| ECVision indexed and annotated bibliography of cognitive computer vision publications This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1 The complete text version of this BibTeX file is available here: ECVision_bibliography.bib
Michael C. Burl and Lars Asker and Padhraic Smyth and Usama M. Fayyad and Pietro Perona and Larry Crumpler and Jayne Aubele
ABSTRACT
Dramatic improvements in sensor and image acquisition technology have created a demand for automated tools that can aid in the analysis of large image databases. We describe the development of JARtool, a trainable software system that learns to recognize volcanoes in a large data set of Venusian imagery. A machine learning approach is used because it is much easier for geologists to identify examples of volcanoes in the imagery than it is to specify domain knowledge as a set of pixel level constraints. This approach can also provide portability to other domains without the need for explicit reprogramming; the user simply supplies the system with a new set of training examples. We show how the development of such a system requires a completely different set of skills than are required for applying machine learning to toy world domains. This paper discusses important aspects of the application process not commonly encountered in the toy world, including obtaining labeled training data, the difficulties of working with pixel data, and the automatic extraction of higher level features. |
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