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Differential Oriented image Foresting Transform and Its Applications to Support High-level Priors for Object segmentation

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JOURNAL OF MATHEMATICAL IMAGING AND VISION 2023年第5期65卷 802-817页

作者： Condori, Marcos A. T. Miranda, Paulo A. V. Univ Sao Paulo Inst Math & Stat R Matao 1010 BR-05508090 Sao Paulo SP Brazil

image foresting transform (IFT) is a graph-based framework to develop image operators based on optimum connectivity between a root set and the remaining nodes, according to a given path-cost function. Oriented image foresting transform (OIFT) was proposed as an extension of some seeded IFT-based segmentation methods to directed graphs, enabling them to support the processing of global object properties, such as connectedness, shape constraints, boundary polarity, and hierarchical constraints, allowing their customization to a given target object. OIFT lies in the intersection of generalized graph cut and general fuzzy connectedness frameworks, inheriting their properties. Its returned segmentation is optimal, with respect to an appropriate graph cut measure, among all segmentations satisfying the given constraints. In this work, we propose differential oriented image foresting transform, which allows multiple OIFT executions for different root sets, making the processing time proportional to the number of modified nodes. Experimental results show considerable efficiency gains over the sequential flow of OIFTs in image segmentation, while maintaining a good treatment of tie zones. We also demonstrate that the differential flow makes it feasible to incorporate the prior knowledge about the maximum allowable size for the segmented object, thus avoiding false positive errors in the segmentation of multi-dimensional images. We also propose an algorithm to efficiently create a hierarchy map that encodes area-constrained OIFT results for all possible thresholds, facilitating the quick selection of the object of interest.

关键词： Oriented image foresting transform image segmentation in directed graphs Generalized graph cut Differential algorithms

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Differential Oriented image Foresting Transform segmentation by Seed Competition 2nd

Differential Oriented Image Foresting Transform Segmentation...

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2nd International Conference on Discrete Geometry and Mathematical Morphology (DGMM)

作者： Condori, Marcos A. T. Miranda, Paulo A., V Univ Sao Paulo Inst Math & Stat BR-05508090 Sao Paulo SP Brazil

ISBN: (纸本)9783031198960;9783031198977

The image Foresting Transform (IFT) is a graph-based framework to develop image operators based on optimum connectivity between a root set and the remaining nodes, according to a given path-cost function. Oriented image Foresting Transform (OIFT) was proposed as an extension of some IFT-based segmentation methods to directed graphs, enabling them to support the processing of global object properties, such as connectedness, shape constraints, boundary polarity, and hierarchical constraints, allowing their customization to a given target object. OIFT lies in the intersection of the Generalized Graph Cut and the General Fuzzy Connectedness frameworks, inheriting their properties. Its returned segmentation is optimal, with respect to an appropriate graph cut measure, among all segmentations satisfying the given constraints. In this work, we propose the Differential Oriented image Foresting Transform (DOIFT), which allows multiple OIFT executions for different root sets, making the processing time proportional to the number of modified nodes. Experimental results show considerable efficiency gains over the sequential flow of OIFTs in image segmentation, while maintaining a good treatment of tie zones. We also demonstrate that the differential flow makes it feasible to incorporate area constraints in OIFT segmentation of multi-dimensional images.

关键词： Oriented image Foresting Transform image segmentation in directed graphs Generalized Graph Cut Differential algorithms

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