ImageYousef A. Atoum

Ph.D. Student
Computer Vision Lab
Department of Electrical and Computer Engineering
Michigan State University
Email: ,
Phone: 269-276-6791

About me

  • I am a U.S. citizen born in Fort Worth, Texas in 1987. I received my B.Sc. degree in Computer Engineering from Yarmouk University, Jordan in 2009. After that I came back to the U.S. to continue my education. I received my M.S. degree in Computer Engineering from Western Michigan University (WMU) in 2012. I have completed 4 years of my Ph.D journey at MSU under the supervision of my advisor Dr. Xiaoming Liu.

Research Interests

  • Computer Vision
  • Pattern Recognition
  • Image Processing
  • I'm particularly focused on building real-time systems to detect and track target objects. My main goal in such systems, is to achieve the correct balance between efficiency and accuracy in order to work well in practice.


  • Yousef Atoum, Joseph Roth, Michael Bliss, Wende Zhang, Xiaoming Liu. "Monocular Video-Based Trailer Coupler Detection using Multiplexer Convolutional Neural Network," In Proceeding of International Conference on Computer Vision (ICCV 2017), Venice, Italy, Oct. 2017.
  • Yousef Atoum, Yaojie Liu, Amin Jourabloo, and Xiaoming Liu. "Face Anti-Spoofing Using Patch and Depth-Based CNNs,"  In Proceeding of International Joint Conference on Biometrics (IJCB 2017), Denver, CO, Oct. 2017.
  • Seyed Morteza Safdarnejad, Yousef Atoum, Xiaoming Liu, “Temporally Robust Global Motion Compensation by Keypoint-based Congealing,” Proc. European Conference on Computer Vision, Amsterdam, The Netherlands, 2016.
  • Yousef Atoum, Liping Chen, Alex Liu, Stephen Hsu, Xiaoming Liu, “Automated Online Exam Proctoring,” IEEE Transaction on Multimedia, Dec 2015.
  • Yousef Atoum, Muhammad Jamal Afridi, Xiaoming Liu, J. Mitchell McGrath, Linda E. Hanson, “On Developing and Enhancing Plant-Level Disease Rating Systems in Real Fields,” Pattern Recognition, Vol. 53, pp. 287-299, May 2016. 
  • Yousef Atoum, Steven Srivastava, Xiaoming Liu, “Automatic Feeding Control for Dense Aquaculture Fish Tanks,” IEEE Signal Processing Letters, Vol. 22, No. 8, pp.1089-1093, Aug. 2015.



Automated Online Exam Proctoring  (Project Page)

In this project, we present a multimedia analytics system which performs automatic online exam proctoring. The system hardware includes one webcam, one wearcam, and a microphone, for the purpose of monitoring the visual and acoustic environment of the testing location. The system includes six basic components which continuously estimate the key behavior cues: user verification, text detection, voice detection, active window detection, gaze estimation and phone detection. By combining the continuous estimation components, and applying a temporal sliding window, we design higherlevel features to classify whether the test taker is cheating at any moment during the exam.oep

Rating CLS Disease in Real Fields (Project Page)

We propose a novel computer vision system, CLS Rater, to automatically and accurately rate plant images in the real field to the “USDA scale” of 0 to 10. Given a set of plant images captured by a tractor-mounted camera, CLS Rater extracts multi-scale superpixels, where in each scale a novel histogram of importances feature encodes both the within-superpixel local and across-superpixel global appearance variations. These features at different superpixel scales are then fused for learning a regressor that estimates the rating for each plant image. We further address the issue of the noisy labels by experts in the field, and propose a method to enhance the performance of the CLS Rater by automatically calibrating the experts ratings to ensure consistency.cls

Automatic Feeding Control for Dense Aquaculture Fish Tanks

This project introduces an efficient visual signal processing system to continuously control the feeding process of fish in aquaculture tanks. The aim is to improve the production profit in fish farms by controlling the amount of feed at an optimal rate. The automatic feeding control includes two components: 1) a continuous decision on whether the fish are actively consuming feed, and 2) automatic detection of the number of excess feed populated on the water surface of the tank using a two-stage approach. The amount of feed is initially detected using the correlation filer applied to an optimum local region within the video frame, and then followed by a SVM-based refinement classifier to suppress the falsely detected