Abstract
Object learning is an important problem in machine vision with direct implications on the ability of a computer to understand an image. The goal of this paper is to demonstrate an object learning-detection-segmentation-matching paradigm (Fig. 1) meant to facilitate image understanding by computers. We will show how various types of objects can be learned and subsequently retrieved from gray level images without attempting to completely partition and label the image.
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Duta, N., Jain, A.K. (2001). Learning-Based Detection, Segmentation and Matching of Objects. In: Singh, S., Murshed, N., Kropatsch, W. (eds) Advances in Pattern Recognition — ICAPR 2001. ICAPR 2001. Lecture Notes in Computer Science, vol 2013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44732-6_45
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DOI: https://doi.org/10.1007/3-540-44732-6_45
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