AI Article Synopsis
- The paper introduces a novel method for attribute filtering that merges contrast and structural information to enhance object detail retention while minimizing unwanted background noise.
- The method leverages hyperconnectivity based on k-flat zones and offers a fast algorithm that operates efficiently with a linear complexity in terms of pixels/voxels, outperforming traditional methods, including anisotropic diffusion.
- Testing on various 2D and 3D datasets demonstrates improved noise robustness and retains the benefits of earlier filtering techniques.
Article Abstract
In this paper, we present a new method for attribute filtering, combining contrast and structural information. Using hyperconnectivity based on k-flat zones, we improve the ability of attribute filters to retain internal details in detected objects. Simultaneously, we improve the suppression of small, unwanted detail in the background. We extend the theory of attribute filters to hyperconnectivity and provide a fast algorithm to implement the new method. The new version is only marginally slower than the standard Max-Tree algorithm for connected attribute filters, and linear in the number of pixels or voxels. It is two orders of magnitude faster than anisotropic diffusion. The method is implemented in the form of a filtering rule suitable for handling both increasing (size) and nonincreasing (shape) attributes. We test this new framework on nonincreasing shape filters on both 2D images from astronomy, document processing, and microscopy, and 3D CT scans, and show increased robustness to noise while maintaining the advantages of previous methods.
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| http://dx.doi.org/10.1109/TPAMI.2010.74 | DOI Listing |
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