2012
DOI: 10.1109/tpami.2011.267
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Detecting Curves with Unknown Endpoints and Arbitrary Topology Using Minimal Paths

Abstract: Existing state-of-the-art minimal path techniques work well to extract simple open curves in images when both endpoints of the curve are given as user input or when one input is given and the total length of the curve is known in advance. Curves which branch require even further prior input from the user, namely, each branch endpoint. In this work, we present a novel minimal path-based algorithm which works on much more general curve topologies with far fewer demands on the user for initial input compared to p… Show more

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Cited by 104 publications
(67 citation statements)
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“…MCP with keypoint detection is used in [109], [120], [117], [104], [110]. Instead of retrieving the MCP between the source and the end point, new intermediate points, called keypoints, are found imposing constraints on the length of the path between consecutive keypoints.…”
Section: B Minimum Cost Pathmentioning
confidence: 99%
“…MCP with keypoint detection is used in [109], [120], [117], [104], [110]. Instead of retrieving the MCP between the source and the end point, new intermediate points, called keypoints, are found imposing constraints on the length of the path between consecutive keypoints.…”
Section: B Minimum Cost Pathmentioning
confidence: 99%
“…A recent variant of the Fast Marching technique in [14], which is labelled Self Terminating Fast Marching (STFM) in this paper, requires initializing the path finding at one endpoint only, the other endpoint of the crack being automatically determined by some stopping criteria. The path finding can propagate to the branches of a crack at some extent.…”
Section: Path Finding Techniques For Semi-automatic Pgt Data Colmentioning
confidence: 99%
“…In [6] Sheraz et al proposed automatic word ground truth creation of camera captured document images using a document image retrieval system. In [7] Vivek et al created and tested a method for curve detection by means of synthetically formed curves. Validation and evaluation of magnetic resonance images (MRI) segmentation is a difficult task due to shortage of reliable ground truth information.…”
Section: Literature Reviewmentioning
confidence: 99%