| 1. |
- 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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| 2. |
- 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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| 3. |
- 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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| 4. |
- Ibrahim, Muhammad Talal, et al.
(författare)
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Velocity and pressure-based partitions of horizontal and vertical trajectories for on-line signature verification
- 2010
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Ingår i: Pattern Recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 43:8, s. 2817-2832
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Tidskriftsartikel (refereegranskat)abstract
- In general, shape of an on-line signature is used as a single discriminating feature. Sometimes shape of signature is used alone for verification purposes and sometimes it is used in combination with some other dynamic features such as velocity, pressure and acceleration. The shape of an on-line signature is basically formed due to the wrist and fingers movements where the wrist movement is represented by the horizontal trajectory and the movement of the fingers is represented by vertical trajectory. As the on-line signature is formed due to the combination of two movements that are essentially independent of each other, it will be more effective to use them as two separate discriminating features. Based on this observation, we propose to use these trajectories in isolation by first decomposing the pressure and velocity profiles into two partitions and then extracting the underlying horizontal and vertical trajectories. So the overall process can be thought as the process which exploits the inter-feature dependencies by decomposing signature trajectories depending upon pressure and velocity information and performs verification on each partition separately. As a result, we are able to extract eight discriminating features and among them the most stable discriminating feature is used in verification process. Further Principal Component Analysis (PCA) has been proposed to make the signatures rotation invariant. Experimental results demonstrate superiority of our approach in on-line signature verification in comparison with other techniques.
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| 5. |
- Liu, Jin, et al.
(författare)
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A spatially constrained fuzzy hyper-prototype clustering algorithm
- 2012
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Ingår i: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 45:4, s. 1759-1771
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Tidskriftsartikel (refereegranskat)abstract
- We present in this paper a fuzzy clustering algorithm which can handle spatially constraint problems often encountered in pattern recognition. The proposed method is based on the notions of hyperplanes, the fuzzy c-means, and spatial constraints. By adding a spatial regularizer into the fuzzy hyperplane-based objective function, the proposed method can take into account additionally important information of inherently spatial data. Experimental results have demonstrated that the proposed algorithm achieves superior results to some other popular fuzzy clustering models, and has potential for cluster analysis in spatial domain.
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| 6. |
- 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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| 7. |
- Ma, Zhanyu, et al.
(författare)
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Bayesian estimation of Dirichlet mixture model with variational inference
- 2014
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Ingår i: Pattern Recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 47:9, s. 3143-3157
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Tidskriftsartikel (refereegranskat)abstract
- In statistical modeling, parameter estimation is an essential and challengeable task. Estimation of the parameters in the Dirichlet mixture model (DMM) is analytically intractable, due to the integral expressions of the gamma function and its corresponding derivatives. We introduce a Bayesian estimation strategy to estimate the posterior distribution of the parameters in DMM. By assuming the gamma distribution as the prior to each parameter, we approximate both the prior and the posterior distribution of the parameters with a product of several mutually independent gamma distributions. The extended factorized approximation method is applied to introduce a single lower-bound to the variational objective function and an analytically tractable estimation solution is derived. Moreover, there is only one function that is maximized during iterations and, therefore, the convergence of the proposed algorithm is theoretically guaranteed. With synthesized data, the proposed method shows the advantages over the EM-based method and the previously proposed Bayesian estimation method. With two important multimedia signal processing applications, the good performance of the proposed Bayesian estimation method is demonstrated.
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| 8. |
- Markus, Nenad, et al.
(författare)
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Eye pupil localization with an ensemble of randomized trees
- 2014
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Ingår i: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 47:2, s. 578-587
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Tidskriftsartikel (refereegranskat)abstract
- We describe a method for eye pupil localization based on an ensemble of randomized regression trees and use several publicly available datasets for its quantitative and qualitative evaluation. The method compares well with reported state-of-the-art and runs in real-time on hardware with limited processing power, such as mobile devices.
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| 9. |
- Ng, Theam Foo, et al.
(författare)
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Feature interaction in subspace clustering using the Choquet integral
- 2012
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Ingår i: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 45:7, s. 2645-2660
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Tidskriftsartikel (refereegranskat)abstract
- Subspace clustering has recently emerged as a popular approach to removing irrelevant and redundant features during the clustering process. However, most subspace clustering methods do not consider the interaction between the features. This unawareness limits the analysis performance in many pattern recognition problems. In this paper, we propose a novel subspace clustering technique by introducing the feature interaction using the concepts of fuzzy measures and the Choquet integral. This new framework of subspace clustering can provide optimal subsets of interacted features chosen for each cluster, and hence can improve clustering-based pattern recognition tasks. Various experimental results illustrate the effective performance of the proposed method.
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| 10. |
- Pham, Tuan D
(författare)
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Fuzzy posterior-probabilistic fusion
- 2011
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Ingår i: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 44:5, s. 1023-1030
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Tidskriftsartikel (refereegranskat)abstract
- The paradigm of the permanence of updating ratios, which is a well-proven concept in experimental engineering approximation, has recently been utilized to construct a probabilistic fusion approach for combining knowledge from multiple sources. This ratio-based probabilistic fusion, however, assumes the equal contribution of attributes of diverse evidences. This paper introduces a new framework of a fuzzy probabilistic data fusion using the principles of the permanence of ratios paradigm, and the theories of fuzzy measures and fuzzy integrals. The fuzzy sub-fusion of the proposed approach allows an effective model for incorporating evidence importance and interaction.
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| 11. |
- Pham, Tuan D
(författare)
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GeoEntropy : A measure of complexity and similarity
- 2010
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Ingår i: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 43:3, s. 887-896
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Tidskriftsartikel (refereegranskat)abstract
- Measuring the complexity of a pattern expressed either in time or space has been introduced to quantify the information content of the pattern, which can then be applied for classification. Such information measures are particularly useful for the understanding of systems complexity in many fields of sciences, business and engineering. The novel concept of geostatistical entropy (GeoEntropy) as a measure of pattern complexity and similarity is addressed in this paper. It has been experimentally shown that GeoEntropy is an effective algorithm for studying signal predictability and has superior capability of classifying complex bio-patterns.
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| 12. |
- Su, Ran, et al.
(författare)
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Junction detection for linear structures based on Hessian, correlation and shape information
- 2012
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Ingår i: Pattern Recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 45:10, s. 3695-3706
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Tidskriftsartikel (refereegranskat)abstract
- Junctions have been demonstrated to be important features in many visual tasks such as image registration, matching, and segmentation, as they can provide reliable local information. This paper presents a method for detecting junctions in 2D images with linear structures as well as providing the number of branches and branch orientations. The candidate junction points are selected through a new measurement which combines Hessian information and correlation matrix. Then the locations of the junction centers are refined and the branches of the junctions are found using the intensity information of a stick-shaped window at a number of orientations and the correlation value between the intensity of a local region and a Gaussian-shaped multi-scale stick template. The multi-scale template is used here to detect the structures with various widths. We present the results of our algorithm on images of different types and compare our algorithm with three other methods. The results have shown that the proposed approach can detect junctions more accurately.
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| 13. |
- Verikas, Antanas, et al.
(författare)
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Mining data with random forests : A survey and results of new tests
- 2011
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Ingår i: Pattern Recognition. - Oxford : Pergamon Press. - 0031-3203 .- 1873-5142. ; 44:2, s. 330-349
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Tidskriftsartikel (refereegranskat)abstract
- Random forests (RF) has become a popular technique for classification, prediction, studying variable importance, variable selection, and outlier detection. There are numerous application examples of RF in a variety of fields. Several large scale comparisons including RF have been performed. There are numerous articles, where variable importance evaluations based on the variable importance measures available from RF are used for data exploration and understanding. Apart from the literature survey in RF area, this paper also presents results of new tests regarding variable rankings based on RF variable importance measures. We studied experimentally the consistency and generality of such rankings. Results of the studies indicate that there is no evidence supporting the belief in generality of such rankings. A high variance of variable importance evaluations was observed in the case of small number of trees and small data sets.
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| 14. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Phase congruency-based detection of circular objects applied to analysis of phytoplankton images
- 2012
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Ingår i: Pattern Recognition. - Amsterdam : Elsevier. - 0031-3203 .- 1873-5142. ; 45:4, s. 1659-1670
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Tidskriftsartikel (refereegranskat)abstract
- Detection and recognition of objects representing the Prorocentrum minimum (P. minimum) species in phytoplankton images is the main objective of the article. The species is known to cause harmful blooms in many estuarine and coastal environments. A new technique, combining phase congruency-based detection of circular objects in images, stochastic optimization-based object contour determination, and SVM- as well as random forest (RF)-based classification of objects was developed to solve the task. A set of various features including a subset of new features computed from phase congruency preprocessed images was used to characterize extracted objects. The developed algorithms were tested using 114 images of 1280×960 pixels. There were 2088 P. minimum cells in the images in total. The algorithms were able to detect 93.25% of objects representing P. minimum cells and correctly classified 94.9% of all detected objects. The feature set used has shown considerable tolerance to out-of-focus distortions. The obtained results are rather encouraging and will be used to develop an automated system for obtaining abundance estimates of the species. © 2011 Elsevier Ltd All rights reserved.
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