Dr. Yufeng Zheng Profile

Dr. Yufeng Zheng

Associate Professor at Univ of Mississippi Medical Ctr

SPIE Involvement:

Conference Program Committee | Author | Editor | Instructor | Science Fair Judge

Area of Expertise:

Bio-inspired Image analysis , Pattern recognition , Biometrics (face recognition) , Computer-aided (cancer) detection , Multispectral image fusion and colorization , Deep learning convolutional neural network

Websites:

Personal Website | Company Website

Profile Summary

Yufeng Zheng is an associate professor of data science in the University of Mississippi Medical Center (Jackson, MS). He received his Ph.D. degree in Optical Engineering/Image Processing from the Tianjin University (Tianjin, China) in 1997. He was a postdoctoral research associate at the University of Louisville (Kentucky) from 2001-2005. Dr. Zheng is the principal investigator of many funded projects such as cybersecurity enhancement with keyboard dynamics, canopy coverage estimation with neural network, multisensory image fusion and colorization; thermal face recognition; and multispectral face recognition. Dr. Zheng holds a utility patents in face recognition, and has edited three books, published six book chapters and more than 80 scientific papers. His research interests focus on bio-inspired image analysis, deep learning convolutional neural network, biometrics, and computer-aided diagnosis. Dr. Zheng is a Cisco Certified Network Professional (CCNP).

Publications (55)

Proceedings Article | 7 June 2024 Presentation + Paper

Presentation + Paper

Digital twin meets information fusion: panel summary

Erik Blasch, Genshe Chen, Yu Chen, Yufeng Zheng, Lynne Grewe, Ivan Kadar, Andreas Savakis

Proceedings Volume 13057, 130570F (2024) https://doi.org/10.1117/12.3014996

KEYWORDS: Data modeling, Systems modeling, Information fusion, Sensors, Instrument modeling, Design, Data fusion, Medicine, Engineering, Aerospace engineering

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Proceedings Article | 7 June 2024 Presentation + Paper

Presentation + Paper

Heart rate and oxygen level estimation from facial videos using a hybrid deep learning model

Yufeng Zheng

Proceedings Volume 13033, 1303307 (2024) https://doi.org/10.1117/12.3013956

KEYWORDS: Vital signs, Deep learning, Transformers

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Proceedings Article | 14 June 2023 Presentation + Paper

Presentation + Paper

Facial micro-expression recognition using deep spatio-temporal neural networks

Yufeng Zheng, Erik Blasch

Proceedings Volume 12547, 125470V (2023) https://doi.org/10.1117/12.2665189

KEYWORDS: Data modeling, Visual process modeling, Transformers, Emotion, Neural networks, Facial recognition systems, Convolution

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Proceedings Article | 8 June 2022 Presentation + Paper

Presentation + Paper

Joint data learning panel summary

Erik Blasch, Andreas Savakis, Yufeng Zheng, Genshe Chen, Ivan Kadar, Uttam Majumder, Ali Raz

Proceedings Volume 12122, 121220K (2022) https://doi.org/10.1117/12.2619537

KEYWORDS: Synthetic aperture radar, Data modeling, Data fusion, Systems engineering, Information fusion, Systems modeling, Sensors, Image fusion, Target detection, Social networks

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Proceedings Article | 1 June 2022 Presentation + Paper

Presentation + Paper

Vineyard LAI and canopy coverage estimation with convolutional neural network models and drone pictures

Yufeng Zheng, Erol Sarigul, Girish Panicker, Diane Stott

Proceedings Volume 12120, 1212006 (2022) https://doi.org/10.1117/12.2620100

KEYWORDS: Transformers, Convolutional neural networks, Neural networks

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Proceedings Article | 27 May 2022 Presentation + Paper

Presentation + Paper

Advances in deep learning for infrared image processing and exploitation

Erik Blasch, Zheng Liu, Yufeng Zheng

Proceedings Volume 12107, 121071M (2022) https://doi.org/10.1117/12.2619140

KEYWORDS: Infrared imaging, Thermography, Image fusion, Infrared sensors, Sensors, Infrared radiation, Nondestructive evaluation, Image enhancement, Visualization, Inspection

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Proceedings Article | 27 May 2022 Presentation + Paper

Presentation + Paper

Improving cybersecurity through deep learning on keystroke and mouse dynamics

Yufeng Zheng, Lixin Yu, Sardar Haque, Ping Zhang, Adel Elmaghraby

Proceedings Volume 12100, 1210005 (2022) https://doi.org/10.1117/12.2620104

KEYWORDS: Biometrics, Behavioral biometrics, Distance measurement, Neural networks, Data modeling, Mahalanobis distance

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Proceedings Article | 27 May 2022 Paper

Paper

Adolescent behavioral risk analysis and prediction using machine learning: a foundation for precision suicide prevention

Yufeng Zheng, Brian Christman, Matthew Morris, William Hillegass, Yunxi Zhang, Kimberly Douglas, Chris Kelly, Lei Zhang

Proceedings Volume 12100, 121000Q (2022) https://doi.org/10.1117/12.2620105

KEYWORDS: Machine learning, Neural networks, Statistical analysis, Feature selection

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Proceedings Article | 12 April 2021 Presentation + Paper

Presentation + Paper

Predicting vineyard canopy coverage using drone pictures

Yufeng Zheng, Girish Panicker, Diane Stott

Proceedings Volume 11747, 1174706 (2021) https://doi.org/10.1117/12.2585806

KEYWORDS: Spherical lenses, Photography, Neural networks, Image analysis, Digital cameras

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Proceedings Article | 12 April 2021 Presentation + Paper

Presentation + Paper

Cybersecurity enhancement using recurrent neural networks and keystroke dynamics

Adel Elmaghraby, Yufeng Zheng

Proceedings Volume 11734, 117340D (2021) https://doi.org/10.1117/12.2585803

KEYWORDS: Behavioral biometrics, Convolutional neural networks, Biometrics

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Proceedings Article | 19 May 2020 Presentation + Paper

Presentation + Paper

Image fusion for context-aided automatic target recognition

Erik Blasch, Zheng Liu, Yufeng Zheng

Proceedings Volume 11394, 113940U (2020) https://doi.org/10.1117/12.2564876

KEYWORDS: Data modeling, Automatic target recognition, Image fusion, Systems modeling, Sensors, Information fusion, Unmanned aerial vehicles, Statistical analysis, Data communications, Machine learning

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Proceedings Article | 21 April 2020 Presentation + Paper

Presentation + Paper

Enhancing breast cancer detection with recurrent neural network

Yufeng Zheng, Clifford Yang, Hongyu Wang

Proceedings Volume 11399, 113990C (2020) https://doi.org/10.1117/12.2558817

KEYWORDS: Mammography, Feature extraction, Breast cancer, Computer aided diagnosis and therapy, Neural networks

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Proceedings Article | 21 April 2020 Open Access

Open Access

Presentation + Paper

Mobile application for monitoring body temperature from facial images using convolutional neural network and support vector machine

Yufeng Zheng, Hongyu Wang, Yingguang Hao

Proceedings Volume 11399, 113990B (2020) https://doi.org/10.1117/12.2557856

KEYWORDS: Body temperature, Data modeling, Convolutional neural networks, Facial recognition systems, Feature extraction, Image classification

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Proceedings Article | 21 April 2020 Presentation + Paper

Presentation + Paper

CNN study of convolutional neural networks in classification and feature extraction applications

Yufeng Zheng, Hongyu Wang, Yingguang Hao

Proceedings Volume 11395, 113950K (2020) https://doi.org/10.1117/12.2560372

KEYWORDS: Feature extraction, Image classification, Data modeling, Convolutional neural networks, Visual process modeling

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Proceedings Article | 14 May 2019 Presentation + Paper

Presentation + Paper

Multisource deep learning for situation awareness

Erik Blasch, Zheng Liu, Yufeng Zheng, Uttam Majumder, Alex Aved, Peter Zulch

Proceedings Volume 10988, 109880M (2019) https://doi.org/10.1117/12.2519236

KEYWORDS: Image fusion, Molybdenum, Information fusion, Data modeling, Image processing, Mid-IR, Data fusion, Sensors, Infrared imaging, Systems modeling

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Proceedings Article | 14 May 2019 Presentation + Paper

Presentation + Paper

CNN classification based on global and local features

Yufeng Zheng, Jun Huang, Tianwen Chen, Yang Ou, Wu Zhou

Proceedings Volume 10996, 109960G (2019) https://doi.org/10.1117/12.2519660

KEYWORDS: Image classification, Visual process modeling, Convolutional neural networks

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In this study, we investigated how shapes are classified based on local and global features by four representative convolutional neural networks (CNNs), i.e., AlexNet, VGG, ResNet and Inception. While the local features are based on simple components, such as orientation of line segment, the global features are based on the whole object, such as whether an object has a hole. For example, solid triangles and solid squares are differentiated by local features, solid circles and rings are differentiated by a global feature. Two sets of experiments were performed in this research. In the first experiment, we examined how the four CNNs pre-trained on ImageNet (with transfer learning) learned to differentiate the regular shapes (equilateral-triangles, squares, circles and rings). Our results showed that the pre-trained CNNs exhibited faster learning rates in the tasks discriminating the local features than in the tasks discriminating the global feature. However, the transfer learning of discriminating the global feature in regular shapes were better generalized to irregular shapes than the transfer learning of discriminating local features. In the second experiment, the CNNs were trained from scratch (with random weights initialization) to discriminate local and global features in regular and irregular shapes. Different from the transfer learning, the CNNs exhibited faster learning rates in discriminating the global feature than the local features. Similar to transfer learning, the CNNs exhibited excellent learning generalization to discriminating the global feature of irregular shapes, but poor learning generalization to discriminating the local features in the irregular shapes. The overarching goal of this research is to create a paradigm and benchmark to directly compare how the CNNs and primate visual systems process geometrical invariants. In contrast to the ImageNet approach which employs natural images to train CNNs, we employed the “ShapeNet” approach which features geometrical shapes with well-defined properties. The ShapeNet approach will not only help elucidate the strengths and limitations of CNN computation, but also provide insights into visual information processing in the primates.

Proceedings Article | 14 May 2018 Presentation + Paper

Presentation + Paper

Processing global and local features in convolutional neural network (CNN) and primate visual systems

Yufeng Zheng, Jun Huang, Tianwen Chen, Yang Ou, Wu Zhou

Proceedings Volume 10668, 1066809 (2018) https://doi.org/10.1117/12.2305421

KEYWORDS: Visual system, Convolutional neural networks, Visual process modeling, Image classification, Visual cortex

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Proceedings Article | 14 May 2018 Presentation + Paper

Presentation + Paper

Breast cancer screening using convolutional neural network and follow-up digital mammography

Yufeng Zheng, Clifford Yang, Alex Merkulov

Proceedings Volume 10669, 1066905 (2018) https://doi.org/10.1117/12.2304564

KEYWORDS: Mammography, Breast cancer, Computer aided diagnosis and therapy, Data modeling, Feature extraction

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SPIE Press Book | 27 March 2018

Multispectral Image Fusion and Colorization

Erik Blasch, Yufeng Zheng, Zheng Liu

KEYWORDS: Image fusion, Image processing, Image segmentation, Image quality, Image enhancement, Wavelets, Sensors, Visualization, Facial recognition systems, Infrared imaging

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Multispectral Image Fusion and Colorization

Proceedings Article | 10 May 2017 Presentation + Paper

Presentation + Paper

Fast and accurate face recognition based on image compression

Yufeng Zheng, Erik Blasch

Proceedings Volume 10221, 1022105 (2017) https://doi.org/10.1117/12.2263541

KEYWORDS: Image compression, Facial recognition systems, Detection and tracking algorithms, Image quality, Wavelets, Cameras, Image fusion, Visible radiation, Data fusion, Infrared imaging

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Proceedings Article | 19 May 2016 Paper

Paper

Image quality (IQ) guided multispectral image compression

Yufeng Zheng, Genshe Chen, Zhonghai Wang, Erik Blasch

Proceedings Volume 9871, 98710C (2016) https://doi.org/10.1117/12.2225532

KEYWORDS: Image compression, Image quality, Multispectral imaging, Discrete wavelet transforms, Image quality standards, Visualization, Signal to noise ratio, JPEG2000, Data modeling, Thermography

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Proceedings Article | 19 May 2016 Paper

Paper

Multispectral image fusion for vehicle identification and threat analysis

Yufeng Zheng, Erik Blasch

Proceedings Volume 9871, 98710G (2016) https://doi.org/10.1117/12.2228772

KEYWORDS: Image fusion, Multispectral imaging, Information fusion, Cameras, Sensors, Analytical research, Thermography, Defense and security, Image processing, Data fusion

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Proceedings Article | 19 May 2016 Paper

Paper

Early breast cancer detection with digital mammograms using Haar-like features and AdaBoost algorithm

Yufeng Zheng, Clifford Yang, Alex Merkulov, Malavika Bandari

Proceedings Volume 9871, 98710D (2016) https://doi.org/10.1117/12.2227342

KEYWORDS: Computer aided diagnosis and therapy, Breast cancer, Mammography, Detection and tracking algorithms, Solid modeling, Computer aided diagnosis and therapy, Detection and tracking algorithms, Computer aided design, Cancer, Feature extraction, Breast, Databases

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Proceedings Article | 21 May 2015 Paper

Paper

Cross-modal face recognition using multi-matcher face scores

Yufeng Zheng, Erik Blasch

Proceedings Volume 9474, 947412 (2015) https://doi.org/10.1117/12.2176409

KEYWORDS: Image fusion, Facial recognition systems, Cameras, Detection and tracking algorithms, Thermography, Data fusion, Feature extraction, Infrared cameras, Visible radiation, Gaussian filters

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Proceedings Article | 20 May 2015 Paper

Paper

Hypothesis on human eye perceiving optical spectrum rather than an image

Yufeng Zheng, Harold Szu

Proceedings Volume 9496, 94960F (2015) https://doi.org/10.1117/12.2180827

KEYWORDS: Cones, Retina, Eye, Neurons, Visualization, Retinal scanning, Rods, Fourier transforms, Visual process modeling, Nerve

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Proceedings Article | 20 June 2014 Paper

Paper

The advantages of stereo vision in a face recognition system

Yufeng Zheng, Erik Blasch

Proceedings Volume 9091, 90910Y (2014) https://doi.org/10.1117/12.2049985

KEYWORDS: Image fusion, Facial recognition systems, Cameras, Image processing, Detection and tracking algorithms, Stereo vision systems, Imaging systems, Thermography, Infrared imaging, Feature extraction

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Humans can recognize a face with binocular vision, while computers typically use a single face image. It is known that the performance of face recognition (by a computer) can be improved using the score fusion of multimodal images and multiple algorithms. A question is: Can we apply stereo vision to a face recognition system? We know that human binocular vision has many advantages such as stereopsis (3D vision), binocular summation, and singleness of vision including fusion of binocular images (cyclopean image). For face recognition, a 3D face or 3D facial features are typically computed from a pair of stereo images. In human visual processes, the binocular summation and singleness of vision are similar as image fusion processes. In this paper, we propose an advanced face recognition system with stereo imaging capability, which is comprised of two 2-in-1 multispectral (visible and thermal) cameras and three recognition algorithms (circular Gaussian filter, face pattern byte, and linear discriminant analysis [LDA]). Specifically, we present and compare stereo fusion at three levels (images, features, and scores) by using stereo images (from left camera and right camera). Image fusion is achieved with three methods (Laplacian pyramid, wavelet transform, average); feature fusion is done with three logical operations (AND, OR, XOR); and score fusion is implemented with four classifiers (LDA, k-nearest neighbor, support vector machine, binomial logical regression). The system performance is measured by probability of correct classification (PCC) rate (reported as accuracy rate in this paper) and false accept rate (FAR). The proposed approaches were validated with a multispectral stereo face dataset from 105 subjects. Experimental results show that any type of stereo fusion can improve the PCC, meanwhile reduce the FAR. It seems that stereo image/feature fusion is superior to stereo score fusion in terms of recognition performance. Further score fusion after image/feature fusion does not benefit much (or at all) for performance improvement which may imply that the power of image/feature/score fusion shall not be doubly applied to a face recognition process pipeline.

Proceedings Article | 31 December 2013 Paper

Paper

An integrated multispectral face recognition system

Yufeng Zheng

Proceedings Volume 9042, 90420K (2013) https://doi.org/10.1117/12.2036243

KEYWORDS: Facial recognition systems, Image fusion, Detection and tracking algorithms, Biometrics, Data fusion, Multispectral imaging, Visible radiation, Gaussian filters, Databases, RGB color model

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Proceedings Article | 7 June 2013 Paper

Paper

A vehicle threat detection system using correlation analysis and synthesized x-ray images

Yufeng Zheng, Adel Elmaghraby

Proceedings Volume 8709, 87090V (2013) https://doi.org/10.1117/12.2016646

KEYWORDS: Image fusion, X-rays, X-ray imaging, Databases, LCDs, Image processing, Image enhancement, Visualization, Visual analytics, X-ray detectors

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Proceedings Article | 23 May 2013 Paper

Paper

Qualitative evaluations and comparisons of six night-vision colorization methods

Yufeng Zheng, Kristopher Reese, Erik Blasch, Paul McManamon

Proceedings Volume 8745, 874511 (2013) https://doi.org/10.1117/12.2016643

KEYWORDS: Image fusion, Near infrared, RGB color model, Long wavelength infrared, Multispectral imaging, Image processing, Image segmentation, Image quality, Night vision, Infrared imaging

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Current multispectral night vision (NV) colorization techniques can manipulate images to produce colorized images that closely resemble natural scenes. The colorized NV images can enhance human perception by improving observer object classification and reaction times especially for low light conditions. This paper focuses on the qualitative (subjective) evaluations and comparisons of six NV colorization methods. The multispectral images include visible (Red-Green- Blue), near infrared (NIR), and long wave infrared (LWIR) images. The six colorization methods are channel-based color fusion (CBCF), statistic matching (SM), histogram matching (HM), joint-histogram matching (JHM), statistic matching then joint-histogram matching (SM-JHM), and the lookup table (LUT). Four categries of quality measurements are used for the qualitative evaluations, which are contrast, detail, colorfulness, and overall quality. The score of each measurement is rated from 1 to 3 scale to represent low, average, and high quality, respectively. Specifically, high contrast (of rated score 3) means an adequate level of brightness and contrast. The high detail represents high clarity of detailed contents while maintaining low artifacts. The high colorfulness preserves more natural colors (i.e., closely resembles the daylight image). Overall quality is determined from the NV image compared to the reference image. Nine sets of multispectral NV images were used in our experiments. For each set, the six colorized NV images (produced from NIR and LWIR images) are concurrently presented to users along with the reference color (RGB) image (taken at daytime). A total of 67 subjects passed a screening test (“Ishihara Color Blindness Test”) and were asked to evaluate the 9-set colorized images. The experimental results showed the quality order of colorization methods from the best to the worst: CBCF < SM < SM-JHM < LUT < JHM < HM. It is anticipated that this work will provide a benchmark for NV colorization and for quantitative evaluation using an objective metric such as objective evaluation index (OEI).

Proceedings Article | 21 May 2013 Paper

Paper

A holistic image segmentation framework for cloud detection and extraction

Dan Shen, Haotian Xu, Erik Blasch, Gregory Horvath, Khanh Pham, Yufeng Zheng, Haibin Ling, Genshe Chen

Proceedings Volume 8739, 873911 (2013) https://doi.org/10.1117/12.2015175

KEYWORDS: Clouds, Image segmentation, Image processing, Video, Fermium, Frequency modulation, RGB color model, Analytical research, Atmospheric sensing, Binary data

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SPIE Journal Paper | 5 September 2012 Open Access

Open Access

Qualitative and quantitative comparisons of multispectral night vision colorization techniques

Yufeng Zheng, Wenjie Dong, Erik Blasch

OE, Vol. 51, Issue 8, 087004, (September 2012) https://doi.org/10.1117/12.10.1117/1.OE.51.8.087004

KEYWORDS: Image fusion, Image segmentation, RGB color model, Long wavelength infrared, Multispectral imaging, Near infrared, Night vision, Optical engineering, Image processing, Infrared imaging

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Proceedings Article | 15 May 2012 Paper

Paper

Interdisciplinary education approach to the human science

Harold Szu, Yufeng Zheng, Nian Zhang

Proceedings Volume 8401, 840103 (2012) https://doi.org/10.1117/12.923440

KEYWORDS: Wavelets, Independent component analysis, Nanoengineering, Biomedical optics, Biology, Machine learning, Sensing systems, Ear, Neural networks, Iron

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Proceedings Article | 10 May 2012 Paper

Paper

A wavelet-based method for multispectral face recognition

Yufeng Zheng, Chaoyang Zhang, Zhaoxian Zhou

Proceedings Volume 8401, 840105 (2012) https://doi.org/10.1117/12.919264

KEYWORDS: Facial recognition systems, Image fusion, Databases, Detection and tracking algorithms, Near infrared, Principal component analysis, Wavelet transforms, Binary data, Visible radiation, Data fusion

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Proceedings Article | 10 May 2012 Paper

Paper

An objective evaluation metric for color image fusion

Wenjie Dong, Yufeng Zheng

Proceedings Volume 8401, 84010U (2012) https://doi.org/10.1117/12.919211

KEYWORDS: Image fusion, Image quality, Image sensors, RGB color model, Image filtering, Image analysis, Infrared imaging, Infrared radiation, Composites, Sensors

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Proceedings Article | 2 February 2012 Paper

Paper

Face detection and eyeglasses detection for thermal face recognition

Yufeng Zheng

Proceedings Volume 8300, 83000C (2012) https://doi.org/10.1117/12.907123

KEYWORDS: Facial recognition systems, Eyeglasses, Glasses, Thermography, Detection and tracking algorithms, Image segmentation, Databases, Head, Image processing algorithms and systems, Binary data

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SPIE Journal Paper | 1 November 2011

Orientation-based face recognition using multispectral imagery and score fusion

Yufeng Zheng

OE, Vol. 50, Issue 11, 117202, (November 2011) https://doi.org/10.1117/12.10.1117/1.3643729

KEYWORDS: Facial recognition systems, Image fusion, Databases, Detection and tracking algorithms, Multispectral imaging, Principal component analysis, Thermography, Binary data, Glasses, Wavelet transforms

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Proceedings Article | 24 September 2011 Paper

Paper

A channel-based color fusion technique using multispectral images for night vision enhancement

Yufeng Zheng

Proceedings Volume 8135, 813511 (2011) https://doi.org/10.1117/12.895518

KEYWORDS: Image fusion, Image segmentation, Long wavelength infrared, RGB color model, Near infrared, Multispectral imaging, Image processing, Image registration, Infrared imaging, Night vision

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Proceedings Article | 17 September 2011 Paper

Paper

Game theoretic approach to similarity-based image segmentation

Dan Shen, Genshe Chen, Yufeng Zheng, Erik Blasch, Khanh Pham

Proceedings Volume 8137, 813708 (2011) https://doi.org/10.1117/12.896681

KEYWORDS: Image segmentation, Image processing algorithms and systems, RGB color model, Data modeling, Chemical elements, Machine vision, Computer vision technology, Image processing, Medical research, Analytical research

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Proceedings Article | 7 June 2011 Paper

Paper

A hidden Markov model for multimodal biometrics score fusion

Yufeng Zheng

Proceedings Volume 8064, 80640D (2011) https://doi.org/10.1117/12.884743

KEYWORDS: Databases, Biometrics, Data fusion, Systems modeling, Performance modeling, Image fusion, Data modeling, Reliability, Facial recognition systems, Detection and tracking algorithms

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Proceedings Article | 3 June 2011 Paper

Paper

A novel orientation code for face recognition

Yufeng Zheng

Proceedings Volume 8056, 805606 (2011) https://doi.org/10.1117/12.884745

KEYWORDS: Facial recognition systems, Databases, Detection and tracking algorithms, Principal component analysis, Thermography, Wavelet transforms, Binary data, Visible radiation, Infrared imaging, Visualization

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Proceedings Article | 28 April 2010 Paper

Paper

An orientation-based fusion algorithm for multisensor image fusion

Yufeng Zheng

Proceedings Volume 7710, 77100K (2010) https://doi.org/10.1117/12.849656

KEYWORDS: Image fusion, Image quality, Long wavelength infrared, Discrete wavelet transforms, Wavelet transforms, Visible radiation, Thermography, Infrared imaging, Infrared radiation, Image filtering

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Proceedings Article | 14 April 2010 Paper

Paper

A novel thermal face recognition approach using face pattern words

Yufeng Zheng

Proceedings Volume 7667, 766703 (2010) https://doi.org/10.1117/12.850664

KEYWORDS: Facial recognition systems, Databases, Glasses, Thermography, Eyeglasses, Image fusion, Head, Wavelet transforms, Detection and tracking algorithms, Diffusion

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Proceedings Article | 5 May 2009 Paper

Paper

Component-based target recognition inspired by human vision

Yufeng Zheng, Kwabena Agyepong

Proceedings Volume 7335, 73350V (2009) https://doi.org/10.1117/12.820074

KEYWORDS: Target recognition, Visual process modeling, Image processing, Human vision and color perception, Visualization, Automatic target recognition, Feature extraction, Detection and tracking algorithms, Image enhancement, Machine vision

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Proceedings Article | 28 April 2009 Paper

Paper

Bio-inspired color image enhancement model

Yufeng Zheng

Proceedings Volume 7341, 73410D (2009) https://doi.org/10.1117/12.819519

KEYWORDS: Image enhancement, Image processing, Visual process modeling, Cones, Retina, RGB color model, Biomimetics, Visualization, Reverse modeling, Neurons

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Human being can perceive natural scenes very well under various illumination conditions. Partial reasons are due to the contrast enhancement of center/surround networks and opponent analysis on the human retina. In this paper, we propose an image enhancement model to simulate the color processes in the human retina. Specifically, there are two center/surround layers, bipolar/horizontal and ganglion/amacrine; and four color opponents, red (R), green (G), blue (B), and yellow (Y). The central cell (bipolar or ganglion) takes the surrounding information from one or several horizontal or amacrine cells; and bipolar and ganglion both have ON and OFF sub-types. For example, a +R/-G bipolar (red-center- ON/green-surround-OFF) will be excited if only the center is illuminated, or inhibited if only the surroundings (bipolars) are illuminated, or stay neutral if both center and surroundings are illuminated. Likewise, other two color opponents with ON-center/OFF-surround, +G/-R and +B/-Y, follow the same rules. The yellow (Y) channel can be obtained by averaging red and green channels. On the other hand, OFF-center/ON-surround bipolars (i.e., -R/+G and -G/+R, but no - B/+Y) are inhibited when the center is illuminated. An ON-bipolar (or OFF-bipolar) only transfers signals to an ONganglion (or OFF-ganglion), where amacrines provide surrounding information. Ganglion cells have strong spatiotemporal responses to moving objects. In our proposed enhancement model, the surrounding information is obtained using weighted average of neighborhood; excited or inhibited can be implemented with pixel intensity increase or decrease according to a linear or nonlinear response; and center/surround excitations are decided by comparing their intensities. A difference of Gaussian (DOG) model is used to simulate the ganglion differential response. Experimental results using natural scenery pictures proved that, the proposed image enhancement model by simulating the two-layer center/surrounding retinal networks can effectively enhance color images in terms of color contrast and image details.

Proceedings Article | 17 April 2008 Paper

Paper

Multisensory data exploitation using advanced image fusion and adaptive colorization

Yufeng Zheng, Kwabena Agyepong, Ognjen Kuljaca

Proceedings Volume 6968, 69681U (2008) https://doi.org/10.1117/12.784043

KEYWORDS: Image fusion, Image segmentation, Discrete wavelet transforms, Image processing, Image quality, Infrared imaging, RGB color model, Visualization, Diffusion, Image enhancement

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Proceedings Article | 24 March 2008 Paper

Paper

Gaussian model-based statistical matching for image enhancement and segmentation

Yufeng Zheng

Proceedings Volume 6978, 697802 (2008) https://doi.org/10.1117/12.784092

KEYWORDS: Image segmentation, Image enhancement, Medical imaging, Statistical modeling, Image processing, Diffusion, Image fusion, Statistical analysis, Model-based design, Infrared imaging

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Proceedings Article | 8 March 2007 Paper

Paper

Mass detection with digitized screening mammograms by using Gabor features

Yufeng Zheng, Kwabena Agyepong

Proceedings Volume 6514, 651402 (2007) https://doi.org/10.1117/12.707382

KEYWORDS: Mammography, Image segmentation, Image classification, Image filtering, Breast cancer, Image processing, Detection and tracking algorithms, Diffusion, Breast, Computer aided diagnosis and therapy

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Breast cancer is the leading cancer among American women. The current lifetime risk of developing breast cancer is 13.4% (one in seven). Mammography is the most effective technology presently available for breast cancer screening. With digital mammograms computer-aided detection (CAD) has proven to be a useful tool for radiologists. In this paper, we focus on mass detection that is a common category of breast cancers relative to calcification and architecture distortion. We propose a new mass detection algorithm utilizing Gabor filters, termed as "Gabor Mass Detection" (GMD). There are three steps in the GMD algorithm, (1) preprocessing, (2) generating alarms and (3) classification (reducing false alarms). Down-sampling, quantization, denoising and enhancement are done in the preprocessing step. Then a total of 30 Gabor filtered images (along 6 bands by 5 orientations) are produced. Alarm segments are generated by thresholding four Gabor images of full orientations (Stage-I classification) with image-dependent thresholds computed via histogram analysis. Next a set of edge histogram descriptors (EHD) are extracted from 24 Gabor images (6 by 4) that will be used for Stage-II classification. After clustering EHD features with fuzzy C-means clustering method, a k-nearest neighbor classifier is used to reduce the number of false alarms. We initially analyzed 431 digitized mammograms (159 normal images vs. 272 cancerous images, from the DDSM project, University of South Florida) with the proposed GMD algorithm. And a ten-fold cross validation was used for testing the GMD algorithm upon the available data. The GMD performance is as follows: sensitivity (true positive rate) = 0.88 at false positives per image (FPI) = 1.25, and the area under the ROC curve = 0.83. The overall performance of the GMD algorithm is satisfactory and the accuracy of locating masses (highlighting the boundaries of suspicious areas) is relatively high. Furthermore, the GMD algorithm can successfully detect early-stage (with small values of Assessment & low Subtlety) malignant masses. In addition, Gabor filtered images are used in both stages of classifications, which greatly simplifies the GMD algorithm.

Proceedings Article | 10 March 2006 Paper

Paper

Three-band MRI image fusion utilizing the wavelet-based method optimized with two quantitative fusion metrics

Yufeng Zheng, Adel Elmaghraby, Hichem Frigui

Proceedings Volume 6144, 61440R (2006) https://doi.org/10.1117/12.651689

KEYWORDS: Image fusion, Magnetic resonance imaging, Image quality, Discrete wavelet transforms, Image processing, Spatial frequencies, Image segmentation, Image analysis, Data fusion, Medical imaging

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SPIE Journal Paper | 1 March 2005

Advanced discrete wavelet transform fusion algorithm and its optimization by using the metric of image quality index

Yufeng Zheng, Edward Essock, Bruce Hansen

OE, Vol. 44, Issue 03, 037003, (March 2005) https://doi.org/10.1117/12.10.1117/1.1871812

KEYWORDS: Image fusion, Discrete wavelet transforms, Image quality, Image processing, Wavelets, Medical imaging, Infrared imaging, Optical engineering, Image filtering, Principal component analysis

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Proceedings Article | 17 January 2005 Paper

Paper

Coloring night-vision imagery with statistical properties of natural colors by using image segmentation and histogram matching

Yufeng Zheng, Bruce Hansen, Andrew Haun, Edward Essock

Proceedings Volume 5667, (2005) https://doi.org/10.1117/12.584939

KEYWORDS: Image segmentation, Diffusion, RGB color model, Nonlinear filtering, Image processing, Infrared imaging, Image enhancement, Image fusion, Roads, Image filtering

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SPIE Journal Paper | 1 January 2005

Resolution improvement and detail enhancement for computed tomography scout images

Yufeng Zheng, Mark Wachowiak, Adel Elmaghraby

JEI, Vol. 14, Issue 01, 013003, (January 2005) https://doi.org/10.1117/12.10.1117/1.1867997

KEYWORDS: Image fusion, Image enhancement, Finite element methods, Image resolution, Electronic filtering, Discrete wavelet transforms, Point spread functions, Image processing, Wavelets, Computed tomography

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Proceedings Article | 28 May 2004 Paper

Paper

An advanced image fusion algorithm based on wavelet transform: incorporation with PCA and morphological processing

Yufeng Zheng, Edward Essock, Bruce Hansen

Proceedings Volume 5298, (2004) https://doi.org/10.1117/12.523966

KEYWORDS: Image fusion, Discrete wavelet transforms, Image quality, Medical imaging, Image processing, Wavelets, Principal component analysis, Infrared imaging, Image sensors, Image analysis

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Proceedings Article | 15 May 2003 Paper

Paper

CT scout z-resolution improvement with image restoration methods

Yufeng Zheng, Xiaohui Cui, Mark Wachowiak, Adel Elmaghraby

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.480308

KEYWORDS: Electronic filtering, Image filtering, Point spread functions, Image restoration, Image resolution, Computed tomography, Image enhancement, Image processing, Image fusion, Image quality

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Proceedings Article | 18 December 2001 Paper

Paper

Single-character type identification

Yefeng Zheng, Changsong Liu, Xiaoqing Ding

Proceedings Volume 4670, (2001) https://doi.org/10.1117/12.450738

KEYWORDS: Optical character recognition, Feature extraction, Image quality, Error analysis, Neural networks, Electronics engineering, Fluctuations and noise, Mathematics, Sodium, Virtual colonoscopy

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Proceedings Article | 31 December 1996 Paper

Paper

Echocardiographic visual processing and quantitative analysis

Yufeng Zheng, Ji Du, Long Qi, Daoyin Yu

Proceedings Volume 2866, (1996) https://doi.org/10.1117/12.263028

KEYWORDS: Quantitative analysis, Edge detection, Visual analytics, Visualization, Heart, Detection and tracking algorithms, Image processing, Computer aided diagnosis and therapy, Digital filtering, Computing systems

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Showing 5 of 55 publications

Proceedings Volume Editor (2)

SPIE Conference Volume | 6 October 2016

Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016

SPIE Conference Volume | 19 June 2015

Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII

Conference Committee Involvement (22)

Multimodal Image Exploitation and Learning 2025

14 April 2025 | Orlando, Florida, United States

Sensors and Systems for Space Applications XVIII

13 April 2025 | Orlando, Florida, United States

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIV

13 April 2025 | Orlando, Florida, United States

Sensors and Systems for Space Applications XVII

23 April 2024 | National Harbor, Maryland, United States

Multimodal Image Exploitation and Learning 2024

22 April 2024 | National Harbor, Maryland, United States

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII

22 April 2024 | National Harbor, Maryland, United States

Sensors and Systems for Space Applications XVI

2 May 2023 | Orlando, Florida, United States

Multimodal Image Exploitation and Learning 2023

1 May 2023 | Orlando, Florida, United States

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII

1 May 2023 | Orlando, Florida, United States

Sensors and Systems for Space Applications XV

4 April 2022 | Orlando, Florida, United States

Multimodal Image Exploitation and Learning 2022

4 April 2022 | Orlando, Florida, United States

Multimodal Image Exploitation and Learning 2021

12 April 2021 | Online Only, Florida, United States

Sensors and Systems for Space Applications XIV

12 April 2021 | Online Only, Florida, United States

Mobile Image Exploitation and Learning 2020

27 April 2020 | Online Only, California, United States

Sensors and Systems for Space Applications XIII

27 April 2020 | Online Only, California, United States

Mobile Multimedia/Image Processing, Security, and Applications 2019

15 April 2019 | Baltimore, MD, United States

Mobile Multimedia/Image Processing, Security, and Applications 2018

16 April 2018 | Orlando, FL, United States

Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016

17 April 2016 | Baltimore, MD, United States

Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII

23 April 2015 | Baltimore, MD, United States

Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII

7 May 2014 | Baltimore, MD, United States

Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI

1 May 2013 | Baltimore, Maryland, United States

Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X

25 April 2012 | Baltimore, Maryland, United States

Showing 5 of 22 Conference Committees

Course Instructor

SC1135: Multispectral Image Fusion and Night Vision Colorization

This course presents methods and applications of multispectral image fusion and night vision colorization organized into three areas: (1) image fusion methods, (2) evaluation, and (3) applications. Two primary multiscale fusion approaches, image pyramid and wavelet transform, will be emphasized. Image fusion comparisons include data, metrics, and analytics. Fusion applications presented include off-focal images, medical images, night vision, and face recognition. Examples will be discussed of night-vision images rendered using channel-based color fusion, lookup-table color mapping, and segment-based method colorization. These colorized images resemble natural color scenes and thus can improve the observer’s performance. After taking this course you will know how to combine multiband images and how to render the result with colors in order to enhance computer vision and human vision especially in low-light conditions. In addition to the course notes, attendees will receive a set of published papers, the data sets used in the analysis, and MATLAB code of methods and metrics for evaluation. A FTP website is established for course resource access.

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