2022 - Research.com Rising Star of Science Award
Yuming Fang mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Image quality. His studies in Visualization, Feature, Histogram, Seam carving and Human visual system model are all subfields of Artificial intelligence research. His biological study spans a wide range of topics, including Salient and Salience.
His Pattern recognition study integrates concerns from other disciplines, such as Image processing, Feature detection and Entropy. The Feature extraction study combines topics in areas such as Luminance, Support vector machine and Image texture. As a member of one scientific family, Yuming Fang mostly works in the field of Image quality, focusing on Local binary patterns and, on occasion, Normalization.
Yuming Fang focuses on Artificial intelligence, Computer vision, Pattern recognition, Image quality and Feature extraction. His work in the fields of Artificial intelligence, such as Image, Visualization, Feature and Human visual system model, overlaps with other areas such as Distortion. The various areas that Yuming Fang examines in his Computer vision study include Visual attention and Salience.
His Pattern recognition research includes themes of Histogram and Saliency map. His research in Image quality intersects with topics in Transform coding, Machine learning, Data mining and Image fusion. His work deals with themes such as Feature detection, Object detection, Kadir–Brady saliency detector and Image texture, which intersect with Feature extraction.
Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Image are his primary areas of study. Yuming Fang carries out multidisciplinary research, doing studies in Artificial intelligence and Distortion. His research in Computer vision is mostly concerned with View synthesis.
The concepts of his Pattern recognition study are interwoven with issues in Pixel and Feature. His Feature extraction study combines topics in areas such as Object detection, Anomaly detection and Human visual system model. His Image research incorporates elements of Algorithm, Compressed sensing, Chaotic and Light field.
Yuming Fang mainly focuses on Artificial intelligence, Feature extraction, Computer vision, Pattern recognition and Visualization. Yuming Fang incorporates Artificial intelligence and Distortion in his studies. Yuming Fang combines subjects such as RGB color model, Object detection and Benchmark with his study of Feature extraction.
His study in Computer vision is interdisciplinary in nature, drawing from both Scrambling and Compressed sensing. His Pattern recognition research is multidisciplinary, incorporating elements of Contrast, Noise, Image restoration and Norm. His Visualization study combines topics from a wide range of disciplines, such as Recurrent neural network, Feature, Pyramid and Salience.
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Saliency Detection in the Compressed Domain for Adaptive Image Retargeting
Yuming Fang;Zhenzhong Chen;Weisi Lin;Chia-Wen Lin.
IEEE Transactions on Image Processing (2012)
No-Reference Quality Assessment of Contrast-Distorted Images Based on Natural Scene Statistics
Yuming Fang;Kede Ma;Zhou Wang;Weisi Lin.
IEEE Signal Processing Letters (2015)
A Saliency Detection Model Using Low-Level Features Based on Wavelet Transform
N. Imamoglu;Weisi Lin;Yuming Fang.
IEEE Transactions on Multimedia (2013)
A Video Saliency Detection Model in Compressed Domain
Yuming Fang;Weisi Lin;Zhenzhong Chen;Chia-Ming Tsai.
IEEE Transactions on Circuits and Systems for Video Technology (2014)
Video Saliency Incorporating Spatiotemporal Cues and Uncertainty Weighting
Yuming Fang;Zhou Wang;Weisi Lin;Zhijun Fang.
IEEE Transactions on Image Processing (2014)
Saliency Detection for Stereoscopic Images
Yuming Fang;Junle Wang;Manish Narwaria;Patrick Le Callet.
IEEE Transactions on Image Processing (2014)
Perceptual Quality Assessment of Screen Content Images
Huan Yang;Yuming Fang;Weisi Lin.
IEEE Transactions on Image Processing (2015)
No-Reference Quality Assessment for Multiply-Distorted Images in Gradient Domain
Qiaohong Li;Weisi Lin;Yuming Fang.
IEEE Signal Processing Letters (2016)
Bottom-Up Saliency Detection Model Based on Human Visual Sensitivity and Amplitude Spectrum
Yuming Fang;Weisi Lin;Bu-Sung Lee;Chiew-Tong Lau.
IEEE Transactions on Multimedia (2012)
Saliency-Based Defect Detection in Industrial Images by Using Phase Spectrum
Xiaolong Bai;Yuming Fang;Weisi Lin;Lipo Wang.
IEEE Transactions on Industrial Informatics (2014)
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