J
Jie Yao
Researcher at Concordia University
Publications - 8
Citations - 316
Jie Yao is an academic researcher from Concordia University. The author has contributed to research in topics: Genetic algorithm & Randomized Hough transform. The author has an hindex of 7, co-authored 8 publications receiving 288 citations. Previous affiliations of Jie Yao include Concordia University Wisconsin.
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Journal ArticleDOI
Bi-Objective Multipopulation Genetic Algorithm for Multimodal Function Optimization
TL;DR: The results of the empirical comparison provide clear evidence that supports the conclusion that BMPGA is better than the other GAs in terms of overall effectiveness, applicability, and reliability.
Journal ArticleDOI
A multi-population genetic algorithm for robust and fast ellipse detection
TL;DR: In thorough and fair experimental tests, MPGA exhibits solid advantages over RHT and SGA in terms of accuracy of recognition—even in the presence of noise or/and multiple imperfect ellipses in an image—and speed of computation.
Journal ArticleDOI
Automatic segmentation of cells from microscopic imagery using ellipse detection
Nawwaf Kharma,Hussein Moghnieh,Jie Yao,Y. P. Guo,A. Abu-Baker,J. Laganiere,Guy A. Rouleau,Mohamed Cheriet +7 more
TL;DR: A new method has been added, which automatically extracts cells from microscopic imagery, and does so in two phases, which uses a novel genetic algorithms-based ellipse detection algorithm to identify cells, quickly and reliably.
Proceedings ArticleDOI
Fast robust GA-based ellipse detection
TL;DR: In thorough and fair experimental tests, utilizing both synthetic and real-world images, MPGA exhibits solid advantages over RHT and SGA in terms of accuracy of recognition - even in the presence of noise or/and multiple imperfect ellipses, as well as speed of computation.
Proceedings ArticleDOI
BMPGA: a bi-objective multi-population genetic algorithm for multi-modal function optimization
TL;DR: This paper introduces two innovations into the world of multi-modal function optimization: a new multi-population genetic algorithm (GA), with two complementary fitness terms (called BMPGA); and a new similarity function that is used to decide whether two points belong to the same cluster or not, called recursive middling (RM).