Abstract
Gaining the understanding of objects present in the surrounding environment is necessary to perform many fundamental tasks. Human vision systems utilize the contour information of objects to perform identification of objects and use prior learnings for their classification. However, computer vision systems still face many limitations in object analysis and classification. The crux of the problem in computer vision systems is identifying and grouping edges which correspond to the object contour and rejecting those which correspond to finer details.
The approach proposed in this work aims to eliminate this edge selection and analysis and instead generate run length codes which correspond to different contour patterns. These codes would then be useful to classify various objects identified. The approach has been successfully applied for day time vehicle detection.
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Kanitkar, A.R., Bharti, B.K., Hivarkar, U.N. (2011). Object Classification Using Encoded Edge Based Structural Information. In: Abraham, A., Mauri, J.L., Buford, J.F., Suzuki, J., Thampi, S.M. (eds) Advances in Computing and Communications. ACC 2011. Communications in Computer and Information Science, vol 193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22726-4_38
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DOI: https://doi.org/10.1007/978-3-642-22726-4_38
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