| 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
Maloof, M.A. and Langley, P. and Sage, S. and Binford, T.O.
ABSTRACT
In this paper, we examine the use of machine learning to improve the robustness of systems for image analysis on the task of roof detection. We review the problem of analyzing aerial photographs, and describe an existing vision system that attempts to automate the identification of buildings in aerial images. After this, we briefly review several well known learning algorithms that represent a wide variety of inductive biases. We report three experiments designed to illuminate facets of applying machine learning methods to the image analysis task. One experiment focuses on within image learning, another deals with the cost of different errors, and a third addresses between image learning. Experimental results demonstrate that machine-learned classifiers meet or exceed the accuracy of handcrafted solutions and that useful generalization occurs when training and testing on data derived from different images. |
|
| Site generated on Friday, 06 January 2006
|