicative of models.
no code implementations • 3 Feb 2021 • Ammar Mansoor Kamoona, Amirali Khodadadian Gostar, Alireza Bab-Hadiashar, Reza Hoseinnezhad
The results show that using point pattern features, such as SIFT as data points for random finite set-based anomaly detection achieves the most consistent defect detection accuracy on the MVTec-AD dataset.
no code implementations • 10 Sep 2020 • WeiQin Chuah, Ruwan Tennakoon, Reza Hoseinnezhad, Alireza Bab-Hadiashar, David Suter
Consequently, the learning algorithms often produce unreliable depth estimates of foreground objects, particularly at large distances~($>50$m).
no code implementations • 2 Sep 2020 • Ayman Mukhaimar, Ruwan Tennakoon, Chow Yin Lai, Reza Hoseinnezhad, Alireza Bab-Hadiashar
In this paper, we present a robust spherical harmonics approach for the classification of point cloud-based objects.
1 code implementation • 3 Jul 2020 • Ammar Mansoor Kamoona, Amirali Khodadadian Gosta, Alireza Bab-Hadiashar, Reza Hoseinnezhad
The proposed approach uses both abnormal and normal video clips during the training phase which is developed in the multiple instance framework where we treat video as a bag and video clips as instances in the bag.
Anomaly Detection In Surveillance Videos Multiple Instance Learning +1
1 code implementation • 26 May 2017 • Ruwan Tennakoon, Alireza Sadri, Reza Hoseinnezhad, Alireza Bab-Hadiashar
In this paper, we propose an effective sampling method to obtain a highly accurate approximation of the full graph required to solve multi-structural model fitting problems in computer vision.
no code implementations • 20 Apr 2016 • Tharindu Rathnayake, Reza Hoseinnezhad, Ruwan Tennakoon, Alireza Bab-Hadiashar
This paper presents a track-before-detect labeled multi-Bernoulli filter tailored for industrial mobile platform safety applications.