Dr. Chong-Yang Zhang Profile

Dr. Chong-Yang Zhang

at Shanghai Jiao Tong Univ

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Publications (3)

Proceedings Article | 28 September 2016 Presentation + Paper

Global velocity constrained cloud motion prediction for short-term solar forecasting

Yanjun Chen, Wei Li, Chongyang Zhang, Chuanping Hu

Proceedings Volume 9971, 99711U (2016) https://doi.org/10.1117/12.2239468

KEYWORDS: Solar energy, Clouds, Motion estimation, Clouds, Solar energy, Particles, Motion estimation, Image processing, Digital filtering, Sun, Roads, Algorithm development, Communication engineering

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Proceedings Article | 23 September 2014 Paper

Learning to predict where human gaze is using quaternion DCT based regional saliency detection

Ting Li, Yi Xu, Chongyang Zhang

Proceedings Volume 9217, 92171K (2014) https://doi.org/10.1117/12.2063974

KEYWORDS: Visualization, Image segmentation, Fourier transforms, Optimization (mathematics), Visual process modeling, Technetium, Binary data, Digital image processing, Spectral models, Detection and tracking algorithms

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Many current visual attention approaches used semantic features to accurately capture human gaze. However, these approaches demand high computational cost and can hardly be applied to daily use. Recently, some quaternion-based saliency detection models, such as PQFT (phase spectrum of Quaternion Fourier Transform), QDCT (Quaternion Discrete Cosine Transform), have been proposed to meet real-time requirement of human gaze tracking tasks. However, current saliency detection methods used global PQFT and QDCT to locate jump edges of the input, which can hardly detect the object boundaries accurately. To address the problem, we improved QDCT-based saliency detection model by introducing superpixel-wised regional saliency detection mechanism. The local smoothness of saliency value distribution is emphasized to distinguish noises of background from salient regions. Our algorithm called saliency confidence can distinguish the patches belonging to the salient object and those of the background. It decides whether the image patches belong to the same region. When an image patch belongs to a region consisting of other salient patches, this patch should be salient as well. Therefore, we use saliency confidence map to get background weight and foreground weight to do the optimization on saliency map obtained by QDCT. The optimization is accomplished by least square method. The optimization approach we proposed unifies local and global saliency by combination of QDCT and measuring the similarity between each image superpixel. We evaluate our model on four commonly-used datasets (Toronto, MIT, OSIE and ASD) using standard precision-recall curves (PR curves), the mean absolute error (MAE) and area under curve (AUC) measures. In comparison with most state-of-art models, our approach can achieve higher consistency with human perception without training. It can get accurate human gaze even in cluttered background. Furthermore, it achieves better compromise between speed and accuracy.

SPIE Journal Paper | 6 July 2012

Causality-weighted active learning for abnormal event identification based on the topic model

Yawen Fan, Shibao Zheng, Hua Yang, Chongyang Zhang, Hang Su

OE, Vol. 51, Issue 7, 077204, (July 2012) https://doi.org/10.1117/12.10.1117/1.OE.51.7.077204

KEYWORDS: Statistical modeling, Data modeling, Video, Visualization, Performance modeling, Optical engineering, Machine learning, Video surveillance, Visual process modeling, Fluctuations and noise

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