Z
Zhijun Fang
Researcher at Shanghai University of Engineering Sciences
Publications - 110
Citations - 2599
Zhijun Fang is an academic researcher from Shanghai University of Engineering Sciences. The author has contributed to research in topics: Computer science & Feature extraction. The author has an hindex of 20, co-authored 94 publications receiving 1764 citations. Previous affiliations of Zhijun Fang include Shanghai University & Jiangxi University of Finance and Economics.
Papers
More filters
Journal ArticleDOI
No-Reference Quality Assessment of Contrast-Distorted Images Based on Natural Scene Statistics
TL;DR: A simple but effective method for no-reference quality assessment of contrast distorted images based on the principle of natural scene statistics (NSS), which demonstrates the promising performance of the proposed method based on three publicly available databases.
Journal ArticleDOI
Sliding Mode Control of Fuzzy Singularly Perturbed Systems With Application to Electric Circuit
TL;DR: A novel integral-type fuzzy switching surface function is put forward, which contains singular perturbation matrix and state-dependent input matrix simultaneously in a transformed fuzzy SPSs such that the matched uncertainty/perturbation is completely compensated without amplifying the unmatched one.
Journal ArticleDOI
Video Saliency Incorporating Spatiotemporal Cues and Uncertainty Weighting
TL;DR: Experimental results show that the proposed method significantly outperforms state-of-the-art video saliency detection models.
Journal ArticleDOI
Fuzzy-Model-Based Sliding Mode Control of Nonlinear Descriptor Systems
TL;DR: This paper addresses the problem of sliding mode control (SMC) for a type of uncertain time-delay nonlinear descriptor systems represented by T–S fuzzy models by resorting to Frobenius’ theorem and double orthogonal complement and presenting the existence condition of the fuzzy manifold.
Journal ArticleDOI
Objective Quality Assessment for Image Retargeting Based on Structural Similarity
TL;DR: The experimental results show that IR-SSIM is better correlated with subjective evaluations than existing methods in the literature and embeds it into a multi-operator image retargeting process, which generates visually appealing retargeted results.