| 31. |
- Luengo, Cris
(författare)
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Constrained and Dimensionality-Independent Path Openings
- 2010
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Ingår i: IEEE Transactions on Image Processing. - 1057-7149 .- 1941-0042. ; 19, s. 1587-1595
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Tidskriftsartikel (refereegranskat)abstract
- Path openings and closings are morphological operations with flexible line segments as structuring elements. These line segments have the ability to adapt to local image structures, and can be used to detect lines that are not perfectly straight. They also are a convenient and efficient alternative to straight line segments as structuring elements when the exact orientation of lines in the image is not known. These path operations are defined by an adjacency relation, which typically allows for lines that are approximately horizontal, vertical or diagonal. However, because this definition allows zig-zag lines, diagonal paths can be much shorter than the corresponding horizontal or vertical paths. This undoubtedly causes problems when attempting to use path operations for length measurements. This paper 1) introduces a dimensionality-independent implementation of the path opening and closing algorithm by Appleton and Talbot, 2) proposes a constraint on the path operations to improve their ability to perform length measurements, and 3) shows how to use path openings and closings in a granulometry to obtain the length distribution of elongated structures directly from a gray-value image, without a need for binarizing the image and identifying individual objects.
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| 32. |
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| 33. |
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| 34. |
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| 35. |
- Luengo, Cris, et al.
(författare)
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Rapid prototyping of image analysis applications
- 2011
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Ingår i: Medical Image Processing: Techniques and Applications. - New York, NY : Springer New York. - 9781441997692 ; , s. 5-25
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Bokkapitel (övrigt vetenskapligt/konstnärligt)
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| 36. |
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| 37. |
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| 38. |
- Luengo Hendriks, Cris L.
(författare)
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Path openings and their applications
- 2010
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Ingår i: Proceedings SSBA 2010. - Uppsala : Centre for Image Analysis. ; , s. 79-82
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)
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| 39. |
- Luengo Hendriks, Cris L., 1974-
(författare)
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Revisiting priority queues for image analysis
- 2010
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Ingår i: Pattern Recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 43:9, s. 3003-3012
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Tidskriftsartikel (refereegranskat)abstract
- Many algorithms in image analysis require a priority queue, a data structure that holds pointers to pixels in the image, and which allows efficiently finding the pixel in the queue with the highest priority. However, very few articles describing such image analysis algorithms specify which implementation of the priority queue was used. Many assessments of priority queues can be found in the literature, but mostly in the context of numerical simulation rather than image analysis. Furthermore, due to the ever-changing characteristics of computing hardware, performance evaluated empirically 10 years ago is no longer relevant. In this paper I revisit priority queues as used in image analysis routines, evaluate their performance in a very general setting, and come to a very different conclusion than other authors: implicit heaps are the most efficient priority queues. At the same time. I propose a simple modification of the hierarchical queue (or bucket queue) that is more efficient than the implicit heap for extremely large queues.
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| 40. |
- Malmberg, Filip, et al.
(författare)
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An efficient algorithm for exact evaluation of stochastic watersheds
- 2014
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Ingår i: Pattern Recognition Letters. - : Elsevier BV. - 0167-8655 .- 1872-7344. ; 47, s. 80-84
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Tidskriftsartikel (refereegranskat)abstract
- The stochastic watershed is a method for unsupervised image segmentation proposed by Angulo and Jeulin (2007). The method first computes a probability density function (PDF), assigning to each piece of contour in the image the probability to appear as a segmentation boundary in seeded watershed segmentation with randomly selected seeds. Contours that appear with high probability are assumed to be more important. This PDF is then post-processed to obtain a final segmentation. The main computational hurdle with the stochastic watershed method is the calculation of the PDF. In the original publication by Angulo and Jeulin, the PDF was estimated by Monte Carlo simulation, i.e., repeatedly selecting random markers and performing seeded watershed segmentation. Meyer and Stawiaski (2010) showed that the PDF can be calculated exactly, without performing any Monte Carlo simulations, but do not provide any implementation details. In a naive implementation, the computational cost of their method is too high to make it useful in practice. Here, we extend the work of Meyer and Stawiaski by presenting an efficient (quasi-linear) algorithm for exact computation of the PDF. We demonstrate that in practice, the proposed method is faster than any previously reported method by more than two orders of magnitude. The algorithm is formulated for general undirected graphs, and thus trivially generalizes to images with any number of dimensions.
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