| 1. |
- Dražić, Slobodan, et al.
(author)
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Precise Estimation of the Projection of a Shape from a Pixel Coverage Representation
- 2011
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In: Proceedings of the 7th IEEE International Symposium on Image and Signal Processing and Analysis (ISPA). - : IEEE Computer Society. - 9781457708411 ; , s. 569-574, s. 569-574
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Conference paper (peer-reviewed)abstract
- Measuring width and diameter of a shape areproblems well studied in the literature. A pixel coverage repre-sentation is one specific type of digital fuzzy representation of acontinuous image object, where the (membership) value of eachpixel is (approximately) equal to the relative area of the pixelwhich is covered by the continuous object. Lately a number ofmethods for shape analysis use pixel coverage for reducing errorof estimation. We introduce a novel method for estimating theprojection of a shape in a given direction. The method is based onutilizing pixel coverage representation of a shape. Performance ofthe method is evaluated by a number of tests on synthetic objects,confirming high precision and applicability for calculation ofdiameter and elongation of a shape.
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| 2. |
- Lindblad, Joakim
(author)
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Coverage segmentation of thin structures by linear unmixing and local centre of gravity attraction
- 2013
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In: Image And Signal Processing And Analysis. - 1845-5921. ; , s. 83-
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Conference paper (peer-reviewed)abstract
- We present a coverage segmentation method for extracting thin structures in two-dimensional images. These thin structures can be, for example, retinal vessels, or microtubules in cytoskeleton, which are often 1-2 pixels thick. There exist several methods for coverage segmentation, but when it comes to thin and long structures, the segmentation is often unreliable.We propose a method that does not shrink the structures inappropriately and creates a trustworthy segmentation. In addition, as a by-product a high-resolution crisp reconstruction is provided. The method needs a reliable crisp segmentation as an input and uses information from linear unmixing and the crisp segmentation to create a high-resolution crisp reconstruction of the object. After a procedure where holes and protrusions are removed, the high-resolution crisp image is optionally down-sampled back to its original size, creating a coverage segmentation that preserves thin structures.
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