Building an Intelligent Knowledgebase of Brachiopod Paleontology
University of Kansas
Electrical Engineering & Computer Science
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Science advances not only because of new discoveries, but also due to revolutionary ideas drawn from accumulated data. The quality of studies in paleontology, in particular, depends on accessibility of fossil data. This research builds an intelligent system based on brachiopod fossil images and their descriptions published in Treatise on Invertebrate Paleontology. The project is still on going and some significant developments will be discussed here. This thesis has two major parts. The first part describes the digitization, organization and integration of information extracted from the Treatise. The Treatise is in PDF format and it is non-trivial to convert large volumes into a structured, easily accessible digital library. Three important topics will be discussed: (1) how to extract data entries from the text, and save them in a structured manner; (2) how to crop individual specimen images from figures automatically, and associate each image with text entries; (3) how to build a search engine to perform both keyword search and natural language search. The search engine already has a web interface and many useful tasks can be done with ease. Verbal descriptions are second-hand information of fossil images and thus have limitations. The second part of the thesis develops an algorithm to compare fossil images directly, without referring to textual information. After similarities between fossil images are calculated, we can use the results in image search, fossil classification, and so on. The algorithm is based on deformable templates, and utilizes expectation propagation to find the optimal deformation. Specifically, I superimpose a warp on each image. Each node of the warp encapsulates a vector of local texture features, and comparing two images involves two steps: (1) deform the warp to the optimal configuration, so the energy function is minimized; and (2) based on the optimal configuration, compute the distance of two images. Experiment results confirmed that the method is reasonable and robust.
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