Prof. Tao Fang Profile

Prof. Tao Fang

Professor at Shanghai Jiao Tong Univ

SPIE Involvement:

Author

Publications (20)

SPIE Journal Paper | 30 November 2018 Open Access

Object tracking with mask-constrained spatial context (Publisher’s note)

Yiyou Guo, Hong Huo, Tao Fang

JEI, Vol. 27, Issue 06, 069801, (November 2018) https://doi.org/10.1117/12.10.1117/1.JEI.27.6.069801

KEYWORDS: Electronic imaging, Electrical engineering

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SPIE Journal Paper | 19 November 2018

Object tracking with mask-constrained spatial context

Yiyou Guo, Hong Huo, Tao Fang

JEI, Vol. 27, Issue 06, 063007, (November 2018) https://doi.org/10.1117/12.10.1117/1.JEI.27.6.063007

KEYWORDS: Image filtering, Electronic filtering, Video, Optical tracking, Detection and tracking algorithms, Visualization, Lead, Video surveillance, Statistical modeling, Automatic tracking

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SPIE Journal Paper | 19 November 2018

Object tracking with mask-constrained spatial context

Yiyou Guo, Hong Huo, Tao Fang

JEI, Vol. 27, Issue 06, 060901, (November 2018) https://doi.org/10.1117/12.10.1117/1.JEI.27.6.060901

KEYWORDS: Image filtering, Electronic filtering, Video, Optical tracking, Detection and tracking algorithms, Visualization, Lead, Video surveillance, Statistical modeling, Automatic tracking

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Proceedings Article | 9 August 2018 Paper

Checkerboard image processing under uneven illumination for robust Harris corner detection in camera calibration

J.-P. Liu, Z. Yang, H. Huo, T. Fang

Proceedings Volume 10806, 1080664 (2018) https://doi.org/10.1117/12.2503575

KEYWORDS: Corner detection, Calibration, Cameras, Image enhancement, Image processing, Reflection, Specular reflections

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SPIE Journal Paper | 3 August 2018

Camera calibration method with checkerboard pattern under complicated illumination

Jiapeng Liu, Zhengwu Yang, Hong Huo, Tao Fang

JEI, Vol. 27, Issue 04, 043038, (August 2018) https://doi.org/10.1117/12.10.1117/1.JEI.27.4.043038

KEYWORDS: Calibration, Cameras, Sensors, Corner detection, Distortion, Reconstruction algorithms, Optimization (mathematics), Light sources and illumination, 3D modeling, Reflectivity

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SPIE Journal Paper | 26 May 2018

Phase recovery approach based on high dynamic range imaging

Jiapeng Liu, Zhengwu Yang, Hong Huo, Tao Fang

OE, Vol. 57, Issue 05, 054118, (May 2018) https://doi.org/10.1117/12.10.1117/1.OE.57.5.054118

KEYWORDS: High dynamic range imaging, Phase shifts, Reflection, 3D modeling, Cameras, Image processing, Reconstruction algorithms, 3D printing, Optical engineering, Error analysis

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SPIE Journal Paper | 13 January 2018

Two-dimensional hidden semantic information model for target saliency detection and eyetracking identification

Weibing Wan, Lingfeng Yuan, Qunfei Zhao, Tao Fang

JEI, Vol. 27, Issue 01, 013006, (January 2018) https://doi.org/10.1117/12.10.1117/1.JEI.27.1.013006

KEYWORDS: Data modeling, Eye models, Eye, Target detection, Visualization, Visual process modeling, Data hiding, Associative arrays, Feature extraction, Performance modeling

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SPIE Journal Paper | 20 March 2017

Age and gender classification in the wild with unsupervised feature learning

Lihong Wan, Hong Huo, Tao Fang

JEI, Vol. 26, Issue 02, 023007, (March 2017) https://doi.org/10.1117/12.10.1117/1.JEI.26.2.023007

KEYWORDS: Biological research, Image classification, Databases, Convolution, Feature extraction, Image filtering, Principal component analysis, Spherical lenses, Network architectures, Image analysis

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SPIE Journal Paper | 1 February 2017

Local feature representation based on linear filtering with feature pooling and divisive normalization for remote sensing image classification

Lihong Wan, Na Liu, Yiyou Guo, Hong Huo, Tao Fang

JARS, Vol. 11, Issue 01, 016017, (February 2017) https://doi.org/10.1117/12.10.1117/1.JRS.11.016017

KEYWORDS: Linear filtering, Image classification, Remote sensing, Image filtering, Gaussian filters, Feature extraction, Nonlinear filtering, Computer programming, Principal component analysis, Image fusion

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SPIE Journal Paper | 11 October 2016

Seamless texture mapping algorithm for image-based three-dimensional reconstruction

Jiapeng Liu, Bin Liu, Tao Fang, Hong Huo, Yuming Zhao

JEI, Vol. 25, Issue 05, 053025, (October 2016) https://doi.org/10.1117/12.10.1117/1.JEI.25.5.053025

KEYWORDS: Volume rendering, Reconstruction algorithms, 3D image processing, 3D image reconstruction, Image fusion, Visibility, 3D modeling, Image processing, Image quality, Image segmentation

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SPIE Journal Paper | 23 April 2012

Shadow detection for color remotely sensed images based on multi-feature integration

Jiahang Liu, Tao Fang, Deren Li

JARS, Vol. 6, Issue 01, 063521, (April 2012) https://doi.org/10.1117/12.10.1117/1.JRS.6.063521

KEYWORDS: Phase modulation, Lithium, Roentgenium, Detection and tracking algorithms, RGB color model, Bandpass filters, Image segmentation, Statistical analysis, Image processing algorithms and systems, Tin

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SPIE Journal Paper | 1 October 2011

Vehicle detection algorithm based on light pairing and tracking at nighttime

Weibing Wan, Tao Fang, Shuguang Li

JEI, Vol. 20, Issue 04, 043008, (October 2011) https://doi.org/10.1117/12.10.1117/1.3663961

KEYWORDS: Headlamps, Detection and tracking algorithms, Roads, Cameras, Lithium, Video, Calibration, Reflection, Lamps, Control systems

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SPIE Journal Paper | 1 October 2010

Histogram of oriented gradient detector with color-invariant gradients in Gaussian color space

Zhen Lei, Tao Fang, Deren Li

OE, Vol. 49, Issue 10, 109701, (October 2010) https://doi.org/10.1117/12.10.1117/1.3503944

KEYWORDS: RGB color model, Sensors, Optical engineering, Image processing, Image segmentation, Light, Video, Sensor performance, Reflectivity, Remote sensing

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SPIE Journal Paper | 1 May 2010 Open Access

Color conspicuity map based on wavelet low-pass pyramid for popping out contours of salient objects

Zhiqiang Li, Tao Fang, Hong Huo, Julian Zhu

OE, Vol. 49, Issue 05, 050502, (May 2010) https://doi.org/10.1117/12.10.1117/1.3425655

KEYWORDS: Wavelets, Radon, Image segmentation, Image processing, Visual process modeling, Roentgenium, Image processing algorithms and systems, Optical engineering, Analytical research, Lithium

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SPIE Journal Paper | 1 October 2009

Supervised classification of multispectral remote sensing images based on the nearest reduced convex hull approach

Jianjun Qing, Hong Huo, Tao Fang

JARS, Vol. 3, Issue 01, 033550, (October 2009) https://doi.org/10.1117/12.10.1117/1.3253613

KEYWORDS: Remote sensing, Prototyping, Image classification, Multispectral imaging, Vegetation, Spatial resolution, Image segmentation, Neural networks, Binary data, Feature extraction

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Proceedings Article | 28 October 2006 Paper

A new kind of tuple for spatial data mining

Fuquan Wang, Qingxian Sun, Tao Fang, Dazhi Guo

Proceedings Volume 6420, 642001 (2006) https://doi.org/10.1117/12.712598

KEYWORDS: Data mining, Mining, Remote sensing, Databases, Image classification, Visualization, Safety, Knowledge discovery, Fuzzy logic, Geographic information systems

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Proceedings Article | 19 May 2006 Paper

Hyperspectral remote sensing image retrieval based on spectral similarity measure

Peijun Du, Yunhao Chen, Hong Tang, Tao Fang

Proceedings Volume 6199, 61990I (2006) https://doi.org/10.1117/12.673667

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Spectral similarity measure plays important roles in hyperspectral Remote Sensing (RS) information processing, and it can be used to content-based hyperspectral RSimage retrieval effectively too. The applications of spectral features to Remote Sensing (RS) image retrieval are discussed by taking hyperspectral RS image as examples oriented to the demands of massive information management. It is proposed that spectral features-based image retrieval includes two modes: retrieval based on point template and facial template. Point template is used usually, for example, a spectral curve, or a pixel vector in hyperspectral RS image. One or more regions (or blocks with area shape) are given as examples in image retrieval based on facial template. The most important issues in image retrieval are spectral features extraction and spectral similarity measure. Spectral vector can be used to retrieval directly, and spectral angle and spectral information divergence (SID) are more effective than Euclidean distance and correlation coefficient in similarity measure and image retrieval. Both point and pure area template can be transformed into spectral vector and used to spectral similarity measure. In addition, the local maximum and minimum in reflection spectral curve, corresponding to reflection peak and absorption valley, can be used to retrieval also. The width, height, symmetry and power of each peak or valley can be used to encode spectral features. By comparison to three approaches for spectral absorption and reflection features matching and similarity measures, it is found that spectral absorption and reflection features are not very effective in hyperspectral RS image retrieval. Finally, a prototype system is designed, and it proves that the hyperspectral RS image retrieval based on spectral similarity measure proposed in this paper is effective and some similarity measure index including spectral angle, SID and encoding measure are suitable for image retrieval in practice.

Proceedings Article | 5 January 2006 Paper

A framework for the uncertain spatial data mining

Binbin He, Tao Fang, Dazhi Guo

Proceedings Volume 5985, 59853Z (2006) https://doi.org/10.1117/12.658220

KEYWORDS: Data mining, Monte Carlo methods, Expectation maximization algorithms, Mining, Computer simulations, Databases, Data modeling, Fuzzy logic, Information science, Information technology

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Proceedings Article | 4 November 2005 Paper

Image feature ratio model: similarity measures with both semantic and visual features in interactive image retrieval

Hong Tang, Tao Fang, Peng-Fei Shi

Proceedings Volume 6044, 604407 (2005) https://doi.org/10.1117/12.652287

KEYWORDS: Visualization, Visual process modeling, Image retrieval, Fuzzy logic, Feature extraction, Binary data, Databases, Systems modeling, Diagnostics, Visual analytics

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Proceedings Article | 31 July 2002 Paper

Information characteristics and band combination of Landsat TM RS image used to terrestrial surface evolution in mining area

Pei-jun DU, Tao Fang, Da-zhi GUO, Pengfei Shi

Proceedings Volume 4875, (2002) https://doi.org/10.1117/12.477154

KEYWORDS: Mining, Remote sensing, Landsat, Genetic algorithms, Statistical analysis, Environmental monitoring, Satellite imaging, Satellites, Principal component analysis, Earth observing sensors

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Showing 5 of 20 publications

Conference Committee Involvement (1)

International Conference on Earth Observation Data Processing and Analysis

28 December 2008 | Wuhan, China

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