Prof. Mark S. Drew Profile

Prof. Mark S. Drew

Professor at Simon Fraser Univ

SPIE Involvement:

Author

Publications (13)

Proceedings Article | 24 February 2017 Paper

Hyperspectral image processing for detection and grading of skin erythema

Ali Madooei, Ramy Mohammed Abdlaty, Lilian Doerwald-Munoz, Joseph Hayward, Mark Drew, Qiyin Fang, Josiane Zerubia

Proceedings Volume 10133, 1013322 (2017) https://doi.org/10.1117/12.2254132

KEYWORDS: Image processing, Hyperspectral imaging, Skin, RGB color model, Cameras, Imaging systems, Sensors, Visualization, Radiotherapy, Medical imaging

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Proceedings Article | 8 February 2011 Paper

Colour image compression by grey to colour conversion

Mark Drew, Graham Finlayson, Abhilash Jindal

Proceedings Volume 7873, 78730Z (2011) https://doi.org/10.1117/12.871353

KEYWORDS: Image compression, Image segmentation, Colorimetry, RGB color model, Distortion, Video compression, Visual system, Cones, Human vision and color perception, Video

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Proceedings Article | 24 January 2011 Paper

Multispectral image invariant to illumination colour, strength, and shading

Mark Drew, Amin Yazdani Salekdeh

Proceedings Volume 7876, 78760A (2011) https://doi.org/10.1117/12.871946

KEYWORDS: Light sources and illumination, Multispectral imaging, RGB color model, Sensors, Image visualization, Visualization, Image sensors, Cameras, Calibration, Image acquisition

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Proceedings Article | 10 February 2009 Paper

Improved colour to greyscale via integrability correction

Mark Drew, David Connah, Graham Finlayson, Marina Bloj

Proceedings Volume 7240, 72401B (2009) https://doi.org/10.1117/12.810290

KEYWORDS: Magnetic resonance imaging, Image compression, Sun, Image analysis, Visualization, Electronic imaging, Image processing, Diffusion, Computer science, Life sciences

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Proceedings Article | 7 May 2003 Paper

Tracking objects with shadows

Hao Jiang, Mark Drew

Proceedings Volume 5022, (2003) https://doi.org/10.1117/12.476458

KEYWORDS: Cameras, Video, Light sources and illumination, Motion detection, Sensors, Motion estimation, Calibration, Image processing, Video surveillance, Image segmentation

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Proceedings Article | 10 January 2003 Paper

Image object search combining color with Gabor wavelet shape descriptors

Darryl Anderson, Mark Drew

Proceedings Volume 5021, (2003) https://doi.org/10.1117/12.476251

KEYWORDS: Image quality, Wavelets, Object recognition, Databases, Image retrieval, Image filtering, Detection and tracking algorithms, Shape analysis, Image classification, Image analysis

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SPIE Journal Paper | 1 November 2000

Physically based dual representation of spectral functions

Yinlong Sun, F. Fracchia, Mark Drew

OE, Vol. 39, Issue 11, (November 2000) https://doi.org/10.1117/12.10.1117/1.1315568

KEYWORDS: Composites, Lamps, RGB color model, Image analysis, Visible radiation, Optical engineering, Diffraction, Sodium, Realistic image synthesis, Optical properties

Proceedings Article | 6 June 2000 Paper

Towards automatic segmentation of MS lesions in PD/T2 MR images

M. Stella Atkins, Mark Drew, Zinovi Tauber

Proceedings Volume 3979, (2000) https://doi.org/10.1117/12.387743

KEYWORDS: Brain, Tissues, Magnetic resonance imaging, Image segmentation, Neuroimaging, Data modeling, Statistical analysis, Technetium, Space reconnaissance, Feature extraction

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Proceedings Article | 23 December 1999 Paper

Feature localization and search by object model under illumination change

Mark Drew, Zinovi Tauber, Ze-Nian Li

Proceedings Volume 3972, (1999) https://doi.org/10.1117/12.373572

KEYWORDS: RGB color model, Databases, Image segmentation, Data modeling, Light sources and illumination, Image retrieval, Video, 3D modeling, Cameras, Detection and tracking algorithms

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Proceedings Article | 23 December 1997 Paper

Illumination-invariant video segmentation by hierarchical robust thresholding

Jie Wei, Mark Drew, Ze-Nian Li

Proceedings Volume 3312, (1997) https://doi.org/10.1117/12.298442

KEYWORDS: Video, Light sources and illumination, Distance measurement, Binary data, RGB color model, Cameras, Wavelets, Sensors, Image compression, Linear filtering

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Many methods for video segmentation rely upon the setting and tuning of thresholds for classifying interframe distances under various difference measures. An approach that has been used with some success has been to establish statistical measures for each new video and identify camera cuts as difference values far from the mean. For this type of strategy the mean and dispersion for some interframe distance measure must be calculated for each new video as a whole. Here we eliminate this statistical characterization step and at the same time allow for segmentation of streaming video by introducing a preprocessing step for illumination-invariance that concomitantly reduces input values to a uniform scale. The preprocessing step provides a solution to the problem that simple changes of illumination in a scene, such as an actor emerging from a shadow, can trigger a false positive transition, no matter whether intensity alone or chrominance is used in a distance measure. Our means of discounting lighting change for color constancy consists of the simple yet effective operation of normalizing each color channel to length 1 (when viewed as a long, length-N vector). We then reduce the dimensionality of color to two-dimensional chromaticity, with values which are in 0..1. Chromaticity histograms can be treated as images, and effectively low-pass filtered by wavelet-based reduction, followed by DCT and zonal coding. This results in an indexing scheme based on only 36 numbers, and lends itself to a binary search approach to transition detection. To this end we examine distributions for intra-clip and inter-clip distances separately, characterizing each using robust statistics, for temporal intervals from 32 frames to 1 frame by powers of 2. Then combining transition and non-transition distributions for each frame internal, we seek the valley between them, again robustly, for each threshold. Using the present method values of precision and recall are increased over previous methods. Moreover, illumination change produces very few false positives.

SPIE Journal Paper | 1 October 1997

Constrained least-squares regression in color spaces

Graham Finlayson, Mark Drew

JEI, Vol. 6, Issue 04, (October 1997) https://doi.org/10.1117/12.10.1117/12.278080

KEYWORDS: Scanners, RGB color model, Reflectivity, Sensors, Cameras, Calibration, Error analysis, Matrices, Color reproduction, Measurement devices

Proceedings Article | 3 June 1997 Paper

Photometric stereo without multiple images

Mark Drew

Proceedings Volume 3016, (1997) https://doi.org/10.1117/12.274534

KEYWORDS: Optical spheres, RGB color model, Calibration, Light sources and illumination, Reflectivity, Phase modulation, Skin, Cameras, Optical filters, Image filtering

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Proceedings Article | 8 September 1993 Paper

Color constancy: enhancing von Kries adaption via sensor transformations

Graham Finlayson, Mark Drew, Brian Funt

Proceedings Volume 1913, (1993) https://doi.org/10.1117/12.152721

KEYWORDS: Sensors, Cones, Reflectivity, Eye, Algorithms, Light sources and illumination, Temperature metrology, RGB color model, Optical filters, Matrices

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Showing 5 of 13 publications

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