- 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.
- 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.
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.
Liping Chen, Alex Liu, Stephen Hsu, Xiaoming Liu, “Automated Online Exam
Proctoring,” IEEE Transaction on Multimedia, Dec 2015.
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.
Steven Srivastava, Xiaoming Liu, “Automatic Feeding Control for Dense
Aquaculture Fish Tanks,” IEEE Signal Processing Letters, Vol. 22, No. 8, pp.1089-1093,
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.|
Rating CLS Disease in Real Fields (Project Page)
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
Automatic Feeding Control for Dense Aquaculture Fish Tanks
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 feed.|