Abstract
This paper presents a bottom-up approach for fast segmentation of natural images. This approach has two main stages: firstly, it detects the homogeneous regions of the input image using a colour-based distance and then, it merges these regions using a more complex distance. Basically, this distance complements a contrast measure defined between regions with internal region descriptors and with attributes of the shared boundary. These two stages are performed over the same hierarchical framework: the Bounded Irregular Pyramid (BIP). The performance of the proposed algorithm has been quantitatively evaluated with respect to ground-truth segmentation data.
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Marfil, R., Bandera, A., Sandoval, F. (2007). Perception-Based Image Segmentation Using the Bounded Irregular Pyramid. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds) Pattern Recognition. DAGM 2007. Lecture Notes in Computer Science, vol 4713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74936-3_25
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DOI: https://doi.org/10.1007/978-3-540-74936-3_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74933-2
Online ISBN: 978-3-540-74936-3
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