A
Alireza Moini
Researcher at University of Adelaide
Publications - 23
Citations - 448
Alireza Moini is an academic researcher from University of Adelaide. The author has contributed to research in topics: Very-large-scale integration & Motion detection. The author has an hindex of 10, co-authored 22 publications receiving 448 citations.
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Journal ArticleDOI
Segmentation of the face and hands in sign language video sequences using color and motion cues
TL;DR: A hand and face segmentation methodology using color and motion cues for the content-based representation of sign language video sequences and derives a segmentation threshold for the classifier.
Journal ArticleDOI
An insect vision-based motion detection chip
Alireza Moini,Abdesselam Bouzerdoum,Kamran Eshraghian,Andre Yakovleff,Xuan Thong Nguyen,Andrew J. Blanksby,Richard Beare,Derek Abbott,R.E. Bogner +8 more
TL;DR: The architectural and circuit design aspects of a mixed analog/digital very large scale integration (VLSI) motion detection chip based on models of the insect visual system are described, implementing a reconfigurable architecture which facilitates the evaluation of several newly designed analog circuits.
Proceedings ArticleDOI
An analog implementation of early visual processing in insects
Alireza Moini,A. Bouzerdoum,Andre Yakovleff,Derek Abbott,O. Kim,Kamran Eshraghian,R.E. Bogner +6 more
TL;DR: An analog VLSI implementation which mimics the early visual processing stages in insects is described, which serves as the front end processor for a motion detection chip.
Proceedings ArticleDOI
Automatic thresholding for change detection in digital video
TL;DR: Results show that the thresholding technique for change detection in digital video is capable of detecting true motion in very images.
Proceedings ArticleDOI
A new VLSI smart sensor for collision avoidance inspired by insect vision
Derek Abbott,Alireza Moini,Andre Yakovleff,X. Thong Nguyen,Andrew J. Blanksby,Gyudong Kim,Abdesselam Bouzerdoum,R.E. Bogner,Kamran Eshraghian +8 more
TL;DR: An analog VLSI implementation of a smart microsensor that mimics the early visual processing stage in insects is described with an emphasis on the overall concept and the front- end detection.