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Discrete Geometry and Mathematical Morphology : First International Joint Conference, DGMM 2021, Uppsala, Sweden, May 24–27, 2021, Proceedings
- 2021
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Samlingsverk (redaktörskap) (refereegranskat)abstract
- This book constitutes the proceedings of the First IAPR International Conference on Discrete Geometry and Mathematical Morphology, DGMM 2021, which was held during May 24-27, 2021, in Uppsala, Sweden.The conference was created by joining the International Conference on Discrete Geometry for computer Imagery, DGCI, with the International Symposium on Mathematical Morphology, ISMM.The 36 papers included in this volume were carefully reviewed and selected from 59 submissions. They were organized in topical sections as follows: applications in image processing, computer vision, and pattern recognition; discrete and combinatorial topology; discrete geometry - models, transforms, visualization; discrete tomography and inverse problems; hierarchical and graph-based models, analysis and segmentation; learning-based approaches to mathematical morphology; multivariate and PDE-based mathematical morphology, morphological filtering.The book also contains 3 invited keynote papers.
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| 2. |
- Curic, Vladimir, 1981-, et al.
(författare)
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Distance measures between digital fuzzy objects and their applicability in image processing
- 2011
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Ingår i: Combinatorial Image Analysis. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642210723 ; 6636, s. 385-397
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Konferensbidrag (refereegranskat)abstract
- We present two different extensions of the Sum of minimal distances and the Complement weighted sum of minimal distances to distances between fuzzy sets. We evaluate to what extent the proposed distances show monotonic behavior with respect to increasing translation and rotation of digital objects, in noise free, as well as in noisy conditions. Tests show that one of the extension approaches leads to distances exhibiting very good performance. Furthermore, we evaluate distance based classification of crisp and fuzzy representations of objects at a range of resolutions. We conclude that the proposed distances are able to utilize the additional information available in a fuzzy representation, thereby leading to improved performance of related image processing tasks.
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- Dražić, Slobodan, et al.
(författare)
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Precise Estimation of the Projection of a Shape from a Pixel Coverage Representation
- 2011
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Ingår i: 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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Konferensbidrag (refereegranskat)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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- Öfverstedt, Johan, et al.
(författare)
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Stochastic Distance Transform : Theory, Algorithms and Applications
- 2020
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Ingår i: Journal of Mathematical Imaging and Vision. - : Springer Science and Business Media LLC. - 0924-9907 .- 1573-7683. ; 62, s. 751-769
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Tidskriftsartikel (refereegranskat)abstract
- Distance transforms (DTs) are standard tools in image analysis, with applications in image registration and segmentation. The DT is based on extremal (minimal) distance values and is therefore highly sensitive to noise. We present a stochastic distance transform (SDT) based on discrete random sets, in which a model of element-wise probability is utilized and the SDT is computed as the first moment of the distance distribution to the random set. We present two methods for computing the SDT and analyze them w.r.t. accuracy and complexity. Further, we propose a method, utilizing kernel density estimation, for estimating probability functions and associated random sets to use with the SDT. We evaluate the accuracy of the SDT and the proposed framework on images of thin line structures and disks corrupted by salt and pepper noise and observe excellent performance. We also insert the SDT into a segmentation framework and apply it to overlapping objects, where it provides substantially improved performance over previous methods. Finally, we evaluate the SDT and observe very good performance, on simulated images from localization microscopy, a state-of-the-art super-resolution microscopy technique which yields highly spatially localized but noisy point-clouds.
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| 7. |
- Sladoje, Natasa, et al.
(författare)
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Distance Between Vector-Valued Representations of Objects in Images with Application in Object Detection and Classification
- 2017
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Ingår i: In Proc. of the 18th International Workshop on Combinatorial Image Analysis, IWCIA2017. - Cham : Springer. - 9783319591070 - 9783319591087 ; , s. 243-255
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Konferensbidrag (refereegranskat)abstract
- We present a novel approach to measuring distances between objects in images, suitable for information-rich object representations which simultaneously capture several properties in each image pixel. Multiple spatial fuzzy sets on the image domain, unified in a vector-valued fuzzy set, are used to model such representations. Distance between such sets is based on a novel point-to-set distance suitable for vector-valued fuzzy representations. The proposed set distance may be applied in, e.g., template matching and object classification, with an advantage that a number of object features are simultaneously considered. The distance measure is of linear time complexity w.r.t. the number of pixels in the image. We evaluate the performance of the proposed measure in template matching in presence of noise, as well as in object detection and classification in low resolution Transmission Electron Microscopy images.
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| 8. |
- Sladoje, Nataša
(författare)
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Ellipses estimation from their digitization
- 1997
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Ingår i: International Conference on Discrete Geometry for Computer Imagery. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783540638841 ; , s. 187-198
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Konferensbidrag (refereegranskat)
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| 9. |
- Sladoje, Natasa, et al.
(författare)
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Estimation of moments of digitized objects with fuzzy borders
- 2005
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Ingår i: Proc. of 13th International Conference on Image Analysis and Processing. - Berlin, Heidelberg : Springer. - 9783540318668 ; , s. 188-195
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Konferensbidrag (refereegranskat)abstract
- Error bounds for estimation of moments from a fuzzy representation of a shape are derived, and compared with estimations from a crisp representation. It is shown that a fuzzy membership function based on the pixel area coverage provides higher accuracy of the estimates, compared to binary Gauss digitization at the same spatial image resolution. Theoretical results are confirmed by a statistical study of disks and squares, where the moments of the shape, up to order two, are estimated from its fuzzy discrete representation. The errors of the estimates decrease both with increased size of a shape (spatial resolution) and increased membership resolution (number of available grey-levels).
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