Analyses of Honeybee Color Vision Using Spectral Images
Date Issued
2008
Date
2008
Author(s)
Chiang, Shou-Shan
Abstract
The objective of this research is to simulate and analyze the neural mechanisms of honeybee color vision using image processing technique, and to explain the relationship between the intercellular recording data and the color discrimination function. There are over 19 COC patterns in the former but only 2 patterns in the later. In order to acquire images matching to the spectral sensitivities of the honeybee''s photoreceptors, a CCD with ultraviolet sensitivity was employed with specific UV filter, blue light filter, and green light filter. The images of flowers and spiders which are usually seen by honeybees in the nature were acquired and analyzed. Image fusion with those spectral images according to the neural mechanisms of honeybee color vision was employed in this study. There are 27 patterns of color-coding neurons, including hypothesized patterns and real recorded neurons. Because the multi spectral neural mechanisms are more important, 7 patterns (narrow-band neurons) are ignored while the remained 20 patterns (board-band and color opponent neurons) were used for images fusion with linear or nonlinear COC patterns. After computing the correlation and the difference of texture features between two images in the image sets, these images were clustered by "affinity propagation" clustering algorithm. The experimental results reveal that there are essentially 2 clusters in the 20 COC patterns. This results is consistent with the finding from the behavior experiments in the existing literature.
Subjects
Color vision
Color coding
Color opponency
Honeybee
Classification
Multi-spectral image
Affinity propagation
Type
thesis
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