| 1. |
- Curic, Vladimir, et al.
(författare)
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Adaptive hit or miss transform
- 2015
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Ingår i: Mathematical Morphology and Its Applications to Signal and Image Processing. - Cham : Springer. - 9783319187198 ; , s. 741-752
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Konferensbidrag (refereegranskat)abstract
- The Hit or Miss Transform is a fundamental morphological operator, and can be used for template matching. In this paper, we present a framework for adaptive Hit or Miss Transform, where structuring elements are adaptive with respect to the input image itself. We illustrate the difference between the new adaptive Hit or Miss Transform and the classical Hit or Miss Transform. As an example of its usefulness, we show how the new adaptive Hit or Miss Transform can detect particles in single molecule imaging.
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| 2. |
- Landström, Anders
(författare)
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An Approach to Adaptive Quadratic Structuring Functions Based on the Local Structure Tensor
- 2015
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Ingår i: Mathematical Morphology and Its Applications to Signal and Image Processing. - Cham : Encyclopedia of Global Archaeology/Springer Verlag. - 9783319187198 - 9783319187204 ; , s. 729-740
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Konferensbidrag (refereegranskat)abstract
- Classical morphological image processing, where the same structuring element is used to process the whole image, has its limitations. Consequently, adaptive mathematical morphology is attracting more and more attention.So far, however, the use of non-flat adaptive structuring functions is very limited. This work presents a method for defining quadratic structuring functions from the well known local structure tensor, building on previous work for flat adaptive morphology. The result is a novel approach to adaptive mathematical morphology, suitable for enhancement and linking of directional features in images. Moreover, the presented strategy can be quite efficiently implemented and is easy to use as it relies on just two user-set parameters which are directly related to image measures.
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| 3. |
- Lindblad, Joakim, et al.
(författare)
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Exact linear time Euclidean distance transforms of grid line sampled shapes
- 2015
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Ingår i: Mathematical Morphology and its Applications to Signal and Image Processing. - Cham : Springer. - 9783319187198 ; , s. 645-656
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Konferensbidrag (refereegranskat)abstract
- We propose a method for computing, in linear time, the exact Euclidean distance transform of sets of points s. t. one coordinate of a point can be assigned any real value, whereas other coordinates are restricted to discrete sets of values. The proposed distance transform is applicable to objects represented by grid line sampling, and readily provides sub-pixel precise distance values. The algorithm is easy to implement; we present complete pseudo code. The method is easy to parallelize and extend to higher dimensional data. We present two ways of obtaining approximate grid line sampled representations, and evaluate the proposed EDT on synthetic examples. The method is competitive w. r. t. state-of-the-art methods for sub-pixel precise distance evaluation.
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| 4. |
- Selig, Bettina, et al.
(författare)
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Fast evaluation of the robust stochastic watershed
- 2015
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Ingår i: Mathematical Morphology and Its Applications to Signal and Image Processing. - Cham : Springer. - 9783319187198 ; 9082, s. 705-716
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Konferensbidrag (refereegranskat)abstract
- The stochastic watershed is a segmentation algorithm that estimates the importance of each boundary by repeatedly segmenting the image using a watershed with randomly placed seeds. Recently, this algorithm was further developed in two directions: (1) The exact evaluation algorithm efficiently produces the result of the stochastic watershed with an infinite number of repetitions. This algorithm computes the probability for each boundary to be found by a watershed with random seeds, making the result deterministic and much faster. (2) The robust stochastic watershed improves the usefulness of the segmentation result by avoiding false edges in large regions of uniform intensity. This algorithm simply adds noise to the input image for each repetition of the watershed with random seeds. In this paper, we combine these two algorithms into a method that produces a segmentation result comparable to the robust stochastic watershed, with a considerably reduced computation time. We propose to run the exact evaluation algorithm three times, with uniform noise added to the input image, to produce three different estimates of probabilities for the edges. We combine these three estimates with the geometric mean. In a relatively simple segmentation problem, F-measures averaged over the results on 46 images were identical to those of the robust stochastic watershed, but the computation times were an order of magnitude shorter.
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