| 1. |
- Borgefors, Gunilla, et al.
(författare)
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Computing skeletons in three dimensions
- 1999
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Ingår i: Pattern Recognition. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0031-3203 .- 1873-5142. ; 32:7, s. 1225-1236
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Tidskriftsartikel (refereegranskat)abstract
- Skeletonization will probably become as valuable a tool for shape analysis in 3D, as it is in 2D. We present a topology preserving 3D skeletonization method which computes both surface and curve skeletons whose voxels are labelled with the D-6 distance to
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| 2. |
- Granlund, Gösta H.
(författare)
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Statistical Analysis of Chromosome Characteristics
- 1974
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Ingår i: Pattern Recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 6:2, s. 115-126
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Tidskriftsartikel (refereegranskat)abstract
- The advent of new stains for chromosomes has increased the possibility of implementing useful automated chromosome analysis. The case with which chromosomes can now be recognized makes it possible to perform detailed statistical analysis of the chromosomes of an individual. This paper describes methods for assembling chromosome information from several cells in such a way that accidental variations due to preparation, etc. can be eliminated and an undistorted set of characteristics of the chromosome complement can be established. This set of characteristics can then be compared with various references, and statements can be made concerning the relationships between variations in the chromosome complement and genetic traits. These same methods can be employed in multiple-cell karyotyping to circumvent the classical problem of touching and overlapping chromosomes. The methods also allow one to achieve very reliable descriptions of the chromosome complement. The importance of appropriate descriptors of the chromosomes is illustrated.
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| 3. |
- Yu, Jun, 1962-, et al.
(författare)
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Multispectral image classification using wavelets : a simulation study
- 2003
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Ingår i: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 36:4, s. 889-898
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Tidskriftsartikel (refereegranskat)abstract
- This work presents methods for multispectral image classification using the discrete wavelet transform. Performance of some conventional classification methods is evaluated, through a Monte Carlo study, with or without using the wavelet transform. Spatial autocorrelation is present in the computer-generated data on different scenes, and the misclassification rates are compared. The results indicate that the wavelet-based method performs best among the methods under study.
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| 4. |
- Adan, Antonio, et al.
(författare)
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Pattern Recognition Referees 2009
- 2010
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Ingår i: Pattern Recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 43:1, s. 1-4
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Tidskriftsartikel (refereegranskat)
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| 5. |
- Bacauskiene, Marija, et al.
(författare)
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A feature selection technique for generation of classification committees and its application to categorization of laryngeal images
- 2009
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Ingår i: Pattern Recognition. - New York : Pergamon Press. - 0031-3203 .- 1873-5142. ; 42:5, s. 645-654
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Tidskriftsartikel (refereegranskat)abstract
- This paper is concerned with a two phase procedure to select salient features (variables) for classification committees. Both filter and wrapper approaches to feature selection are combined in this work. In the first phase, definitely redundant features are eliminated based on the paired t-test. The test compares the saliency of the candidate and the noise features. In the second phase, the genetic search is employed. The search integrates the steps of training, aggregation of committee members, selection of hyper-parameters, and selection of salient features into the same learning process. A small number of genetic iterations needed to find a solution is the characteristic feature of the genetic search procedure developed. The experimental tests performed on five real-world problems have shown that significant improvements in Classification accuracy can be obtained in a small number of iterations if compared to the case of using all the features available.
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| 6. |
- Barnes, Nick, et al.
(författare)
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The regular polygon detector
- 2010
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Ingår i: Pattern Recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 43:3, s. 592-602
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Tidskriftsartikel (refereegranskat)abstract
- This paper describes a robust regular polygon detector. Given image edges, we derive the a posteriori probability for a mixture of regular polygons, and thus the probability density function for the appearance of a set of regular polygons. Likely regular polygons can be isolated quickly by discretising and collapsing the search space into three dimensions. We derive a complete formulation for efficiently recovering the remaining dimensions using maximum likelihood at the locations of the most likely polygons. Results show robustness to noise, the ability to find and differentiate different shape types, and to perform real-time sign detection for driver assistance.
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| 7. |
- Bernard, Florian, et al.
(författare)
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Synchronisation of partial multi-matchings via non-negative factorisations
- 2019
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Ingår i: Pattern Recognition. - Amsterdam : Elsevier. - 0031-3203 .- 1873-5142. ; 92, s. 146-155
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Tidskriftsartikel (refereegranskat)abstract
- In this work we study permutation synchronisation for the challenging case of partial permutations, which plays an important role for the problem of matching multiple objects (e.g. images or shapes). The term synchronisation refers to the property that the set of pairwise matchings is cycle-consistent, i.e. in the full matching case all compositions of pairwise matchings over cycles must be equal to the identity. Motivated by clustering and matrix factorisation perspectives of cycle-consistency, we derive an algo- rithm to tackle the permutation synchronisation problem based on non-negative factorisations. In order to deal with the inherent non-convexity of the permutation synchronisation problem, we use an initialisation procedure based on a novel rotation scheme applied to the solution of the spectral relaxation. Moreover, this rotation scheme facilitates a convenient Euclidean projection to obtain a binary solution after solving our relaxed problem. In contrast to state-of-the-art methods, our approach is guaranteed to produce cycle-consistent results. We experimentally demonstrate the efficacy of our method and show that it achieves better results compared to existing methods. © 2019 Elsevier Ltd
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| 8. |
- Björnsdotter, Malin, et al.
(författare)
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Clustered sampling improves random subspace brain mapping
- 2012
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Ingår i: Pattern recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 45:6, s. 2035-2040
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Tidskriftsartikel (refereegranskat)abstract
- Intuitive and efficient, the random subspace ensemble approach provides an appealing solution to the problem of the vast dimensionality of functional magnetic resonance imaging (fMRI) data for maximal-accuracy brain state decoding. Recently, efforts to generate biologically plausible and interpretable maps of brain regions which contribute information to the ensemble decoding task have been made and two approaches have been introduced: globally multivariate random subsampling and locally multivariate Monte Carlo mapping. Both types of maps reflect voxel-wise decoding accuracies averaged across repeatedly randomly sampled voxel subsets, highlighting voxels which consistently participate in high-classification subsets. We compare the mapping sensitivities of the approaches on realistic simulated data containing both locally and globally multivariate information and demonstrate that utilizing the inherent volumetric nature of fMRI through clustered Monte Carlo mapping yields dramatically improved performances in terms of voxel detection sensitivity and efficiency. These results suggest that, unless a priori information specifically dictates a global search, variants of clustered sampling should be the priority for random subspace brain mapping.
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| 9. |
- Bäcklin, Christofer, 1983-, et al.
(författare)
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Self-tuning density estimation based on Bayesian averaging of adaptive kernel density estimations yields state-of-the-art performance
- 2018
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Ingår i: Pattern Recognition. - : ELSEVIER SCI LTD. - 0031-3203 .- 1873-5142. ; 78, s. 133-143
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Tidskriftsartikel (refereegranskat)abstract
- Non-parametric probability density function (pdf) estimation is a general problem encountered in many fields. A promising alternative to the dominating solutions, kernel density estimation (KDE) and Gaussian mixture modeling, is adaptive KDE where kernels are given individual bandwidths adjusted to the local data density. Traditionally the bandwidths are selected by a non-linear transformation of a pilot pdf estimate, containing parameters controlling the scaling, but identifying parameters values yielding competitive performance has turned out to be non-trivial. We present a new self-tuning (parameter free) pdf estimation method called adaptive density estimation by Bayesian averaging (ADEBA) that approximates pdf estimates in the form of weighted model averages across all possible parameter values, weighted by their Bayesian posterior calculated from the data. ADEBA is shown to be simple, robust, competitive in comparison to the current practice, and easily generalize to multivariate distributions. An implementation of the method for R is publicly available.
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| 10. |
- Damaschke, Peter, 1963, et al.
(författare)
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Fast algorithms for finding disjoint subsequences with extremal densities
- 2006
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Ingår i: Pattern Recognition. - : Elsevier BV. - 0031-3203. ; 39:12, s. 2281-2292
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Tidskriftsartikel (refereegranskat)abstract
- We derive fast algorithms for the following problem: Given a set of n points on the real line and two parameters s and p, find s disjoint intervals of maximum total length that contain at most p of the given points. Our main contribution consists of algorithms whose time bounds improve upon a straightforward dynamic programming algorithm, in the relevant case that input size n is much bigger than parameters s and p. These results are achievedby selecting a few candidate intervals that are provably sufficient for building an optimal solution via dynamic programming. As a byproduct of this idea we improve an algorithm for a similar subsequence problem of Chen, Lu andTang (2005). The problems are motivated by the search for significant patterns in biological data. Finally we propose several heuristics that further reduce the time complexity in typical instances. One of them leads to an apparently open subsequence sum problem of independent interest.
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