Dr. Jesmin Farzana Khan Profile

Dr. Jesmin Farzana Khan

at Tuskegee Univ

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Author

Publications (13)

Proceedings Article | 15 October 2012 Paper

Spectral unmixing of hyperspectral data for oil spill detection

P. Sidike, J. Khan, M. Alam, S. Bhuiyan

Proceedings Volume 8498, 84981B (2012) https://doi.org/10.1117/12.981870

KEYWORDS: Sensors, Hyperspectral imaging, Target detection, Solar radiation models, Optical filters, Atmospheric corrections, Detection and tracking algorithms, Image sensors, Spectroscopy, Spatial resolution

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SPIE Journal Paper | 24 September 2012

Color image trend adjustment using a color bidimensional empirical mode decomposition method

Sharif Bhuiyan, Jesmin Khan, Mohammad Alam, Reza Adhami

JEI, Vol. 21, Issue 3, 033019, (September 2012) https://doi.org/10.1117/12.10.1117/1.JEI.21.3.033019

KEYWORDS: Image processing, Color image processing, Optical filters, Image filtering, RGB color model, Lithium, Bromine, 3D image processing, 3D displays, Image enhancement

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Proceedings Article | 23 April 2012 Paper

Spatial domain sharpening of color image employing bidimensional empirical mode decomposition

Sharif Bhuiyan, Jesmin Khan, Mohammad Alam

Proceedings Volume 8398, 83980J (2012) https://doi.org/10.1117/12.920525

KEYWORDS: Image processing, Image enhancement, Digital filtering, Optical filters, Color image processing, Image filtering, Filtering (signal processing), Signal processing, RGB color model, Gemini Planet Imager

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Proceedings Article | 26 April 2011 Paper

Empirical mode decomposition of the ECG signal for noise removal

Jesmin Khan, Sharif Bhuiyan, Gregory Murphy, Mohammad Alam

Proceedings Volume 8055, 805504 (2011) https://doi.org/10.1117/12.884744

KEYWORDS: Electrocardiography, Interference (communication), Heart, Signal processing, Electronic filtering, Electrodes, Denoising, Databases, Nonlinear filtering, Filtering (signal processing)

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Proceedings Article | 13 May 2010 Paper

Trend adjustment in color images using bidimensional empirical mode decomposition

Sharif M. Bhuiyan, Jesmin Khan, Mohammad Alam

Proceedings Volume 7696, 76961R (2010) https://doi.org/10.1117/12.852469

KEYWORDS: Image processing, Color image processing, Digital filtering, Image enhancement, RGB color model, Lung, Feature extraction, Statistical analysis, Signal processing, Filtering (signal processing)

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Proceedings Article | 16 September 2005 Paper

Minimax distance transform correlation filter-based target detection in FLIR imagery

J. Khan, M. Alam, R. Adhami, S. Bhuiyan

Proceedings Volume 5909, 590907 (2005) https://doi.org/10.1117/12.618578

KEYWORDS: Image filtering, Target detection, Forward looking infrared, Automatic target recognition, Detection and tracking algorithms, Fourier transforms, Reconstruction algorithms, Sensors, Target recognition, Image classification

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SPIE Journal Paper | 1 July 2005

Target detection in cluttered forward-looking infrared imagery

Jesmin Khan, Mohammad Alam

OE, Vol. 44, Issue 07, 076404, (July 2005) https://doi.org/10.1117/12.10.1117/1.1950147

KEYWORDS: Target detection, Detection and tracking algorithms, Image processing, Image filtering, Forward looking infrared, Automatic target recognition, Sensors, Target recognition, Image enhancement, Optical engineering

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Proceedings Article | 19 May 2005 Paper

Target detection in cluttered FLIR imagery using probabilistic neural networks

J. Khan, M. Alam

Proceedings Volume 5807, (2005) https://doi.org/10.1117/12.603991

KEYWORDS: Target detection, Image filtering, Wavelets, Forward looking infrared, Feature extraction, Neural networks, Detection and tracking algorithms, Automatic target recognition, Sensors, Image processing

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Proceedings Article | 28 March 2005 Paper

Efficient target detection in cluttered FLIR imagery

Jesmin Khan, Mohammad Alam

Proceedings Volume 5816, (2005) https://doi.org/10.1117/12.603994

KEYWORDS: Image filtering, Target detection, Nonlinear filtering, Wavelets, Forward looking infrared, Neural networks, Detection and tracking algorithms, Feature extraction, Image classification, Wavelet transforms

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In this paper, we investigated automatic target detection and classification of low and high contrast targets present in unknown forward looking infrared (FLIR) image sequence. The detection algorithm, based on morphology based preprocessing, acts as a prescreener that selects possible candidate target regions, comprising both true targets and false alarms and places expected target-sized marker to those preselected regions. The application of simple non-linear grayscale operations in the proposed detection algorithm leads to real-time implementations. By considering the known target and background specific attributes, extracted from the training samples, the clutter rejection module discriminates between true target and false alarms previously identified by the detection algorithm. Two approaches are employed for object classification where one uses local features of the image and the other uses template matching technique such as image correlation. For the first approach, to extract features, we employed two methods - nonlinear filtering for texture energy measurement and wavelet decomposition by expending Daubechies high and low pass filter coefficients. Then for classification, a neural network based classifier is used. In the second approach minimax distance transform correlation filter (MDTCF) is applied that minimizes the average squared distance from the filtered true-class training images to a filtered reference image while maximizing the mean squared distance (MSD) of the filtered false-class training images to this filtered reference image. Then classification is performed using the squared distance of a filtered test image to the chosen filtered reference image. The performance of the proposed technique is analyzed for i) neural network with nonlinear texture filtering, ii) neural network with wavelet decomposition and iii) correlation filtering. Preliminary results indicate that the proposed detection algorithms can locate both hot and cold targets from cluttered background. In addition, the clutter rejecters are capable of maintaining a low false alarm rate and excellent discrimination competence. The performance of the proposed techniques has been tested with real life FLIR imagery supplied by the Army Missile Command (AMCOM).

Proceedings Article | 21 September 2004 Paper

Fringe-adjusted joint-transform-correlation-based hetero-associative multiple target-tracking

Mohammad Alam, Jesmin Khan, Abdullah Bal

Proceedings Volume 5426, (2004) https://doi.org/10.1117/12.542315

KEYWORDS: Target detection, Detection and tracking algorithms, Electronic filtering, Optical tracking, Fourier transforms, Target recognition, Joint transforms, Optical filters, Image analysis, Image segmentation

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SPIE Journal Paper | 1 June 2004

Fringe-adjusted joint transform correlator based target detection and tracking in forward looking infrared image sequence

Mohammad Alam, M. Haque, Jasmin Khan, H. Kettani

OE, Vol. 43, Issue 06, (June 2004) https://doi.org/10.1117/12.10.1117/1.1731236

KEYWORDS: Target detection, Detection and tracking algorithms, Image segmentation, Optical correlators, Forward looking infrared, Joint transforms, Fourier transforms, Optical filters, Image filtering, Electronic filtering

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Proceedings Article | 12 April 2004 Paper

Modified phase-encoded fringe-adjusted joint transform correlation for multiple target detection

Mohammed Haider, Mohammed Islam, Mohammad Alam, Jesmin Khan

Proceedings Volume 5437, (2004) https://doi.org/10.1117/12.548065

KEYWORDS: Target detection, Fourier transforms, Phase shifts, Joint transforms, Optical filters, Optical correlators, Spatial light modulators, Target recognition, Image filtering, Signal to noise ratio

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Proceedings Article | 12 December 2003 Paper

Fringe-adjusted JTC-based target detection and tracking using subframes from a video sequence

Mohammad Alam, M. Huq, Jasmin Khan, H. Kettani, Abdul Ahad Awwal, Khan Iftekharuddin

Proceedings Volume 5201, (2003) https://doi.org/10.1117/12.502017

KEYWORDS: Target detection, Detection and tracking algorithms, Image segmentation, Video, Fourier transforms, Image enhancement, Optical correlators, Image filtering, Joint transforms, Target recognition

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Showing 5 of 13 publications

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