Enter your login details for Pattern Recognition Letters below. If you do not already have an account you will need to register here.
Check submitted paper
Track accepted paper
Once your article has been accepted you will receive an email from Author Services. This email contains a link to check the status of your article. Track your accepted paper
Most Cited Pattern Recognition Letters Articles
The most cited articles published since 2011, extracted from Scopus.
Volume 33, Issue 4, March 2012, Pages 431-437
Gender recognition is one of fundamental face analysis tasks. Most of the existing studies have focused on face images acquired under controlled conditions. However, real-world applications require gender classification on real-life faces, which is much more challenging due to significant appearance variations in unconstrained scenarios. In this paper, we investigate gender recognition on real-life faces using the recently built database, the Labeled Faces in the Wild (LFW). Local Binary Patterns (LBP) is employed to describe faces, and Adaboost is used to select the discriminative LBP features. We obtain the performance of 94.81% by applying Support Vector Machine (SVM) with the boosted LBP features. The public database used in this study makes future benchmark and evaluation possible. © 2011 Published by Elsevier B.V. All rights reserved.
Volume 32, Issue 12, September 2011, Pages 1598-1603
O. Déniz | G. Bueno | J. Salido | F. De La Torre
Face recognition has been a long standing problem in computer vision. Recently, Histograms of Oriented Gradients (HOGs) have proven to be an effective descriptor for object recognition in general and face recognition in particular. In this paper, we investigate a simple but powerful approach to make robust use of HOG features for face recognition. The three main contributions of this work are: First, in order to compensate for errors in facial feature detection due to occlusions, pose and illumination changes, we propose to extract HOG descriptors from a regular grid. Second, fusion of HOG descriptors at different scales allows to capture important structure for face recognition. Third, we identify the necessity of performing dimensionality reduction to remove noise and make the classification process less prone to overfitting. This is particularly important if HOG features are extracted from overlapping cells. Finally, experimental results on four databases illustrate the benefits of our approach. © 2011 Elsevier B.V. All rights reserved.
Volume 34, Issue 1, January 2013, Pages 3-19
Intelligent multi-camera video surveillance is a multidisciplinary field related to computer vision, pattern recognition, signal processing, communication, embedded computing and image sensors. This paper reviews the recent development of relevant technologies from the perspectives of computer vision and pattern recognition. The covered topics include multi-camera calibration, computing the topology of camera networks, multi-camera tracking, object re-identification, multi-camera activity analysis and cooperative video surveillance both with active and static cameras. Detailed descriptions of their technical challenges and comparison of different solutions are provided. It emphasizes the connection and integration of different modules in various environments and application scenarios. According to the most recent works, some problems can be jointly solved in order to improve the efficiency and accuracy. With the fast development of surveillance systems, the scales and complexities of camera networks are increasing and the monitored environments are becoming more and more complicated and crowded. This paper discusses how to face these emerging challenges. © 2012 Elsevier B.V. All rights reserved.
Volume 32, Issue 7, May 2011, Pages 956-961
Xiangyang Xu | Shengzhou Xu | Lianghai Jin | Enmin Song
This paper proves that Otsu threshold is equal to the average of the mean levels of two classes partitioned by this threshold. Therefore, when the within-class variances of two classes are different, the threshold biases toward the class with larger variance. As a result, partial pixels belonging to this class will be misclassified into the other class with smaller variance. To address this problem and based on the analysis of Otsu threshold, this paper proposes an improved Otsu algorithm that constrains the search range of gray levels. Experimental results demonstrate the superiority of new algorithm compared with Otsu method. © 2010 Elsevier B.V. All rights reserved.
Volume 32, Issue 1, January 2011, Pages 56-68
Sandra Eliza Fontes De Avila | Ana Paula Brandão Lopes | Antonio Da Luz | Arnaldo De Albuquerque Araújo
The fast evolution of digital video has brought many new multimedia applications and, as a consequence, has increased the amount of research into new technologies that aim at improving the effectiveness and efficiency of video acquisition, archiving, cataloging and indexing, as well as increasing the usability of stored videos. Among possible research areas, video summarization is an important topic that potentially enables faster browsing of large video collections and also more efficient content indexing and access. Essentially, this research area consists of automatically generating a short summary of a video, which can either be a static summary or a dynamic summary. In this paper, we present VSUMM, a methodology for the production of static video summaries. The method is based on color feature extraction from video frames and k-means clustering algorithm. As an additional contribution, we also develop a novel approach for the evaluation of video static summaries. In this evaluation methodology, video summaries are manually created by users. Then, several user-created summaries are compared both to our approach and also to a number of different techniques in the literature. Experimental results show - with a confidence level of 98% - that the proposed solution provided static video summaries with superior quality relative to the approaches to which it was compared. © 2010 Elsevier B.V. All rights reserved.
Volume 32, Issue 11, August 2011, Pages 1541-1547
Wonseok Song | Taejeong Kim | Hee Chan Kim | Joon Hwan Choi | Hyoun Joong Kong | Seung Rae Lee
The finger-vein pattern is one of the human biometric signatures that can be used for personal verification. The first task of a verification process using finger-vein patterns is extracting the pattern from an infrared finger image. As a robust extraction method, we propose the mean curvature method, which views the vein image as a geometric shape and finds the valley-like structures with negative mean curvatures. When the matched pixel ratio is used in matching vein patterns, experimental results show that, while maintaining low complexity, the proposed method achieves 0.25% equal error rate, which is significantly lower than what existing methods can achieve. © 2011 Elsevier B.V. All rights reserved.
Volume 34, Issue 15, March 2013, Pages 1995-2006
Lulu Chen | Hong Wei | James Ferryman
Analysis of human behaviour through visual information has been a highly active research topic in the computer vision community. This was previously achieved via images from a conventional camera, however recently depth sensors have made a new type of data available. This survey starts by explaining the advantages of depth imagery, then describes the new sensors that are available to obtain it. In particular, the Microsoft Kinect has made high-resolution real-time depth cheaply available. The main published research on the use of depth imagery for analysing human activity is reviewed. Much of the existing work focuses on body part detection and pose estimation. A growing research area addresses the recognition of human actions. The publicly available datasets that include depth imagery are listed, as are the software libraries that can acquire it from a sensor. This survey concludes by summarising the current state of work on this topic, and pointing out promising future research directions. For both researchers and practitioners who are familiar with this topic and those who are new to this field, the review will aid in the selection, and development, of algorithms using depth data. © 2013 Elsevier B.V. All rights reserved.
Volume 33, Issue 7, May 2012, Pages 934-942
Andreas Fischer | Andreas Keller | Volkmar Frinken | Horst Bunke
For retrieving keywords from scanned handwritten documents, we present a word spotting system that is based on character Hidden Markov Models. In an efficient lexicon-free approach, arbitrary keywords can be spotted without pre-segmenting text lines into words. For a multi-writer scenario on the IAM off-line database as well as for two single writer scenarios on historical data sets, it is shown that the proposed learning-based system outperforms a standard template matching method. © 2011 Elsevier B.V. All rights reserved.
Volume 33, Issue 7, May 2012, Pages 898-903
Loris Bazzani | Marco Cristani | Alessandro Perina | Vittorio Murino
We propose a novel appearance-based method for person re-identification, that condenses a set of frames of an individual into a highly informative signature, called the Histogram Plus Epitome, HPE. It incorporates complementary global and local statistical descriptions of the human appearance, focusing on the overall chromatic content via histogram representation, and on the presence of recurrent local patches via epitomic analysis. The re-identification performance of HPE is then augmented by applying it as human part descriptor, defining a structured feature called asymmetry-based HPE (AHPE). The matching between (A)HPEs provides optimal performances against low resolution, occlusions, pose and illumination variations, defining state-of-the-art results on all the considered datasets. © 2011 Elsevier B.V. All rights reserved.
Volume 33, Issue 3, February 2012, Pages 227-238
Qin Zou | Yu Cao | Qingquan Li | Qingzhou Mao | Song Wang
Pavement cracks are important information for evaluating the road condition and conducting the necessary road maintenance. In this paper, we develop CrackTree, a fully-automatic method to detect cracks from pavement images. In practice, crack detection is a very challenging problem because of (1) low contrast between cracks and the surrounding pavement, (2) intensity inhomogeneity along the cracks, and (3) possible shadows with similar intensity to the cracks. To address these problems, the proposed method consists of three steps. First, we develop a geodesic shadow-removal algorithm to remove the pavement shadows while preserving the cracks. Second, we build a crack probability map using tensor voting, which enhances the connection of the crack fragments with good proximity and curve continuity. Finally, we sample a set of crack seeds from the crack probability map, represent these seeds by a graph model, derive minimum spanning trees from this graph, and conduct recursive tree-edge pruning to identify desirable cracks. We evaluate the proposed method on a collection of 206 real pavement images and the experimental results show that the proposed method achieves a better performance than several existing methods. © 2011 Elsevier B.V. All rights reserved.
Volume 34, Issue 2, January 2013, Pages 107-116
Cunzhao Shi | Chunheng Wang | Baihua Xiao | Yang Zhang | Song Gao
Scene text detection could be formulated as a bi-label (text and non-text regions) segmentation problem. However, due to the high degree of intraclass variation of scene characters as well as the limited number of training samples, single information source or classifier is not enough to segment text from non-text background. Thus, in this paper, we propose a novel scene text detection approach using graph model built upon Maximally Stable Extremal Regions (MSERs) to incorporate various information sources into one framework. Concretely, after detecting MSERs in the original image, an irregular graph whose nodes are MSERs, is constructed to label MSERs as text regions or non-text ones. Carefully designed features contribute to the unary potential to assess the individual penalties for labeling a MSER node as text or non-text, and color and geometric features are used to define the pairwise potential to punish the likely discontinuities. By minimizing the cost function via graph cut algorithm, different information carried by the cost function could be optimally balanced to get the final MSERs labeling result. The proposed method is naturally context-relevant and scale-insensitive. Experimental results on the ICDAR 2011 competition dataset show that the proposed approach outperforms state-of-the-art methods both in recall and precision. © 2012 Elsevier B.V. All rights reserved.
Volume 33, Issue 2, January 2012, Pages 157-163
Rehanullah Khan | Allan Hanbury | Julian Stöttinger | Abdul Bais
Skin detection is used in applications ranging from face detection, tracking body parts and hand gesture analysis, to retrieval and blocking objectionable content. In this paper, we investigate and evaluate (1) the effect of color space transformation on skin detection performance and finding the appropriate color space for skin detection, (2) the role of the illuminance component of a color space, (3) the appropriate pixel based skin color modeling technique and finally, (4) the effect of color constancy algorithms on color based skin classification. The comprehensive color space and skin color modeling evaluation will help in the selection of the best combinations for skin detection. Nine skin modeling approaches (AdaBoost, Bayesian network, J48, Multilayer Perceptron, Naive Bayesian, Random Forest, RBF network, SVM and the histogram approach of Jones and Rehg (2002)) in six color spaces (IHLS, HSI, RGB, normalized RGB, YCbCr and CIELAB) with the presence or absence of the illuminance component are compared and evaluated. Moreover, the impact of five color constancy algorithms on skin detection is reported. Results on a database of 8991 images with manually annotated pixel-level ground truth show that (1) the cylindrical color spaces outperform other color spaces, (2) the absence of the illuminance component decreases performance, (3) the selection of an appropriate skin color modeling approach is important and that the tree based classifiers (Random forest, J48) are well suited to pixel based skin detection. As a best combination, the Random Forest combined with the cylindrical color spaces, while keeping the illuminance component outperforms other combinations, and (4) the usage of color constancy algorithms can improve skin detection performance. © 2011 Elsevier B.V. All rights reserved.
Volume 33, Issue 2, January 2012, Pages 134-143
Junwei Wang | Xiang Bai | Xinge You | Wenyu Liu | Longin Jan Latecki
We propose a novel shape descriptor for matching and recognizing 2D object silhouettes. The contour of each object is represented by a fixed number of sample points. For each sample point, a height function is defined based on the distances of the other sample points to its tangent line. One compact and robust shape descriptor is obtained by smoothing the height functions. The proposed descriptor is not only invariant to geometric transformations such as translation, rotation and scaling but also insensitive to nonlinear deformations due to noise and occlusion. In the matching stage, the Dynamic Programming (DP) algorithm is employed to find out the optimal correspondence between sample points of every two shapes. The height function provides an excellent discriminative power, which is demonstrated by excellent retrieval performances on several popular shape benchmarks, including MPEG-7 data set, Kimia's data set and ETH-80 data set. © 2011 Elsevier B.V. All rights reserved.
Volume 32, Issue 13, October 2011, Pages 1633-1642
Cuneyt Akinlar | Cihan Topal
We propose a linear time line segment detector that gives accurate results, requires no parameter tuning, and runs up to 11 times faster than the fastest known line segment detector in the literature; namely, the line segment detector (LSD) by Grompone von Gioi et al. The proposed algorithm makes use of the clean, contiguous (connected) chain of edge pixels produced by our novel edge detector, the Edge Drawing (ED) algorithm; hence the name EDLines. The detector includes a line validation step due to the Helmholtz principle, which lets it control the number of false detections. With its accurate results and blazing speed, EDLines will be very suitable for the next generation real-time computer vision and image processing applications. © 2011 Elsevier B.V. All rights reserved.
Volume 33, Issue 12, September 2012, Pages 1569-1579
Jinfeng Yang | Yihua Shi
Finger-vein based biometrics, as a new approach to personal identification, has received much attention in recent years. However, the poor visibility of finger-vein imageries is really not beneficial for deepening the understanding of finger-vein characteristics. Moreover, unreliable finger-vein region of interest (ROI) localization can also heavily degrade the performance of a finger-vein based recognition system in practical scenario. Hence, in this paper, we first introduces a new and robust approach for finger-vein ROI localization, and then proposes a new scheme for effectively improving the visibility of finger-vein imageries. Extensive experiments are finally conducted to validate the proposed method. © 2012 Elsevier B.V. All rights reserved.
Volume 32, Issue 14, October 2011, Pages 1701-1705
Murat Erisoglu | Nazif Calis | Sadullah Sakallioglu
Clustering is one of the widely used knowledge discovery techniques to reveal structures in a dataset that can be extremely useful to the analyst. In iterative clustering algorithms the procedure adopted for choosing initial cluster centers is extremely important as it has a direct impact on the formation of final clusters. Since clusters are separated groups in a feature space, it is desirable to select initial centers which are well separated. In this paper, we have proposed an algorithm to compute initial cluster centers for k-means algorithm. The algorithm is applied to several different datasets in different dimension for illustrative purposes. It is observed that the newly proposed algorithm has good performance to obtain the initial cluster centers for the k-means algorithm. © 2011 Elsevier B.V. All rights reserved.
Volume 32, Issue 12, September 2011, Pages 1613-1621
The present paper considers the problem of partitioning a dataset into a known number of clusters using the sum of squared errors criterion (SSE). A new clustering method, called DE-KM, which combines differential evolution algorithm (DE) with the well known K-means procedure is described. In the method, the K-means algorithm is used to fine-tune each candidate solution obtained by mutation and crossover operators of DE. Additionally, a reordering procedure which allows the evolutionary algorithm to tackle the redundant representation problem is proposed. The performance of the DE-KM clustering method is compared to the performance of differential evolution, global K-means method, genetic K-means algorithm and two variants of the K-means algorithm. The experimental results show that if the number of clusters K is sufficiently large, DE-KM obtains solutions with lower SSE values than the other five algorithms. © 2011 Elsevier B.V. All rights reserved.
Volume 33, Issue 4, March 2012, Pages 438-445
Ling Shao | Ling Ji | Yan Liu | Jianguo Zhang
In this paper, we present an automated video analysis system which addresses segmentation and detection of human actions in an indoor environment, such as a gym. The system aims at segmenting different movements from the input video and recognizing the action types simultaneously. Two action segmentation techniques, namely color intensity based and motion based, are proposed. Both methods can efficiently segment periodic human movements into temporal cycles. We also apply a novel approach for human action recognition by describing human actions using motion and shape features. The descriptor contains both the local shape and its spatial layout information, therefore is more effective for action modeling and is suitable for detecting and recognizing a variety of actions. Experimental results show that the proposed action segmentation and detection algorithms are highly effective. © 2011 Published by Elsevier B.V. All rights reserved.
Volume 33, Issue 12, September 2012, Pages 1520-1528
Jen Chun Lee
Vein pattern recognition is one of the newest biometric techniques researched today. In this paper, one of the reliable and robust personal identification authentication approaches using palm vein patterns is presented. We consider the palm vein as a piece of texture and apply texture-based feature extraction techniques to palm vein authentication in our work. A 2-D Gabor filter provides the optimized resolution in both the spatial and frequency domains, thus it is a basis for extracting local features in the palm vein recognition. In order to obtain effective pattern of palm vascular, we proposed an innovative and robust directional coding technique to encode the palm vein features in bit string representation. The bit string representation, called VeinCode, offers speedy template matching and enables more effective template storage and retrieval. The similarity of two VeinCodes is measured by normalized hamming distance. A total of 4140 palm vein images were collected form 207 persons to verify the validity of the proposed palm vein recognition approach. High accuracy has been obtained by the proposed method and the speed of the method is rapid enough for real-time palm vein recognition. Experimental results demonstrate that our proposed approach is feasible and effective for palm vein recognition. © 2012 Elsevier B.V. All rights reserved.
Volume 32, Issue 6, April 2011, Pages 838-853
Marina E. Plissiti | Christophoros Nikou | Antonia Charchanti
In this work, we present an automated method for the detection and boundary determination of cells nuclei in conventional Pap stained cervical smear images. The detection of the candidate nuclei areas is based on a morphological image reconstruction process and the segmentation of the nuclei boundaries is accomplished with the application of the watershed transform in the morphological color gradient image, using the nuclei markers extracted in the detection step. For the elimination of false positive findings, salient features characterizing the shape, the texture and the image intensity are extracted from the candidate nuclei regions and a classification step is performed to determine the true nuclei. We have examined the performance of two unsupervised (K-means, spectral clustering) and a supervised (Support Vector Machines, SVM) classification technique, employing discriminative features which were selected with a feature selection scheme based on the minimal-Redundancy-Maximal- Relevance criterion. The proposed method was evaluated on a data set of 90 Pap smear images containing 10,248 recognized cell nuclei. Comparisons with the segmentation results of a gradient vector flow deformable (GVF) model and a region based active contour model (ACM) are performed, which indicate that the proposed method produces more accurate nuclei boundaries that are closer to the ground truth. © 2011 Elsevier B.V. All rights reserved.
Volume 32, Issue 3, February 2011, Pages 403-410
Orazio Gallo | Roberto Manduchi | Abbas Rafii
Range sensors, in particular time-of-flight and stereo cameras, are being increasingly used for applications such as robotics, automotive, human-machine interface and virtual reality. The ability to recover the geometrical structure of visible surfaces is critical for scene understanding. Typical structured indoor or urban scenes are often represented via compositional models comprising multiple planar surface patches. The RANSAC robust regression algorithm is the most popular technique to date for extracting individual planar patches from noisy data sets containing multiple surfaces. Unfortunately, RANSAC fails to produce reliable results in situations with two nearby patches of limited extent, where a single plane crossing through the two patches may contain more inliers than the "correct" models. This is the case of steps, curbs, or ramps, which represent the focus of our research for the impact they can have on cars' safe parking systems or robot navigation. In an effort to improve the quality of regression in these cases, we propose a modification of the RANSAC algorithm, dubbed CC-RANSAC, that only considers the largest connected components of inliers to evaluate the fitness of a candidate plane. We provide experimental evidence that CC-RANSAC may recover the planar patches composing a typical step or ramp with substantially higher accuracy than the traditional RANSAC algorithm. © 2010 Elsevier B.V. All rights reserved.
A GRASP algorithm for fast hybrid (filter-wrapper) feature subset selection in high-dimensional datasets
Volume 32, Issue 5, April 2011, Pages 701-711
Pablo Bermejo | Jose A. Gámez | Jose M. Puerta
Feature subset selection is a key problem in the data-mining classification task that helps to obtain more compact and understandable models without degrading (or even improving) their performance. In this work we focus on FSS in high-dimensional datasets, that is, with a very large number of predictive attributes. In this case, standard sophisticated wrapper algorithms cannot be applied because of their complexity, and computationally lighter filter-wrapper algorithms have recently been proposed. In this work we propose a stochastic algorithm based on the GRASP meta-heuristic, with the main goal of speeding up the feature subset selection process, basically by reducing the number of wrapper evaluations to carry out. GRASP is a multi-start constructive method which constructs a solution in its first stage, and then runs an improving stage over that solution. Several instances of the proposed GRASP method are experimentally tested and compared with state-of-the-art algorithms over 12 high-dimensional datasets. The statistical analysis of the results shows that our proposal is comparable in accuracy and cardinality of the selected subset to previous algorithms, but requires significantly fewer evaluations.© 2010 Elsevier B.V. All rights reserved.
Volume 32, Issue 1, January 2011, Pages 27-37
C. D. Ferreira | J. A. Santos | R. Da S. Torres | M. A. Gonalves | R. C. Rezende | Weiguo Fan
This paper presents two content-based image retrieval frameworks with relevance feedback based on genetic programming. The first framework exploits only the user indication of relevant images. The second one considers not only the relevant but also the images indicated as non-relevant. Several experiments were conducted to validate the proposed frameworks. These experiments employed three different image databases and color, shape, and texture descriptors to represent the content of database images. The proposed frameworks were compared, and outperformed six other relevance feedback methods regarding their effectiveness and efficiency in image retrieval tasks. © 2010 Elsevier B.V. All rights reserved.
Volume 33, Issue 1, January 2012, Pages 17-23
M. G R Sause | A. Gribov | A. R. Unwin | S. Horn
A new approach is introduced to identify natural clusters of acoustic emission signals. The presented technique is based on an exhaustive screening taking into account all combinations of signal features extracted from the recorded acoustic emission signals. For each possible combination of signal features an investigation of the classification performance of the k-means algorithm is evaluated ranging from two to ten classes. The numerical degree of cluster separation of each partition is calculated utilizing the Davies-Bouldin and Tou indices, Rousseeuw's silhouette validation method and Hubert's Gamma statistics. The individual rating of each cluster validation technique is cumulated based on a voting scheme and is evaluated for the number of clusters with best performance. This is defined as the best partitioning for the given signal feature combination. As a second step the numerical ranking of all these partitions is evaluated for the globally optimal partition in a second voting scheme using the cluster validation methods results. This methodology can be used as an automated evaluation of the number of natural clusters and their partitions without previous knowledge about the cluster structure of acoustic emission signals. The suitability of the current approach was evaluated using artificial datasets with defined degree of separation. In addition the application of the approach to clustering of acoustic emission signals is demonstrated for signals obtained from failure during loading of carbon fiber reinforced plastic specimens. © 2011 Elsevier B.V. All rights reserved.
Volume 33, Issue 5, April 2012, Pages 623-628
Jinfeng Yang | Xu Zhang
Multimodal biometrics based on feature-level fusion is a significant topic in personal identification research community. In this paper, a new fingerprint-vein based biometric method is proposed for making a finger more universal in biometrics. The fingerprint and finger-vein features are first exploited and extracted using a unified Gabor filter framework. Then, a novel supervised local-preserving canonical correlation analysis method (SLPCCAM) is proposed to generate fingerprint-vein feature vectors (FPVFVs) in feature-level fusion. Based on FPVFVs, the nearest neighborhood classifier is employed for personal identification finally. Experimental results show that the proposed approach has a high capability in fingerprint-vein based personal recognition as well as multimodal feature-level fusion. © 2011 Elsevier B.V. All rights reserved.