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Enumeration	Reference	Cover	Find
1.	Borgefors, Gunilla, et al. (author) Computing skeletons in three dimensions 1999 In: Pattern Recognition. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0031-3203 .- 1873-5142. ; 32:7, s. 1225-1236 Journal article (peer-reviewed)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
2.	Granlund, Gösta H. (author) Statistical Analysis of Chromosome Characteristics 1974 In: Pattern Recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 6:2, s. 115-126 Journal article (peer-reviewed)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.
3.	Yu, Jun, 1962-, et al. (author) Multispectral image classification using wavelets : a simulation study 2003 In: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 36:4, s. 889-898 Journal article (peer-reviewed)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.
4.	Adan, Antonio, et al. (author) Pattern Recognition Referees 2009 2010 In: Pattern Recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 43:1, s. 1-4 Journal article (peer-reviewed)
5.	Bacauskiene, Marija, et al. (author) A feature selection technique for generation of classification committees and its application to categorization of laryngeal images 2009 In: Pattern Recognition. - New York : Pergamon Press. - 0031-3203 .- 1873-5142. ; 42:5, s. 645-654 Journal article (peer-reviewed)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.
6.	Barnes, Nick, et al. (author) The regular polygon detector 2010 In: Pattern Recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 43:3, s. 592-602 Journal article (peer-reviewed)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.
7.	Bernard, Florian, et al. (author) Synchronisation of partial multi-matchings via non-negative factorisations 2019 In: Pattern Recognition. - Amsterdam : Elsevier. - 0031-3203 .- 1873-5142. ; 92, s. 146-155 Journal article (peer-reviewed)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
8.	Björnsdotter, Malin, et al. (author) Clustered sampling improves random subspace brain mapping 2012 In: Pattern recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 45:6, s. 2035-2040 Journal article (peer-reviewed)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.
9.	Bäcklin, Christofer, 1983-, et al. (author) Self-tuning density estimation based on Bayesian averaging of adaptive kernel density estimations yields state-of-the-art performance 2018 In: Pattern Recognition. - : ELSEVIER SCI LTD. - 0031-3203 .- 1873-5142. ; 78, s. 133-143 Journal article (peer-reviewed)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.
10.	Danielis, Alessandro, et al. (author) Lip segmentation based on Lambertian shadings and morphological operators for hyper-spectral images 2017 In: Pattern Recognition. - : ELSEVIER SCI LTD. - 0031-3203 .- 1873-5142. ; 63, s. 355-370 Journal article (peer-reviewed)abstract Lip segmentation is a non-trivial task because the colour difference between the lip and the skin regions maybe not so noticeable sometimes. We propose an automatic lip segmentation technique for hyper-spectral images from an imaging prototype with medical applications. Contrarily to many other existing lip segmentation methods, we do not use colour space transformations to localise the lip area. As input image, we use for the first time a parametric blood concentration map computed by using narrow spectral bands. Our method mainly consists of three phases: (i) for each subject generate a subset of face images enhanced by different simulated Lambertian illuminations, then (ii) perform lip segmentation on each enhanced image by using constrained morphological operations, and finally (iii) extract features from Fourier-based modeled lip boundaries for selecting the lip candidate. Experiments for testing our approach are performed under controlled conditions on volunteers and on a public hyper-spectral dataset. Results show the effectiveness of the algorithm against low spectral range, moustache, and noise.
11.	Fierrez-Aguilar, Julian, et al. (author) Discriminative multimodal biometric authentication based on quality measures 2005 In: Pattern Recognition. - Oxford : Pergamon Press. - 0031-3203 .- 1873-5142. ; 38:5, s. 777-779 Journal article (peer-reviewed)abstract A novel score-level fusion strategy based on quality measures for multimodal biometric authentication is presented. In the proposed method, the fusion function is adapted every time an authentication claim is performed based on the estimated quality of the sensed biometric signals at this time. Experimental results combining written signatures and quality-labelled fingerprints are reported. The proposed scheme is shown to outperform significantly the fusion approach without considering quality signals. In particular, a relative improvement of approximately 20% is obtained on the publicly available MCYT bimodal database.
12.	Fouard, Céline, et al. (author) Weighted distance transforms generalized to modules and their computation on point lattices 2007 In: Pattern Recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 40:9, s. 2453-2474 Journal article (peer-reviewed)abstract This paper presents the generalization of weighted distances to modules and their computation through the chamfer algorithm on general point lattices. The first part is dedicated to formalization of definitions and properties (distance, metric, norm) of weighted distances on modules. It resumes tools found in literature to express the weighted distance of any point of a module and to compute optimal weights in the general case to get rotation invariant distances. The second part of this paper proves that, for any point lattice, the sequential two-scan chamfer algorithm produces correct distance maps. Finally, the definitions and computation of weighted distances are applied to the face-centered cubic (FCC) and body-centered cubic (BCC) grids.
13.	Gao, Jiangning, et al. (author) Expression robust 3D face landmarking using thresholded surface normals 2018 In: Pattern Recognition. - : ELSEVIER SCI LTD. - 0031-3203 .- 1873-5142. ; 78, s. 120-132 Journal article (peer-reviewed)abstract 3D face recognition is an increasing popular modality for biometric authentication, for example in the iPhoneX. Landmarking plays a significant role in region based face recognition algorithms. The accuracy and consistency of the landmarking will directly determine the effectiveness of feature extraction and hence the overall recognition performance. While surface normals have been shown to provide high performing features for face recognition, their use in landmarking has not been widely explored. To this end, a new 3D facial landmarking algorithm based on thresholded surface normal maps is proposed, which is applicable to widely used 3D face databases. The benefits of employing surface normals are demonstrated for both facial roll and yaw rotation calibration and nasal landmarks localization. Results on the Bosphorus, FRGC and BU-3DFE databases show that the detected landmarks possess high within class consistency and accuracy under different expressions. For several key landmarks the performance achieved surpasses that of state-of-the-art techniques and is also training free and computationally efficient. The use of surface normals therefore provides a useful representation of the 3D surface and the proposed landmarking algorithm provides an effective approach to localising the key nasal landmarks.
14.	Gelzinis, Adas, et al. (author) Increasing the discrimination power of the co-occurrence matrix-based features 2007 In: Pattern Recognition. - Oxford : Pergamon Press. - 0031-3203 .- 1873-5142. ; 40:9, s. 2367-2372 Journal article (peer-reviewed)abstract This paper is concerned with an approach to exploiting information available from the co-occurrence matrices computed for different distance parameter values. A polynomial of degree n is fitted to each of 14 Haralick's coefficients computed from the average co-occurrence matrices evaluated for several distance parameter values. Parameters of the polynomials constitute a set of new features. The experimental investigations performed substantiated the usefulness of the approach.
15.	Gharaee, Zahra, 1986-, et al. (author) Graph representation learning for road type classification 2021 In: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 120 Journal article (peer-reviewed)abstract We present a novel learning-based approach to graph representations of road networks employing state-of-the-art graph convolutional neural networks. Our approach is applied to realistic road networks of 17 cities from Open Street Map. While edge features are crucial to generate descriptive graph representations of road networks, graph convolutional networks usually rely on node features only. We show that the highly representative edge features can still be integrated into such networks by applying a line graph transformation. We also propose a method for neighborhood sampling based on a topological neighborhood composed of both local and global neighbors. We compare the performance of learning representations using different types of neighborhood aggregation functions in transductive and inductive tasks and in supervised and unsupervised learning. Furthermore, we propose a novel aggregation approach, Graph Attention Isomorphism Network, GAIN. Our results show that GAIN outperforms state-of-the-art methods on the road type classification problem.
16.	Gharehbaghi, Arash, et al. (author) A pattern recognition framework for detecting dynamic changes on cyclic time series 2015 In: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 48:3, s. 696-708 Journal article (peer-reviewed)abstract This paper proposes a framework for binary classification of the time series with cyclic characteristics. The framework presents an iterative algorithm for learning the cyclic characteristics by introducing the discriminative frequency bands (DFBs) using the discriminant analysis along with k-means clustering method. The DFBs are employed by a hybrid model for learning dynamic characteristics of the time series within the cycles, using statistical and structural machine learning techniques. The framework offers a systematic procedure for finding the optimal design parameters associated with the hybrid model. The proposed model is optimized to detect the changes of the heart sound recordings (HSRs) related to aortic stenosis. Experimental results show that the proposed framework provides efficient tools for classification of the HSRs based on the heart murmurs. It is also evidenced that the hybrid model, proposed by the framework, substantially improves the classification performance when it comes to detection of the heart disease.
17.	Gupta, Ankit, et al. (author) Efficient High-Resolution Template Matching with Vector Quantized Nearest Neighbour Fields 2024 In: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 151 Journal article (peer-reviewed)abstract Template matching is a fundamental problem in computer vision with applications in fields including object detection, image registration, and object tracking. Current methods rely on nearest-neighbour (NN) matching, where the query feature space is converted to NN space by representing each query pixel with its NN in the template. NN-based methods have been shown to perform better in occlusions, appearance changes, and non-rigid transformations; however, they scale poorly with high-resolution data and high feature dimensions. We present an NN-based method that efficiently reduces the NN computations and introduces filtering in the NN fields (NNFs). A vector quantization step is introduced before the NN calculation to represent the template with k features, and the filter response over the NNFs is used to compare the template and query distributions over the features. We show that state-of-the-art performance is achieved in low-resolution data, and our method outperforms previous methods at higher resolution.
18.	Halawani, Alaa, 1974-, et al. (author) 100 lines of code for shape-based object localization 2016 In: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 60, s. 458-472 Journal article (peer-reviewed)abstract We introduce a simple and effective concept for localizing objects in densely cluttered edge images based on shape information. The shape information is characterized by a binary template of the object's contour, provided to search for object instances in the image. We adopt a segment-based search strategy, in which the template is divided into a set of segments. In this work, we propose our own segment representation that we callone-pixel segment (OPS), in which each pixel in the template is treated as a separate segment. This is done to achieve high flexibility that is required to account for intra-class variations. OPS representation can also handle scale changes effectively. A dynamic programming algorithm uses the OPS representation to realize the search process, enabling a detailed localization of the object boundaries in the image. The concept's simplicity is reflected in the ease of implementation, as the paper's title suggests. The algorithm works directly with very noisy edge images extracted using the Canny edge detector, without the need for any preprocessing or learning steps. We present our experiments and show that our results outperform those of very powerful, state-of-the-art algorithms.
19.	Hanqing, Zhang, et al. (author) A fast and robust circle detection method using isosceles triangles sampling 2016 In: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 54, s. 218-228 Journal article (peer-reviewed)abstract Circle detection using randomized sampling has been developed in recent years to reduce computational intensity. However, randomized sampling is sensitive to noise that can lead to reduced accuracy and false-positive candidates. To improve on the robustness of randomized circle detection under noisy conditions this paper presents a new methodology for circle detection based upon randomized isosceles triangles sampling. It is shown that the geometrical property of isosceles triangles provides a robust criterion to find relevant edge pixels which, in turn, offers an efficient means to estimate the centers and radii of circles. For best efficiency, the estimated results given by the sampling from individual connected components of the edge map were analyzed using a simple clustering approach. To further improve on the accuracy we applied a two-step refinement process using chords and linear error compensation with gradient information of the edge pixels. Extensive experiments using both synthetic and real images have been performed. The results are compared to leading state-of-the-art algorithms and it is shown that the proposed methodology has a number of advantages: it is efficient in finding circles with a low number of iterations, it has high rejection rate of false-positive circle candidates, and it has high robustness against noise. All this makes it adaptive and useful in many vision applications.
20.	Ibrahim, Muhammad Talal, et al. (author) Velocity and pressure-based partitions of horizontal and vertical trajectories for on-line signature verification 2010 In: Pattern Recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 43:8, s. 2817-2832 Journal article (peer-reviewed)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.
21.	Johansson, Ulf, et al. (author) Rule extraction with guarantees from regression models 2022 In: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 126 Journal article (peer-reviewed)abstract Tools for understanding and explaining complex predictive models are critical for user acceptance and trust. One such tool is rule extraction, i.e., approximating opaque models with less powerful but interpretable models. Pedagogical (or black-box) rule extraction, where the interpretable model is induced using the original training instances, but with the predictions from the opaque model as targets, has many advantages compared to the decompositional (white-box) approach. Most importantly, pedagogical methods are agnostic to the kind of opaque model used, and any learning algorithm producing interpretable models can be employed for the learning step. The pedagogical approach has, however, one main problem, clearly limiting its utility. Specifically, while the extracted models are trained to mimic the opaque, there are absolutely no guarantees that this will transfer to novel data. This potentially low test set fidelity must be considered a severe drawback, in particular when the extracted models are used for explanation and analysis. In this paper, a novel approach, solving the problem with test set fidelity by utilizing the conformal prediction framework, is suggested for extracting interpretable regression models from opaque models. The extracted models are standard regression trees, but augmented with valid prediction intervals in the leaves. Depending on the exact setup, the use of conformal prediction guarantees that either the test set fidelity or the test set accuracy will be equal to a preset confidence level, in the long run. In the extensive empirical investigation, using 20 publicly available data sets, the validity of the extracted models is demonstrated. In addition, it is shown how normalization can be used to provide individualized prediction intervals, thus providing highly informative extracted models.
22.	Khan, Rizwan, et al. (author) A High Dynamic Range Imaging Method for Short Exposure Multiview Images 2023 In: Pattern Recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 137 Journal article (peer-reviewed)abstract The restoration and enhancement of multiview low dynamic range (MVLDR) images captured in low lighting conditions is a great challenge. The disparity maps are hardly reliable in practical, real-world scenarios and suffers from holes and artifacts due to large baseline and angle deviation among multi-ple cameras in low lighting conditions. Furthermore, multiple images with some additional information (e.g., ISO/exposure time, etc.) are required for the radiance map and poses the additional challenges of deghosting to encounter motion artifacts. In this paper, we proposed a method to reconstruct multiview high dynamic range (MVHDR) images from MVLDR images without relying on disparity maps. We de-tect and accurately match the feature points among the involved input views and gather the brightness information from the neighboring viewpoints to optimize an image restoration function based on input exposure gain to finally generate MVHDR images. Our method is very reliable and suitable for a wide baseline among sparse cameras. The proposed method requires only one image per viewpoint without any additional information and outperforms others.
23.	Liu, Jin, et al. (author) A spatially constrained fuzzy hyper-prototype clustering algorithm 2012 In: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 45:4, s. 1759-1771 Journal article (peer-reviewed)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.
24.	Luengo Hendriks, Cris L., 1974- (author) Revisiting priority queues for image analysis 2010 In: Pattern Recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 43:9, s. 3003-3012 Journal article (peer-reviewed)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.
25.	Lv, Zhihan, Dr. 1984-, et al. (author) Memory‐augmented neural networks based dynamic complex image segmentation in digital twins for self‐driving vehicle 2022 In: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 132 Journal article (peer-reviewed)abstract With the continuous increase of the amount of information, people urgently need to identify the information in the image in more detail in order to obtain richer information from the image. This work explores the dynamic complex image segmentation of self-driving vehicle under Digital Twins (DTs) based on Memory-augmented Neural Networks (MANNs), so as to further improve the performance of self-driving in intelligent transportation. In view of the complexity of the environment and the dynamic changes of the scene in intelligent transportation, this work constructs a segmentation model for dynamic complex image of self-driving vehicle under DTs based on MANNs by optimizing the Deep Learning algorithm and further combining with the DTs technology, so as to recognize the information in the environment image during the self-driving. Finally, the performance of the constructed model is analyzed by experimenting with different image datasets (PASCALVOC 2012, NYUDv2, PASCAL CONTEXT, and real self-driving complex traffic image data). The results show that compared with other classical algorithms, the established MANN-based model has an accuracy of about 85.80%, the training time is shortened to 107.00 s, the test time is 0.70 s, and the speedup ratio is high. In addition, the average algorithm parameter of the given energy function α=0.06 reaches the maximum value. Therefore, it is found that the proposed model shows high accuracy and short training time, which can provide experimental reference for future image visual computing and intelligent information processing.
26.	Ma, Zhanyu, et al. (author) Bayesian estimation of Dirichlet mixture model with variational inference 2014 In: Pattern Recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 47:9, s. 3143-3157 Journal article (peer-reviewed)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.
27.	Markus, Nenad, et al. (author) Eye pupil localization with an ensemble of randomized trees 2014 In: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 47:2, s. 578-587 Journal article (peer-reviewed)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.
28.	Nellros, Frida, et al. (author) Automated measurement of sintering degree in optical microscopy through image analysis of particle joins 2015 In: Pattern Recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 48:11, s. 3451-3465 Journal article (peer-reviewed)abstract In general terms, sintering describes the bonding of particles into a more coherent structure, where joins form between packed particles, usually as a result of heating. Characterization of sintering is an important topic in the fields of metallurgy, steel, iron ore pellets, ceramics, and snow for understanding material properties and material strength. Characterization using image analysis has been applied in a number of these fields but is either semi-automatic, requiring human interaction in the analysis, or based on statistical sampling and stereology to characterize the sample. This paper presents a novel fully automatic image analysis algorithm to analyze and determine the degree of sintering based on analysis of the particle joins and structure. Quantitative image analysis of the sintering degree is demonstrated for samples of iron ore pellets but could be readily applied to other packed particle materials. Microscope images of polished cross-sections of iron ore pellets have been imaged in their entirety and automated analysis of hundreds of images has been performed. Joins between particles have been identified based on morphological image processing and features have been calculated based on the geometric properties and curvature of these joins. The features have been analyzed and determined to hold discriminative power by displaying properties consistent with sintering theory and results from traditional pellet diameter measurements on the heated samples, and a statistical evaluation using the Welch t-test.
29.	Ng, Theam Foo, et al. (author) Feature interaction in subspace clustering using the Choquet integral 2012 In: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 45:7, s. 2645-2660 Journal article (peer-reviewed)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.
30.	Pham, Tuan D (author) Fuzzy posterior-probabilistic fusion 2011 In: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 44:5, s. 1023-1030 Journal article (peer-reviewed)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.
31.	Pham, Tuan D (author) GeoEntropy : A measure of complexity and similarity 2010 In: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 43:3, s. 887-896 Journal article (peer-reviewed)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.
32.	Pham, Tuan, 1962- (author) The Kolmogorov-Sinai entropy in the setting of fuzzy sets for image texture analysis and classification 2016 In: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 53, s. 229-237 Journal article (peer-reviewed)abstract The Kolmogorov–Sinai (K–S) entropy is used to quantify the average amount of uncertainty of a dynamical system through a sequence of observations. Sequence probabilities therefore play a central role for the computation of the entropy rate to determine if the dynamical system under study is deterministically non-chaotic, deterministically chaotic, or random. This paper extends the notion of the K–S entropy to measure the entropy rate of imprecise systems using sequence membership grades, in which the underlying deterministic paradigm is replaced with the degree of fuzziness. While constructing sequential probabilities for the calculation of the K–S entropy is difficult in practice, the estimate of the K–S entropy in the setting of fuzzy sets in an image is feasible and can be useful for modeling uncertainty of pixel distributions in images. The fuzzy K–S entropy is illustrated as an effective feature for image analysis and texture classification.
33.	Premaratne, Hemakumar Lalith, et al. (author) A segmentation-free approach to recognise printed Sinhala script using linear symmetry 2004 In: Pattern Recognition. - Amsterdam : Elsevier. - 0031-3203 .- 1873-5142. ; 37:10, s. 2081-2089 Journal article (peer-reviewed)abstract In this paper, a novel approach for printed character recognition using linear symmetry is proposed. When the conventional character recognition methods such as the artificial neural network based techniques are used to recognise Brahmi Sinhala script, segmentation of modified characters into modifier symbols and basic characters is a necessity but a complex issue. The large size of the character set makes the whole recognition process even more complex. In contrast, in the proposed method, the orientation features are effectively used to recognise characters directly using a standard alphabet as the basis without the need for segmentation into basic components. The edge detection algorithm using linear symmetry recognises vertical modifiers. The linear symmetry principle is also used to determine the skew angle. Experiments with the aim for an optical character recognition system for the printed Sinhala script show favourable results.
34.	Savas, Berkant, et al. (author) Handwritten digit classification using higher order singular value decomposition 2007 In: Pattern Recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 40:3, s. 993-1003 Journal article (peer-reviewed)abstract In this paper we present two algorithms for handwritten digit classification based on the higher order singular value decomposition (HOSVD). The first algorithm uses HOSVD for construction of the class models and achieves classification results with error rate lower than 6%. The second algorithm uses the HOSVD for tensor approximation simultaneously in two modes. Classification results for the second algorithm are almost down at 5% even though the approximation reduces the original training data with more than 98% before the construction of the class models. The actual classification in the test phase for both algorithms is conducted by solving a series least squares problems. Considering computational amount for the test presented the second algorithm is twice as efficient as the first one.
35.	Sidorova, Yulia, et al. (author) Bridging from syntactic to statistical methods : Classification with automatically segmented features from sequences 2015 In: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 48:11, s. 3749-3756 Journal article (peer-reviewed)abstract To integrate the benefits of statistical methods into syntactic pattern recognition, a Bridging Approach is proposed: (i) acquisition of a grammar per recognition class; (ii) comparison of the obtained grammars in order to find substructures of interest represented as sequences of terminal and/or non-terminal symbols and filling the feature vector with their counts; (iii) hierarchical feature selection and hierarchical classification, deducing and accounting for the domain taxonomy. The bridging approach has the benefits of syntactic methods: preserves structural relations and gives insights into the problem. Yet, it does not imply distance calculations and, thus, saves a non-trivial task-dependent design step. Instead it relies on statistical classification from many features. Our experiments concern a difficult problem of chemical toxicity prediction. The code and the data set are open-source. (C) 2015 Elsevier Ltd. All rights reserved.
36.	Sternby, Jakob, et al. (author) On-line Arabic handwriting recognition with templates 2009 In: Pattern Recognition. - : Elsevier BV. - 1873-5142 .- 0031-3203. ; 42:12, s. 3278-3286 Conference paper (peer-reviewed)abstract After a long period of focus on western and East Asian scripts there is now a general trend in the on-line handwriting recognition community to explore recognition of other scripts such as Arabic and various Indic scripts. One difficulty with the Arabic script is the number and position of diacritic marks associated to Arabic characters. This paper explores the application of a template matching scheme to the recognition of Arabic script with a novel algorithm for dynamically treating the diacritical marks. Template based systems are robust to conditions with scarce training data and in experiments the proposed system outperformed a reference system based on the promising state-of-the-art network technique of BLSTM. Experiments have been conducted in an environment similar to that of many handheld devices with promising results both in terms of memory consumption and response time. (C) 2009 Elsevier Ltd. All rights reserved.
37.	Su, Ran, et al. (author) Junction detection for linear structures based on Hessian, correlation and shape information 2012 In: Pattern Recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 45:10, s. 3695-3706 Journal article (peer-reviewed)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.
38.	Swaminathan, Muthukaruppan, et al. (author) A new distance measure for non-identical data with application to image classification 2017 In: Pattern Recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 63, s. 384-396 Journal article (peer-reviewed)abstract Distance measures are part and parcel of many computer vision algorithms. The underlying assumption in all existing distance measures is that feature elements are independent and identically distributed. However, in real-world settings, data generally originate from heterogeneous sources even if they do possess a common data generating mechanism. Since these sources are not identically distributed by necessity, the assumption of identical distribution is inappropriate. Here, we use statistical analysis to show that feature elements of local image descriptors are indeed non-identically distributed. To test the effect of omitting the unified distribution assumption, we created a new distance measure called the Poisson-Binomial radius (PBR). PBR is a bin-to-bin distance which accounts for the dispersion of bin-to-bin information. PBR's performance was evaluated on twelve benchmark data sets covering six different classification and recognition applications: texture, material, leaf, scene, ear biometrics and category-level image classification. Results from these experiments demonstrate that PBR outperforms state-of-the-art distance measures for most of the data sets and achieves comparable performance on the rest, suggesting that accounting for different distributions in distance measures can improve performance in classification and recognition tasks.
39.	Tan, Xiao, et al. (author) Feature matching in stereo images encouraging uniform spatial distribution 2015 In: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 48:8, s. 2530-2542 Journal article (peer-reviewed)abstract Finding feature correspondences between a pair of stereo images is a key step in computer vision for 3D reconstruction and object recognition. In practice, a larger number of correct correspondences and a higher percentage of correct matches are beneficial. Previous researches show that the spatial distribution of correspondences are also very important especially for fundamental matrix estimation. So far, no existing feature matching method considers the spatial distribution of correspondences. In our research, we develop a new algorithm to find good correspondences in all the three aspects mentioned, i.e., larger number of correspondences, higher percentage of correct correspondences, and better spatial distribution of correspondences. Our method consists of two processes: an adaptive disparity smoothing filter to remove false correspondences based on the disparities of neighboring correspondences and a matching exploration algorithm to find more correspondences according to the spatial distribution of correspondences so that the correspondences are as uniformly distributed as possible in the images. To find correspondences correctly and efficiently, we incorporate the cheirality constraint under an epipole polar transformation together with the epipolar constraint to predict the potential location of matching point. Experiments demonstrate that our method performs very well on both the number of correct correspondences and the percentage of correct correspondences; and the obtained correspondences are also well distributed over the image space.
40.	Tan, Xiao, et al. (author) Guided image completion by confidence propagation 2016 In: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 50, s. 210-222 Journal article (peer-reviewed)abstract This paper presents a new guided image completion method which fills any missing values by considering information from a guidance image. We develop a confidence propagation scheme that allows the filling process to be carried out simultaneously without the need of deciding the filling order. We conduct experiments in several applications where the problem can be formulated into a guided image completion problem, such as interactive segmentation and colorization. The experimental results show that our method provides good results and succeeds in situations where conventional methods fail. In addition, as all building blocks are parallel processes, our method is much suitable for hardware acceleration.
41.	Tanács, Attila, et al. (author) Estimation of linear deformations of 2D and 3D fuzzy objects 2015 In: Pattern Recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 48:4, s. 1391-1403 Journal article (peer-reviewed)
42.	Uscka-Wehlou, Hanna, 1973- (author) Run-hierarchical structure of digital lines with irrational slopes in terms of continued fractions and the Gauss map 2009 In: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 42:10, s. 2247-2254 Journal article (peer-reviewed)abstract We study relations between digital lines and continued fractions. The main result is a parsimonious description of the construction of the digital line based only on the elements of the continued fraction representing its slope and containing only simple integer computations. The description reflects the hierarchy of digitization runs, which raises the possibility of dividing digital lines into equivalence classes depending on the continued fraction expansions of their slopes. Our work is confined to irrational slopes since, to our knowledge,there exists no such description for these, in contrast to rational slopes which have been extensively examined. The description is exact (it does not use approximations by rationals). Examples of lines with irrational slopes and with very simple digitization patterns are presented. These include both slopes with periodic and non-periodic continued fraction expansions, i.e.\ both quadratic surds and other irrationals. We also derive the connection between the Gauss map and the digitization parameters introduced by the author in 2007.
43.	Verikas, Antanas, et al. (author) Mining data with random forests : A survey and results of new tests 2011 In: Pattern Recognition. - Oxford : Pergamon Press. - 0031-3203 .- 1873-5142. ; 44:2, s. 330-349 Journal article (peer-reviewed)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.
44.	Verikas, Antanas, 1951-, et al. (author) Phase congruency-based detection of circular objects applied to analysis of phytoplankton images 2012 In: Pattern Recognition. - Amsterdam : Elsevier. - 0031-3203 .- 1873-5142. ; 45:4, s. 1659-1670 Journal article (peer-reviewed)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.
45.	Yao, Jie, et al. (author) ADCNN : Towards learning adaptive dilation for convolutional neural networks 2022 In: Pattern Recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 123 Journal article (peer-reviewed)abstract Dilated convolution kernels are constrained by their shared dilation, keeping them from being aware of diverse spatial contents at different locations. We address such limitations by formulating the dilation as trainable weights with respect to individual positions. We propose Adaptive Dilation Convolutional Neural Networks (ADCNN), a light-weighted extension that allows convolutional kernels to adjust their dilation value based on different contents at the pixel level. Unlike previous content-adaptive models, ADCNN dynamically infers pixel-wise dilation via modeling feed-forward inter-patterns, which provides a new perspective for developing adaptive network structures other than sampling kernel spaces. Our evaluation results indicate ADCNNs can be easily integrated into various backbone networks and consistently outperform their regular counterparts on various visual tasks.
46.	Zamboni, Simone, et al. (author) Pedestrian trajectory prediction with convolutional neural networks 2022 In: Pattern Recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 121 Journal article (peer-reviewed)abstract Predicting the future trajectories of pedestrians is a challenging problem that has a range of application, from crowd surveillance to autonomous driving. In literature, methods to approach pedestrian trajectory prediction have evolved, transitioning from physics-based models to data-driven models based on recurrent neural networks. In this work, we propose a new approach to pedestrian trajectory prediction, with the introduction of a novel 2D convolutional model. This new model outperforms recurrent models, and it achieves state-of-the-art results on the ETH and TrajNet datasets. We also present an effective system to represent pedestrian positions and powerful data augmentation techniques, such as the addition of Gaussian noise and the use of random rotations, which can be applied to any model. As an additional exploratory analysis, we present experimental results on the inclusion of occupancy methods to model social information, which empirically show that these methods are ineffective in capturing social interaction.
47.	Damaschke, Peter, 1963, et al. (author) Fast algorithms for finding disjoint subsequences with extremal densities 2006 In: Pattern Recognition. - : Elsevier BV. - 0031-3203. ; 39:12, s. 2281-2292 Journal article (peer-reviewed)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.
48.	Khatami, Mohammad, et al. (author) BundleMAP : Anatomically localized classification, regression, and hypothesis testing in diffusion MRI 2017 In: Pattern Recognition. - : Elsevier BV. - 0031-3203. ; 63, s. 593-600 Journal article (peer-reviewed)abstract Diffusion MRI (dMRI) provides rich information on the white matter of the human brain, enabling insight into neurological disease, normal aging, and neuroplasticity. We present BundleMAP, an approach to extracting features from dMRI data that can be used for supervised classification, regression, and hypothesis testing. Our features are based on aggregating measurements along nerve fiber bundles, enabling visualization and anatomical interpretation. The main idea behind BundleMAP is to use the ISOMAP manifold learning technique to jointly parametrize nerve fiber bundles. We combine this idea with mechanisms for outlier removal and feature selection to obtain a practical machine learning pipeline. We demonstrate that it increases accuracy of disease detection and estimation of disease activity, and that it improves the power of statistical tests.
49.	Lin, Che-Tsung, 1979, et al. (author) Cycle-Object Consistency for Image-to-Image Domain Adaptation 2023 In: Pattern Recognition. - : Elsevier BV. - 0031-3203. ; 138 Journal article (peer-reviewed)abstract Recent advances in generative adversarial networks (GANs) have been proven effective in performing domain adaptation for object detectors through data augmentation. While GANs are exceptionally successful, those methods that can preserve objects well in the image-to-image translation task usually require an auxiliary task, such as semantic segmentation to prevent the image content from being too distorted. However, pixel-level annotations are difficult to obtain in practice. Alternatively, instance-aware image-translation model treats object instances and background separately. Yet, it requires object detectors at test time, assuming that off-the-shelf detectors work well in both domains. In this work, we present AugGAN-Det, which introduces Cycle-object Consistency (CoCo) loss to generate instance-aware translated images across complex domains. The object detector of the target domain is directly leveraged in generator training and guides the preserved objects in the translated images to carry target-domain appearances. Compared to previous models, which e.g., require pixel-level semantic segmentation to force the latent distribution to be object-preserving, this work only needs bounding box annotations which are significantly easier to acquire. Next, as to the instance-aware GAN models, our model, AugGAN-Det, internalizes global and object style-transfer without explicitly aligning the instance features. Most importantly, a detector is not required at test time. Experimental results demonstrate that our model outperforms recent object-preserving and instance-level models and achieves state-of-the-art detection accuracy and visual perceptual quality.
50.	Ng, Chun Chet, et al. (author) When IC meets text: Towards a rich annotated integrated circuit text dataset 2024 In: Pattern Recognition. - 0031-3203. ; 147 Journal article (peer-reviewed)abstract Automated Optical Inspection (AOI) is a process that uses cameras to autonomously scan printed circuit boards for quality control. Text is often printed on chip components, and it is crucial that this text is correctly recognized during AOI, as it contains valuable information. In this paper, we introduce \textit{ICText}, the largest dataset for text detection and recognition on integrated circuits. Uniquely, it includes labels for character quality attributes such as low contrast, blurry, and broken. While loss-reweighting and Curriculum Learning (CL) have been proposed to improve object detector performance by balancing positive and negative samples and gradually training the model from easy to hard samples, these methods have had limited success with one-stage object detectors commonly used in industry. To address this, we propose Attribute-Guided Curriculum Learning (AGCL), which leverages the labeled character quality attributes in \textit{ICText}. Our extensive experiments demonstrate that AGCL can be applied to different detectors in a plug-and-play fashion to achieve higher Average Precision (AP), significantly outperforming existing methods on \textit{ICText} without any additional computational overhead during inference. Furthermore, we show that AGCL is also effective on the generic object detection dataset Pascal VOC. Our code and dataset will be publicly available at \href{https://github.com/chunchet-ng/ICText-AGCL}{https://github.com/chunchet-ng/ICText-AGCL}.

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