Dr. Umair Soori Profile

Dr. Umair Soori

Research Fellow at Cranfield Univ

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Author

Publications (4)

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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