Flower Colours and Pollinators As a Model
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Comparing the efficiency of computational colour constancy algorithms in agent-based simulations: Flower colours and pollinators as a model Samia Faruq Thesis submitted for the degree of Doctor of Philosophy Queen Mary, University Of London 2012 1 Abstract The perceived colour of an object depends on its spectral reflection and spectral composition of the illuminant. Upon illumination change, the light reflected from the object also varies. This results in a different colour sensation if no colour constancy mechanism is available to form consistent representations of colours across various illuminants. We explore various colour constancy mechanisms in an agent-based model of foraging bees selecting flower colour based on reward. The simulations are based on empirically determined spatial distributions of various flower species in different plant communities, their rewards and spectral reflectance properties. Simulated foraging bees memorise the colours of flowers experienced as being most rewarding, and their task is to discriminate against other flower colours with lower rewards, even in the face of changing illumination conditions. The experimental setup of the simulation of bees foraging under different photic environments reveals the performance of various colour constancy mechanisms as well as the selective pressures on flower colour as a result of changing light. We compared the performance of von Kries photoreceptor adaptation and various computational colour constancy models based on the retinex theory with (hypothetical) bees with perfect colour constancy, and with modelled bees with colour blindness. While each individual model generated moderate improvements over a colour-blind bee, the most powerful recovery of reflectance in the face of changing illumination was generated by computational mechanisms that increase perceptual distances between co-occurring colours in the scene. We verified the results of our model using various comparisons between modelled bees’ performance and that predicted by our models, as well as exploring the implications for flower colour distribution in a variety of representative habitats under realistic illumination conditions. 2 Acknowledgements It is my pleasure to thank the tremendous supervision I have received from both Prof. Peter William McOwan and Prof. Lars Chittka. I am very grateful to Peter for stimulating endless inspirational ideas and discussions in my work and providing exceptional support and encouragement throughout my PhD with a lot of patience and belief. The dedication Lars provided as a scientist, an expert and a teacher to supervise me in this PhD has been invaluable. He not only introduced me to the fascinating world of bees, but he pushed me to develop my scientific work, all with an incredible amount of patience. I am very grateful for the knowledge imparted to me by Lars during my time at the bee lab. I'd like to extend my thanks to all those in the Chittka Lab who provided discussions and shared their knowledge at the lab meetings. Including, Steven Le Comber, Adrian Dyer and especially Sarah EJ Arnold with whom I worked with closely on the Floral Reflectance Database in my first few years of the PhD. In the Computer Science department, I had the pleasure to be accompanied by various people on my journey (past and present), the people in ‘RIM’ - Nuzhah Gooda Sahib, Nargis Pauran, people in Theory (with whom I shared my office years with) – Jonathan Heusser, Tom Powell, Tzu-Chun Chen, and also members of the Vision Lab. CS Systems support and their well maintained servers that ran all my experiments and all CS administrative staff, especially Melissa Yeo. I am very grateful for the support of the scholarship I received from EPSRC that made this research possible. In all, I would like to thank the staff at Queen Mary, University of London. I met many wonderful undergraduate, postgraduate/PhD students along with support staff and academic staff. It is impossible to name all - but in this I would like to thank each and every one of you who supported, encouraged, taught or just took the time to talk to me about research, bees and colours. I'd like to extend my thanks to family - my brother Zaheer Ahmed and my sisters Mariam and Kashwar for their continuous advice and support. Above all, I am forever grateful for the enormous effort and sacrifice that my mother Kalsum Bibi and father Mohammed Faruq made to ensure that I received an excellent education. To my parents, I dedicate this thesis. Samia Faruq August 2012 3 Summary of key contributions I outline my contribution to the work conducted and presented as a result of the data and result chapters in this thesis: 1. I developed FReD (Floral Reflectance Database) Version 2.0, an open access database for thousands of flower reflectance spectra, a now well established (and heavily used) resource for evolutionary biologists, pollination ecologists and all scientists interested in signal- received interactions. This was based on a preliminary version of a non-web based database by Sarah Arnold and Lars Chittka. Features of the database are described in Chapter 2 and are available to the public. The database in its present form was fully programmed by me to be later used in modelling bee colour vision and the bee simulations (in Chapter 3, 4, 5, and 6); these include: a. Modelling of flowers under changes of light in the bee colour space, b. Modelling of flowers under assumptions of various receptor spectral sensitivity functions such as the α-band only spectral sensitivity functions and narrowed spectral sensitivity function compared with normal honeybee spectral sensitivity functions – See chapter 3 c. The calculation of perceptual colour shift of flower colours extracted from FReD in the honeybee colour vision model and altered spectral sensitivity of the honeybee described in Appendix I d. The calculation of perceptual colour distances of flower colours in FReD in the bee colour space and altered spectral sensitivity of the honeybee described in Appendix I leading to understanding the relationship between flower colour occurrences and perceptual colour shift in the entire bee colour visual spectrum. e. The development of agent-based modelling with the use of the FReD data to mimic real meadow of flowers leading to understanding the usefulness of colour discrimination under changing illumination compared to perceptual colour shift levels 2. In Chapter 3, I modelled the pattern of perceptual colour shift across the bee colour spectrum under three different illuminations as well as performing analysis of colour shift under altered spectral sensitivity function of the bee. I explored the relationship between perceptual colour shift and colour difference sensitivity in the bee. 3. I modelled an in-silico artificial meadow based on flower distributions of a natural meadow (which consists of five co-occurring flowers based on a field study by Chittka et al, (1997)) 4 in the agent-based simulation environment to measure the performance of the bee-agent based on the amount of nectar collected. In Chapter 4, I analysed this performance against another ideal meadow consisting of flower species with large colour distances between the flower colours under changes of illumination to the extent to which large colour distances between flower colours in a meadow can improve nectar collection under conditions of varying illumination. 4. I developed an algorithm in Mathematica to assign nectar values based on the distribution of real nectar standing crop values to a given flower species that is occurring in the meadow. Nectar standing crop data was collected by K. Pruefert under the Supervision of Prof. Lars Chittka in Germany near Würzburg in 1999. These raw data shown in Appendix III were arranged in a histogram and a log-normal distribution was formed to assign nectar values to flowers in the simulation meadow based on the probability of the distribution of the nectar standing crop values shown in Appendix III. 5. I analysed the performance of a von Kries adaptation mechanism combined with three computational colour constancy mechanisms related to the retinex theory under the agent- based simulation of the honeybee colour vision under varying illumination. I developed the method of using these algorithms in an agent-based model and to apply it into a two- dimensional scene each time the bee moved in the grid of cells, which was the ‘meadow’. The amount of nectar collected in these simulations indicated that performance was best in computational methods of colour constancy when colours in the meadow were distinguishable (i.e. easily discriminable) in the training and testing phase of the simulation. 6. I performed the analysis of the bee-agent model under the natural light changes that affect the performance of the bee, and the affects of a different light condition or flowers in place of the actual conditions found in the Maple forest plant community. Data of the phenology study of the Maple forest plant community were collected by L. Chittka in 1993-4. The reflectance spectra of these flowers come from FReD. 5 Declaration I declare that the work in this thesis is all my own, with the exception of the contributions from others mentioned in the “Summary of key contributions” section. Samia Faruq August 2012 6 Table of Contents ABSTRACT .......................................................................................................................................................... 2 ACKNOWLEDGEMENTS ................................................................................................................................