Current Research Topics

The research summarized here involves instrumentation, computational processing, mathematical and statistical modelling, and applications to machine and biological vision systems. Please see the publications list here for more recently reported work.

1. Hyperspectral imaging methods

Hyperspectral images provide both spatial and spectral representations of scenes, materials, and sources of illumination. They differ from images obtained with a conventional RGB colour camera, which divides the light spectrum into broad overlapping red, green, and blue image slices that when combined seem realistic to the eye. By contrast, a hyperspectral camera effectively divides the spectrum into very many thin image slices, the actual number depending on the camera and application [1]. This fine-grained slicing reveals spectral structure that may not be evident to the eye or to an RGB camera but which does become apparent in a range of optical and visual phenomena [2]  [3].

Current applications work is concerned with the acquisition and analysis of time-lapse hyperspectral image of natural scenes undergoing natural illumination changes [4] and of the remote analysis of local illuminant spectra in natural scenes [5]. These kinds of data provide a defintive test of colour-processing algorithms for surface identification, scene characterization, and object tracking. A new higher-resolution hyperspectral imaging system is in preparation.

1. Foster, D. H. & Amano, K. (2019) Hyperspectral imaging in color vision research: tutorial. Journal of the Optical Society of America A 36, 606-627. See also tutorial on transforming hyperspectral images here.

2. Foster, D.H. et al. (2006). Frequency of metamerism in natural scenes. Journal of the Optical Society of America A, 23, 2359-2372.

3. Foster, D.H. (2011). Color constancy. Vision Research, 51, 674-700.

4. Foster, D.H., et al.. (2016). Time-lapse ratios of cone excitations in natural scenes. Vision Research, 120, 45-60.

5. Nascimento, S.M.C., et al. (2016). Spatial distributions of local illumination color in natural scenes. Vision Research, 120, 39-44.

2. Information-theoretic applications in colour-image processing

Measures of uncertainty drawn from Shannon's information theory allow the relationship between images of a scene taken under different illumination conditions to be compared in a nonparametric way. This technique has been used to compare the practical use of digital-camera sensors [6] and estimate the efficiency of different colour codes [7]. It has a range of other applications including predicting the frequency of colour aliasing (metamerism) in scenes [8], limits on the retrieval of information from scenes [9], and estimating the number of distinguishable surfaces in scenes [10]. Current work is concerned with the limits on surface-colour invariances in natural scenes.

6. Marín-Franch, I., and Foster, D.H. (2013). Estimating information from image colors: An application to digital cameras and natural scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 78-91.

7. Foster, D.H., & Marín-Franch, I. (2008). Coding efficiency of cie color spaces. In: Sixteenth Color Imaging Conference, CIC16 (pp. 285-288). Portland, Oregon: Society for Imaging Science and Technology.

8. Feng, G., & Foster, D.H. (2012). Predicting frequency of metamerism in natural scenes by entropy of colors. Journal of the Optical Society of America A - Optics Image Science and Vision, 29, A200-A208.

9. Foster, D.H. et al. (2009). Approaching ideal observer efficiency in using color to retrieve information from natural scenes. Journal of the Optical Society of America A, 26 (11), B14-B24.

10. 9. Marín-Franch, I., & Foster, D.H. (2010). Number of perceptually distinct surface colors in natural scenes. Journal of Vision, 10(9):9

3. Engineering approaches to biological vision

Opto-electronic imaging of the eye and real-time control of high-resolution image displays can be used in experiments to test limits on processing in the human eye and brain. Detection performance and regularities in apparently random patterns of eye movements have been revealed as observers search for targets in images of natural scenes [11] [12]. Previous research has been concerned with changes in vision in the eyes of individuals with defective photoreceptors [13] and with the development of model-free statistical methods for analysing eye-movement data [14]. Current work is concerned with search, detection, and identification in natural scenes [15].

11.  Amano, K., & Foster, D.H. (2014). Influence of local scene color on fixation position in visual search. Journal of the Optical Society of America A, 31 (4), A254-A262.

12.  Amano, K. et al. (2012). Visual search in natural scenes explained by local color properties. Journal of the Optical Society of America A, 29, A194-A199.

13. Carroll, J. et al. (2009). Cone photoreceptor mosaic disruption associated with Cys203Arg mutation in the M-cone opsin. Proceedings of the National Academy of Sciences, 106 (49), 20948-20953.

14. Mould, M.S., et al. (2012). A simple nonparametric method for classifying eye fixations. Vision Research, 57, 18-25.

15. Foster, D. H. (2018). The Verriest Lecture: Color vision in an uncertain world. Journal of the Optical Society of America. A 35, B192-B201.

A Concise Guide to Communication in Science & Engineering

Concise Guide
  1. How to write research articles, literature reviews, technical reports, dissertations and theses
  2. How to deal with mathematical material, statistical inference, data fitting, plotting graphs
  3. How to submit papers for publication, measure impact, present posters, give lectures and talks
  4. How to handle ethical issues, manage data, avoid plagiarism
The advice in this guide is based on several hundred sources. It includes practical checklists at the ends of chapters for quick reference and verification, and it has over 400 examples of good and bad scientific writing and graphing from published research articles.

Published by Oxford University Press, 2017/18, 408 pages. ISBN: 9780198704249. Available as hardback, paperback, or ebook.

(c) D. H. Foster, 2022 | +44 (0)161 306 3888 | 
Department of Electrical and Electronic Engineering, University of Manchester, Manchester M13 9PL, UK