Morphidas: Progress report

Transcription

1 Morphidas: Progress report David Corney 14 th April

2 Outline Creating the Tilia dataset First steps at automatic analysis Finding the colour checkers Finding the leaves Next steps

3 Photographing Tilia at Kew Herbarium 1897 specimens - all now photographed! c. 25 species includes c. 500 in cultivated folders still typing up the index (inc. metadata) Affordable kit and standard method Allows repetition and extension by us and by other groups Can also compare images with scanned images

4 Canon 50D DSLR (15.1 megapixels) 50mm f/2.5 macro lens (low distortion) Narrow aperture (f11) so everything is in focus Diffuser screen used to minimise cast shadows The equipment

5 The Tilia at Kew

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13 The dataset Total of 1897 images c.10 Gb (JPEG) c.35 Gb (Canon raw CR2 format) 15.1 Mp 300 dpi Every specimen of Tilia photographed Not just the good ones, so less bias in our results We can say how easy/hard automatic analysis of herbarium specimens is Unique set? E.g. Columbia herbarium group discuss digitising herbarium specimens, but actually only use singleleaf images (Belhumeur et al, 2008)

14 Finding colour checkers We added a Gretag Macbeth mini colour checker to each specimen Allows colour & lightness correction, despite varying levels of sunlight during photography Checker maybe only partially visible in image Need to find them automatically in every photo Algorithm looks for high-contrast points in the image Then looks for subsets that are in a grid pattern Then fits a fixed-size rectangle Later: locate the scale ruler and normalise image sizes

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16 Finding leaves Overall strategy: First, find leaves in each image Then analyse their shapes using a different method as appropriate Evaluation Target 1: find one leaf in every image Target 2: find every leaf in every image

17 Deformable templates Manually choose a typical leaf Find its outline to define the initial template Define a series of deformations of the template Vary the view Rotate, translate and re-scale Find where the deformed outlines match edges in target image Minimise the sum: (deformation) + (edge mismatch) E.g. Jain et al., 1996

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19 Energy landscape Strongest edges in original image are defined as lowest energy Shown in black Allows gradient descent optimisation algorithms to find matches

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21 Plans to improve leaf-finding Choose range of template leaves One per species? Represent using Fourier analysis? Include veins? Define allowable deformations Constrain using domain knowledge E.g. maintaining teeth shapes Choose optimisation algorithms E.g. genetic algorithms

22 Plans for subsequent analysis After we ve found some leaf outlines Further templates Elliptic Fourier analysis Time series shapelets (Eamonn Keogh)

23 and then Once we ve found the leaves in each image, and the outline of each leaf, we can Find distributions of various morphometric measurements, within and between taxa Identify species automatically Restore damaged/occluded leaves Automatically find & measure teeth Relate shape/margin to climate

24 Some references Jain, A.K., Zhong, Y. & Lakshmanan, S., Object Matching Using Deformable Templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(3), Ye, L. & Keogh, E., Time series shapelets: a new primitive for data mining. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. Paris, France: ACM, pp Belhumeur, P. et al., Searching the World's Herbaria: A System for Visual Identification of Plant Species. In Computer Vision - ECCV pp Belhumeur, P., Chen, D., Feiner, S., Jacobs, D., Kress, W. J., Ling, H., Lopez, I., Ramamoorthi, R., Sheorey, S., White, S., and Zhang, L Agarwal, G. et al., First steps toward an electronic field guide for plants. Taxon, 55(3), 597 Agarwal, G., Belhumeur, P., Feiner, S., Jacobs, D., Kress, W.J., Ramamoorthi, R., Bourg, N., Dixit, N., Ling, H., Mahajan, D.,Russell, R., Shirdhonkar, S., Sunkavalli, K., and White, S.