D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 49 Citations 10,461 349 World Ranking 3043 National Ranking 285

Research.com Recognitions

Awards & Achievements

2000 - IEEE Fellow For contributions to the theory and applications of visual signal processing and communications.

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Algorithm

King Ngi Ngan focuses on Artificial intelligence, Computer vision, Pattern recognition, Image segmentation and Image processing. Feature extraction, Data compression, Image quality, Discrete cosine transform and Transform coding are the primary areas of interest in his Artificial intelligence study. His Computer vision study frequently draws connections between related disciplines such as Boundary.

His biological study spans a wide range of topics, including Macroblock, Object detection and Histogram, Image. His Image segmentation study combines topics from a wide range of disciplines, such as Facial recognition system, Similarity and Edge detection. His study looks at the intersection of Image processing and topics like Thresholding with Depth of field and Bilateral filter.

His most cited work include:

  • Face segmentation using skin-color map in videophone applications (683 citations)
  • Unsupervised extraction of visual attention objects in color images (345 citations)
  • A Co-Saliency Model of Image Pairs (276 citations)

What are the main themes of his work throughout his whole career to date?

King Ngi Ngan spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Segmentation. Image segmentation, Image quality, Feature extraction, Image processing and Motion compensation are the subjects of his Artificial intelligence studies. His study in Image quality is interdisciplinary in nature, drawing from both Quantization and Metric.

Computer vision is often connected to Distortion in his work. His Pattern recognition research incorporates themes from Object detection, Image, Pascal and Feature. His Segmentation research is multidisciplinary, incorporating elements of Object and Pixel.

He most often published in these fields:

  • Artificial intelligence (73.74%)
  • Computer vision (57.99%)
  • Pattern recognition (28.08%)

What were the highlights of his more recent work (between 2014-2021)?

  • Artificial intelligence (73.74%)
  • Computer vision (57.99%)
  • Pattern recognition (28.08%)

In recent papers he was focusing on the following fields of study:

His primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Feature. His Distortion research extends to Artificial intelligence, which is thematically connected. When carried out as part of a general Computer vision research project, his work on Retargeting, Pixel and Face is frequently linked to work in Visibility and Generative model, therefore connecting diverse disciplines of study.

His Pattern recognition research includes themes of Pascal, Cluster analysis and Standard test image. As part of one scientific family, King Ngi Ngan deals mainly with the area of Segmentation, narrowing it down to issues related to the Object, and often Visualization, Function and Contrast. His study looks at the relationship between Feature and fields such as Representation, as well as how they intersect with chemical problems.

Between 2014 and 2021, his most popular works were:

  • Blind Image Quality Assessment Based on Multichannel Feature Fusion and Label Transfer (110 citations)
  • Fast HEVC Inter CU Decision Based on Latent SAD Estimation (57 citations)
  • An Efficient Frame-Content Based Intra Frame Rate Control for High Efficiency Video Coding (55 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Computer vision
  • Algorithm

His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Image quality and Distortion. His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Machine learning. Human visual system model, Discrete cosine transform, Video tracking, Quantization and Multiview Video Coding are the core of his Computer vision study.

His study on Discriminative model is often connected to Rank as part of broader study in Pattern recognition. His research integrates issues of Automatic image annotation, Digital image, Metric and Standard test image in his study of Image quality. His work carried out in the field of Distortion brings together such families of science as Retargeting, Motion estimation, Quarter-pixel motion, Random access and Sum of absolute differences.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Face segmentation using skin-color map in videophone applications

D. Chai;K.N. Ngan.
IEEE Transactions on Circuits and Systems for Video Technology (1999)

1170 Citations

Automatic segmentation of moving objects for video object plane generation

T. Meier;K.N. Ngan.
IEEE Transactions on Circuits and Systems for Video Technology (1998)

512 Citations

Unsupervised extraction of visual attention objects in color images

J. Han;K.N. Ngan;Mingjing Li;Hong-Jiang Zhang.
IEEE Transactions on Circuits and Systems for Video Technology (2006)

454 Citations

Video segmentation for content-based coding

T. Meier;K.N. Ngan.
IEEE Transactions on Circuits and Systems for Video Technology (1999)

390 Citations

Locating facial region of a head-and-shoulders color image

D. Chai;K.N. Ngan.
ieee international conference on automatic face and gesture recognition (1998)

357 Citations

Spatio-Temporal Just Noticeable Distortion Profile for Grey Scale Image/Video in DCT Domain

Zhenyu Wei;K.N. Ngan.
IEEE Transactions on Circuits and Systems for Video Technology (2009)

323 Citations

A Co-Saliency Model of Image Pairs

Hongliang Li;King Ngi Ngan.
IEEE Transactions on Image Processing (2011)

276 Citations

Admission control in IEEE 802.11e wireless LANs

Deyun Gao;Jianfei Cai;King Ngi Ngan.
IEEE Network (2005)

256 Citations

Image Quality Assessment by Separately Evaluating Detail Losses and Additive Impairments

Songnan Li;Fan Zhang;Lin Ma;King Ngi Ngan.
IEEE Transactions on Multimedia (2011)

177 Citations

Recent advances in rate control for video coding

Zhenzhong Chen;King Ngi Ngan.
Signal Processing-image Communication (2007)

175 Citations

Best Scientists Citing King Ngi Ngan

Weisi Lin

Weisi Lin

Nanyang Technological University

Publications: 84

Zhi Liu

Zhi Liu

Shanghai University

Publications: 55

Sam Kwong

Sam Kwong

City University of Hong Kong

Publications: 39

Hongliang Li

Hongliang Li

University of Electronic Science and Technology of China

Publications: 37

C.-C. Jay Kuo

C.-C. Jay Kuo

University of Southern California

Publications: 36

Wen Gao

Wen Gao

Peking University

Publications: 36

Junwei Han

Junwei Han

Northwestern Polytechnical University

Publications: 35

Siwei Ma

Siwei Ma

Peking University

Publications: 33

Guangming Shi

Guangming Shi

Xidian University

Publications: 33

Shiqi Wang

Shiqi Wang

City University of Hong Kong

Publications: 30

Alan C. Bovik

Alan C. Bovik

The University of Texas at Austin

Publications: 26

Ke Gu

Ke Gu

Beijing University of Technology

Publications: 25

Feng Wu

Feng Wu

University of Science and Technology of China

Publications: 23

Jianfei Cai

Jianfei Cai

Monash University

Publications: 22

Huazhu Fu

Huazhu Fu

Agency for Science, Technology and Research

Publications: 22

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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