Dr. Peter Yuen Profile

Dr. Peter Yuen

Reader in remote sensing & EO at Cranfield Univ

SPIE Involvement:

Author

Publications (19)

SPIE Journal Paper | 26 May 2017

Image dehazing based on partitioning reconstruction and entropy-based alternating fast-weighted guided filters

YuanYu Wang, Peter Yuen

OE, Vol. 56, Issue 05, 053111, (May 2017) https://doi.org/10.1117/12.10.1117/1.OE.56.5.053111

KEYWORDS: Image filtering, Image enhancement, Image quality, Scattering, Image restoration, Image transmission, Reconstruction algorithms, Air contamination

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Proceedings Article | 25 October 2016 Presentation + Paper

Comparative noise performance of a coded aperture spectral imager

Jonathan Piper, Peter Yuen, Peter Godfree, Mengjia Ding, Umair Soori, Senthurran Selvagumar, David James

Proceedings Volume 9992, 99920G (2016) https://doi.org/10.1117/12.2240926

KEYWORDS: Sensors, Coded apertures, Imaging systems, Signal to noise ratio, Multiplexing, Interference (communication), Binary data, Short wave infrared radiation, Neodymium, Infrared sensors

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SPIE Journal Paper | 8 April 2013

Target recognitions in multiple-camera closed-circuit television using color constancy

Umair Soori, Peter Yuen, Ji Wen Han, Izzati Ibrahim, Wentao Chen, Kan Hong, Christian Merfort, Mark Richardson, David James

OE, Vol. 52, Issue 04, 047202, (April 2013) https://doi.org/10.1117/12.10.1117/1.OE.52.4.047202

KEYWORDS: Target detection, Cameras, Detection and tracking algorithms, Target recognition, Video, Data processing, Televisions, Sensors, RGB color model, Optical engineering

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People tracking in crowded scenes from closed-circuit television (CCTV) footage has been a popular and challenging task in computer vision. Due to the limited spatial resolution in the CCTV footage, the color of people’s dress may offer an alternative feature for their recognition and tracking. However, there are many factors, such as variable illumination conditions, viewing angles, and camera calibration, that may induce illusive modification of intrinsic color signatures of the target. Our objective is to recognize and track targets in multiple camera views using color as the detection feature, and to understand if a color constancy (CC) approach may help to reduce these color illusions due to illumination and camera artifacts and thereby improve target recognition performance. We have tested a number of CC algorithms using various color descriptors to assess the efficiency of target recognition from a real multicamera Imagery Library for Intelligent Detection Systems (i-LIDS) data set. Various classifiers have been used for target detection, and the figure of merit to assess the efficiency of target recognition is achieved through the area under the receiver operating characteristics (AUROC). We have proposed two modifications of luminance-based CC algorithms: one with a color transfer mechanism and the other using a pixel-wise sigmoid function for an adaptive dynamic range compression, a method termed enhanced luminance reflectance CC (ELRCC). We found that both algorithms improve the efficiency of target recognitions substantially better than that of the raw data without CC treatment, and in some cases the ELRCC improves target tracking by over 100% within the AUROC assessment metric. The performance of the ELRCC has been assessed over 10 selected targets from three different camera views of the i-LIDS footage, and the averaged target recognition efficiency over all these targets is found to be improved by about 54% in AUROC after the data are processed by the proposed ELRCC algorithm. This amount of improvement represents a reduction of probability of false alarm by about a factor of 5 at the probability of detection of 0.5. Our study concerns mainly the detection of colored targets; and issues for the recognition of white or gray targets will be addressed in a forthcoming study.

SPIE Journal Paper | 4 October 2012

Illumination invariance and shadow compensation via spectro-polarimetry technique

Izzati Ibrahim, Peter Yuen, Kan Hong, Tong Chen, Umair Soori, James Jackman, Mark Richardson

OE, Vol. 51, Issue 10, 107004, (October 2012) https://doi.org/10.1117/12.10.1117/1.OE.51.10.107004

KEYWORDS: Reflectivity, Hyperspectral imaging, Illumination engineering, Polarization, Polarizers, RGB color model, Imaging systems, Sensors, Optical engineering, Cameras

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Proceedings Article | 13 October 2011 Paper

Colour invariant target recognition in multiple camera CCTV surveillance

Umair Soori, P. W. Yuen, I. Ibrahim, J. Han, A. Tsitiridis, K. Hong, T. Chen, J. Jackman, D. James, M. Richardson

Proceedings Volume 8189, 81890P (2011) https://doi.org/10.1117/12.898079

KEYWORDS: Cameras, Target detection, Detection and tracking algorithms, Video, Image processing, Target recognition, RGB color model, Reflectivity, Machine vision, Algorithm development

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Proceedings Article | 12 October 2010 Paper

Effect of payload size on pre-emptive flare countermeasure against man-portable air-defence (MANPAD) system

James Jackman, Mark Richardson, Peter Yuen, David James, Brian Butters, Roy Walmsley, Nic Millwood

Proceedings Volume 7836, 78360M (2010) https://doi.org/10.1117/12.866251

KEYWORDS: Missiles, Lead, Signal processing, 3D modeling, Sensors, Optical simulations, Computer simulations, Performance modeling, Reticles, Signal detection

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Proceedings Article | 12 October 2010 Paper

Effect of pre-emptive flares on Man-Portable Air-Defence (MANPAD) systems with a track angle bias counter-countermeasure (CCM)

James Jackman, Mark Richardson, Brian Butters, Roy Walmsley, Nic Millwood, Peter Yuen, David James

Proceedings Volume 7836, 78360N (2010) https://doi.org/10.1117/12.866252

KEYWORDS: Missiles, Reticles, Head, Imaging infrared seeker, Lead, Sensors, Signal processing, 3D modeling, Systems modeling, Amplitude modulation

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Proceedings Article | 12 October 2010 Paper

Illumination independent object recognitions in hyperspectral imaging

Izzati Ibrahim, Peter Yuen, Aristeidis Tsitiridis, Kan Hong, Tong Chen, James Jackman, David James, Mark Richardson

Proceedings Volume 7838, 78380O (2010) https://doi.org/10.1117/12.864535

KEYWORDS: Reflectivity, RGB color model, Image classification, Hyperspectral imaging, Imaging systems, Digital image correlation, Scene classification, Sensors, Defense and security, Object recognition

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Proceedings Article | 12 October 2010 Paper

An improved cortex-like neuromorphic system for target recognitions

Aristeidis Tsitiridis, Peter Yuen, Kan Hong, Tong Chen, Izzati Ibrahim, James Jackman, David James, Mark Richardson

Proceedings Volume 7838, 78380Q (2010) https://doi.org/10.1117/12.864477

KEYWORDS: Visualization, Copper, Image filtering, Machine vision, Data modeling, Object recognition, Feature extraction, Brain, Image classification, Detection and tracking algorithms

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Proceedings Article | 26 September 2009 Paper

Analysis of first generation MANPAD attacks on fast jets

James Jackman, Mark Richardson, Brian Butters, Roy Walmsley, Nic Millwood, Peter Yuen, David James

Proceedings Volume 7483, 74830I (2009) https://doi.org/10.1117/12.829464

KEYWORDS: Missiles, Lead, 3D modeling, Error analysis, Computer simulations, Head, Signal detection, Signal processing, Sensors, Weapons

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Proceedings Article | 25 September 2009 Paper

A biological cortex like target recognition and tracking in cluttered background

Aristeidis Tsitiridis, Peter Yuen, Kan Hong, Tong Chen, Firmin Kam, James Jackman, David James, Mark Richardson

Proceedings Volume 7486, 74860G (2009) https://doi.org/10.1117/12.830489

KEYWORDS: RGB color model, Target recognition, Visualization, Visual process modeling, Object recognition, Video, Image classification, Feature extraction, Visual cortex, Detection and tracking algorithms

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Proceedings Article | 25 September 2009 Paper

Remote sensing of stress using electro-optics imaging technique

Tong Chen, Peter Yuen, Kan Hong, Aristeidis Tsitiridis, Firmin Kam, James Jackman, David James, Mark Richardson, William Oxford, Jonathan Piper, Francis Thomas, Stafford Lightman

Proceedings Volume 7486, 748606 (2009) https://doi.org/10.1117/12.830485

KEYWORDS: Blood, Heart, Hyperspectral imaging, Brain, Oxygen, Cameras, Remote sensing, Electro optics, Tissues, Ischemia

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Proceedings Article | 25 September 2009 Paper

Detection and classification of stress using thermal imaging technique

Kan Hong, Peter Yuen, Tong Chen, Aristeidis Tsitiridis, Firmin Kam, James Jackman, David James, Mark Richardson, William Oxford, Jonathan Piper, Francis Thomas, Stafford Lightman

Proceedings Volume 7486, 74860I (2009) https://doi.org/10.1117/12.830496

KEYWORDS: Thermography, Blood, Cameras, Skin, Temperature metrology, Brain, Heart, Image processing, Neck, Black bodies

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Proceedings Article | 3 October 2008 Paper

People tracking without prior information: a biological cortex-like neural approach

Peter Yuen

Proceedings Volume 7119, 711908 (2008) https://doi.org/10.1117/12.800315

KEYWORDS: Visualization, Visual process modeling, Object recognition, Surveillance, Image processing, Neurons, Sensors, Target detection, RGB color model, Data modeling

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Proceedings Article | 5 October 2006 Paper

Hyperspectral multiple approach fusion for the long-range detection of low observable objects: MUF2

Peter W. Yuen, G. Bishop

Proceedings Volume 6396, 63960C (2006) https://doi.org/10.1117/12.686818

KEYWORDS: Target detection, Sensors, Detection and tracking algorithms, Modeling, Data modeling, Statistical modeling, Vegetation, Image segmentation, Artificial intelligence, Statistical analysis

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Highly efficient target detection algorithms in hyperspectral remote sensing technology, particularly for the long range detection of very low observable objects which exhibit extremely small detection cross sections, are in great demand. This is more so for a near or real time application. This paper is concerned with global anomaly detections (GAD), and conventional methods to achieve better detection using multiple approach fusion (MAF), which fuses detection outputs from various detectors using either logical operators, or, via a model based estimation of the joint detection statistics from all detectors, is found to be not good enough. This work emphasises the need to integrate a more comprehensive background modelling into the GAD to develop a robust anomaly detector (AD). Then, the detection output from this detector is fused with other detectors via MAF for a further improvement of detection performance. The MUF2 algorithm is formulated exactly using this 2-level fusion mechanism, in which mixture modelling and spectral unmixing fusion have been employed. The significance of background modelling in GAD has been highlighted in this work using real data. The result has shown a factor of 2-5 reduction in detection performance when a very small amount of target pixels (~0.1%) is misclassified as background. This is because anomalies are defined with reference to a model of the background, and subsequently two new background classification techniques have been proposed in this work. The effectiveness of the MUF2 has been assessed using three representative data sets which contain various different types of targets, ranging from vehicles to small plates embedded in backgrounds with various degrees of homogeneity. The performance of MUF2 has been shown to be more superior than the conventional GAD frequently in orders of magnitude, regardless of the background homogeneity and target types. The current version of the MUF2 is run under Matlab and it takes ~2 minutes to process a 20K pixel imagery. This work forms part of the research programme supported by the EMRS DTC established by the UK MOD.

Proceedings Article | 21 October 2005 Paper

The effectiveness and limitations of geometric and statistical spectral unmixing techniques for subpixel target detection in hyperspectral remote sensing

Peter WT Yuen, A. Blagg, G. Bishop

Proceedings Volume 5988, 59880C (2005) https://doi.org/10.1117/12.629864

KEYWORDS: Evolutionary algorithms, Independent component analysis, Artificial intelligence, Expectation maximization algorithms, Remote sensing, Statistical analysis, Hyperspectral imaging, Modeling, Error analysis, Clouds

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In the literature of spectral unmixing (SU), particularly for remote sensing applications, there are claims that both geometric and statistical techniques using independency as cost functions^1-4, are very applicable for analysing hyperspectral imagery. These claims are vigorously examined and verified in this paper, using sets of simulated and real data. The objective is to study how effective these two SU approaches are with respected to the modality and independency of the source data. The data sets are carefully designed such that only one parameter is varied at a time. The 'goodness' of the unmixed result is judged by using the well-known Amari index (AI), together with a 3D visualisation of the deduced simplex in eigenvector space. A total of seven different algorithms, of which one is geometric and the others are statistically independent based have been studied. Two of the statistical algorithms use non-negative constraint of modelling errors (NMF & NNICA) as cost functions and the other four employ the independent component analysis (ICA) principle to minimise mutual information (MI) as the objective function. The result has shown that, the ICA based statistical technique is very effective to find the correct endmember (EM) even for the highly intermixed imagery, provided that the sources are completely independent. Modality of the data source is found to only have a second order impact on the unmixing capabilities of ICA based algorithms. All ICA based algorithms are seen to fail when the MI of sources are above 1, and the NMF type of algorithms are found even more sensitive to the dependency of sources. Typical independency of species found in the natural environment is in the range of 15-30. This indicates that, conventional statistical ICA and matrix factorisation (MF) techniques, are really not very suitable for the spectral unmixing of hyperspectral (HSI) data. Future work is proposed to investigate the idea of a dependent component clustering technique, a fused geometric and statistical approach, and couple these with a modification of the conventional ICA based algorithms to model the independency of the mixing, rather than the sources. This work formulates part of the research programme supported by the EMRS DTC established by the UK MOD.

Proceedings Article | 8 December 2004 Paper

Adaptive feature extractions techniques for subpixel target detections in hyperspectral remote sensing

Peter Yuen, Gary Bishop

Proceedings Volume 5613, (2004) https://doi.org/10.1117/12.578222

KEYWORDS: Target detection, Independent component analysis, Feature extraction, Optical filters, Detection and tracking algorithms, Remote sensing, Scene simulation, Sensors, Expectation maximization algorithms, Hyperspectral target detection

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Proceedings Article | 8 December 2004 Paper

Enhancements of target detection using atmospheric correction preprocessing techniques in hyperspectral remote sensing

Peter Yuen, Gary Bishop

Proceedings Volume 5613, (2004) https://doi.org/10.1117/12.578688

KEYWORDS: Reflectivity, Atmospheric corrections, Atmospheric particles, Atmospheric modeling, Remote sensing, Target detection, Sensors, Visibility, Scattering, Rayleigh scattering

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Proceedings Article | 7 April 1998 Paper

Recent advances in In(As,Sb) SLS and QW LEDs for the 3- to 10-um region

Christopher Phillips, Harvey Hardaway, Joerg Heber, Peter Moeck, Mark Pullin, Patrick Tang, Peter Yuen

Proceedings Volume 3279, (1998) https://doi.org/10.1117/12.304425

KEYWORDS: Laser sintering, Light emitting diodes, Luminescence, Indium arsenide, Diodes, Quantum wells, Heterojunctions, Antimony, Absorption, Black bodies

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