Abstract
This paper describes an approach to recognize building surfaces. A building image is analyzed to extract the natural characters such as the surfaces and their areas, vanishing points, wall region and a list of SIFT feature vectors. These characters are organized as a hierarchical system of features to describe a model of building and then stored in a database. Given a new image, the characters are computed in the same form with in database. Then the new image is compared against the database to choose the best candidate. A cross ratio based algorithm, a novel approach, is used to verify the correct match. Finally, the correct match is used to update the model of building. The experiments show that the approach method clearly decreases the size of database, obtains high recognition rate. Furthermore, the problem of multiple buildings can be solved by separately analyzing each surface of building.
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Trinh, HH., Kim, DN., Jo, KH. (2008). Building Surface Refinement Using Cluster of Repeated Local Features by Cross Ratio. In: Nguyen, N.T., Borzemski, L., Grzech, A., Ali, M. (eds) New Frontiers in Applied Artificial Intelligence. IEA/AIE 2008. Lecture Notes in Computer Science(), vol 5027. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69052-8_3
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DOI: https://doi.org/10.1007/978-3-540-69052-8_3
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