C
C.-C. Jay Kuo
Researcher at University of Southern California
Publications - 1070
Citations - 20283
C.-C. Jay Kuo is an academic researcher from University of Southern California. The author has contributed to research in topics: Computer science & Wavelet. The author has an hindex of 59, co-authored 955 publications receiving 16671 citations. Previous affiliations of C.-C. Jay Kuo include Ningbo University & Beihang University.
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A Two-Stage Shape Retrieval (TSR) Method with Global and Local Features
TL;DR: In this paper, a robust two-stage shape retrieval (TSR) method is proposed to address the 2D shape retrieval problem, which decomposes the decision process into two stages.
Journal ArticleDOI
Complications associated with subaxial placement of pedicle screws versus lateral mass screws in the cervical spine (C2–T1): systematic review and meta-analysis comprising 4,165 patients and 16,669 screws
Mohamed A. Soliman,Alexander O. Aguirre,Slah Khan,C.-C. Jay Kuo,Nicco Ruggiero,Brandon L Mariotti,Alexander G. Fritz,Siddharth Sharma,Anxhela Nezha,Bennett R. Levy,Asham Khan,Amany A. Salem,Patrick K. Jowdy,Qazi Muhammad Zeeshan,Moleca Ghannam,Robert Starling,Kyungduk Rho,John Pollina,Jeffrey P. Mullin +18 more
Proceedings ArticleDOI
New PAR/NL scheme for stochastic texture interpolation
Byung Tae Oh,C.-C. Jay Kuo +1 more
TL;DR: The proposed PAR/NL scheme selects model parameters adaptively based on local image properties with an objective to improve the interpolation performance of nonadaptive models, e.g., the bicubic algorithm.
Proceedings ArticleDOI
Adaptive multimedia services provisioning in wireless communication networks
TL;DR: An adaptive system to support multimedia applications in a wireless network environment is investigated with an improved service model and a novel call admission scheme that enables more efficient radio channel usage.
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A Machine Learning Approach to Optimal Inverse Discrete Cosine Transform (IDCT) Design.
TL;DR: In this paper, the optimal inverse discrete cosine transform (IDCT) kernel was proposed to compensate the quantization error for effective lossy image compression in the presence of small quality factor.