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Copy detection pattern-based document protection for variable media

IET image processing 2012年第8期6卷 1102-1113页

作者： Dirik, A. E. Haas, B. Uludag Univ Fac Engn & Architecture Dept Elect Engn TR-16059 Bursa Turkey Harvard Univ Dept Stat Cambridge MA 02138 USA

A copy detection pattern (CDP) is a shape (typically a rectangle) filled with pixels of random grey levels and incorporated in a digital document to be printed as a unique physical document. It is a powerful tool to detect copies. Whenever a document is printed and scanned, the grey levels of the pixels intermix in a measurable way. When a document of unknown legitimacy containing a CDP is scanned, the CDP distortion is measured and matched against a pre-determined threshold. If this measure is higher than the threshold, an intermediary scan and re-print most likely occurred. For acceptable accuracy, the print quality needs to be reasonably good;therefore in CDP applications, the printer, the CDP media and the scanner must usually be of known type. However, some applications could greatly benefit from CDP features, but have little control over the media. In such applications, the variance of the measurements, owing to media quality variability, is simply too large for a reasonably conclusive detection, at least with current verification techniques. Thus, the authors propose a print-scan model and a smart attack based on the model that causes the CDP copy detection to be mostly ineffective. To handle such an attack scenario and allow a range of different media, new CDP detection metrics were proposed. Their efficacies were tested for different paper types and different attack scenarios. Experimental analysis shows that the proposed features can be used to significantly improve copy detection accuracy.

关键词： variable media digital document CDP copy detection current verification techniques print-scan model CDP media document image processing image colour analysis reasonably conclusive detection unique physical document copyright computer vision and image processing techniques CDP distortion CDP detection metrics Optical, image and video signal processing copy detection pattern-based document protection media quality variability Legal aspects of computing pixels intermix Document processing techniques random grey levels smart attack

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Padua point interpolation and L^p-norm minimisation in colour-based image indexing and retrieval

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IET image processing 2012年第2期6卷 139-147页

作者： Montagna, R. Finlayson, G. D. Univ E Anglia Sch Comp Sci Norwich NR4 7TJ Norfolk England

Colour has proven to be a very powerful feature for image indexing. Many examples of image retrieval systems based on colour or chromaticity histograms have been proposed, following on from the histogram intersection method of Swain and Ballard. Here the authors introduce a compact representation of the chromaticity histogram which is based on the Padua point interpolation technique. Specifically, the histogram is represented as a linear combination of Chebyshev polynomials. This bounds a certain maximum deviation, as opposed to a least-squares criterion used in previous work. With this in mind, the minimisation of different L-p norms and the L-infinity norm of the error is compared. After presenting the Padua point image indexing and retrieval method, the authors compare its performance to the histogram intersection, the discrete cosine transform, and a dataset oriented method based on principal component analysis. The experiments show that the Padua points match and, in some cases, improve the performance of these methods. This is significant as the proposed method is not tuned (unlike the PCA approach to any dataset). Finally, the behaviour of the Padua point method is analysed in relation to the minimisation of different norms.

关键词： image colour analysis Optimisation techniques indexing Lp-norm minimisation image retrieval systems computer vision and image processing techniques L∞-norm minimisation Optical, image and video signal processing least squares approximations least-square criterion Information retrieval techniques Interpolation and function approximation (numerical analysis) Chebyshev polynomials histogram intersection method interpolation Padua point interpolation technique minimisation chromaticity histograms colour-based image indexing image retrieval

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Robust solution to three-dimensional pose estimation using composite extended Kalman observer and Kalman filter

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IET computer vision 2012年第2期6卷 140-152页

作者： Taghirad, H. D. Atashzar, S. F. Shahbazi, M. Advanced Robotics and Automated Systems (ARAS) Faculty of Electrical and Computer Engineering K. N. Toosi University of Technology Iran

Three-dimensional (3D) pose estimation of a rigid object by only one camera has a vital role in visual servoing systems, and extended Kalman filter (EKF) is vastly used for this task in an unstructured environment. In this study, the stability of the EKF-based 3D pose estimators is analysed in detail. The most challenging issue of the state-of-the-art EKF-based 3D pose estimators is the possibility of its divergence because of the measurement and model noises. By analysing the stability of conventional EKF-based pose estimators a composite technique is proposed to guarantee the stability of the procedure. In the proposed technique, the non-linear-uncertain estimation problem is decomposed into a non-linear-certain observation in addition to a linear-uncertain estimation problem. The first part is handled using the extended Kalman observer and the second part is accomplished by a simple Kalman filter. Finally, some experimental and simulation results are given in order to verify the robustness of the method and compare the performance of the proposed method in noisy and uncertain environment to the conventional techniques.

关键词： visual servoing Other topics in statistics Kalman filters robust solution image recognition cameras nonlinear-certain observation nonlinear uncertain estimation problem linear-uncertain estimation problem visual servoing system composite extended Kalman observer pose estimation three-dimensional pose estimation nonlinear filters Filtering methods in signal processing image sensors computer vision and image processing techniques simple Kalman filter observers unstructured environment EKF-based 3D pose estimator Graphics techniques solid modelling nonlinear estimation camera

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Grid-based feature distributions for off-line signature verification

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IET BIOMETRICS 2012年第1期1卷 72-81页

作者： Tselios, K. Zois, E. N. Siores, E. Nassiopoulos, A. Economou, G. Univ Bolton CMRI Bolton BL3 5AB England Technol & Educ Inst Athens Dept Elect Engn Aegaleo 12210 Greece Univ Patras Dept Phys Patras 26500 Greece

In this work, a feature extraction method is presented for handwritten signature verification. The proposed algorithm models the handwritten elements of a signature trace by probabilistically counting the distribution of fixed two-and three-step pixel paths, conditioned that they are confined within predetermined Chebyshev distances of two and three, respectively. This representation correlates the pixel transitions along the signature trace, with the writing style of an individual. Various partitions of the signature image into a group of sub-images were applied in order to define the overall dimensionality of the feature. In order to evaluate the classification efficiency of the introduced method, a number of verification strategies are implemented by making use of two internationally accepted and one domestic datasets. In all schemes, similarity scores and hard margin support vector machines (SVMs) are combined or evaluated as separate entities. Additionally, zoning the extracted feature vector into combinations of tetrads and heptads, which in turn are fed into the afore-mentioned classification schemes, is exploited. Results, derived from random or simple imitations as well as simulated (skilled) forgery indicate that the proposed method achieves noticeably low equal error rates and it is expected to provide a powerful discriminative representation of the handwritten signature.

关键词： Knowledge engineering techniques signature image Chebyshev distance simulated forgery handwritten signature verification handwriting recognition image recognition classification efficiency feature dimensionality three-step pixel path equal error rate support vector machines grid-based feature distribution SVM off-line signature verification similarity score handwritten element pixel transition Interpolation and function approximation (numerical analysis) image classification computer vision and image processing techniques writing style feature extraction hard margin support vector machine skilled forgery feature vector two-step pixel path signature trace Chebyshev approximation

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Recognition of driving postures by contourlet transform and random forests

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IET INTELLIGENT TRANSPORT SYSTEMS 2012年第2期6卷 161-168页

作者： Zhao, C. H. Zhang, B. L. He, J. Lian, J. Southeast Univ Coll Transportat Nanjing 210096 Jiangsu Peoples R China Xian Jiaotong Liverpool Univ Dept Comp Sci & Software Engn Suzhou 215123 Peoples R China

An efficient feature extraction approach for driving postures from a video camera, which consists of Homomorphic filtering, skin-like regions segmentation and contourlet transform (CT), was proposed. With features extracted from a driving posture dataset created at Southeast University (SEU), holdout and cross-validation experiments on driving posture classification were then conducted using random forests (RF) classifier. Compared with a number of commonly used classification methods including linear perceptron classifier, k-nearest-neighbour classifier and multilayer perceptron (MLP) classifier, the experiments results showed that the RF classifier offers the best classification performance among the four classifiers. Among the four predefined classes, that is, grasping the steering wheel, operating the shift gear, eating and talking on a cellular phone, the class of eating is the most difficult to classify. With RF classifier, the classification accuracies of eating are over 88% in holdout and cross-validation experiments, thus demonstrating the effectiveness of the proposed feature extraction method and the importance of RF classifier in automatically understanding and characterising driver's behaviours towards human-centric driver assistance systems.

关键词： Traffic engineering computing skin Neural computing techniques object recognition random forests classifier RF classifier homomorphic filtering CT multilayer perceptrons human-centric driver assistance systems MLP skin-like regions segmentation image recognition video camera computer vision and image processing techniques linear perceptron classifier Southeast University contourlet transform cellular phone talking driving posture recognition Integral transforms image segmentation SEU eating class image classification feature extraction approach feature extraction shift gear operation filtering theory steering wheel grasping k-nearest-neighbour classifier holdout experiments video cameras multilayer perceptron classifier cross-validation experiments transforms driving posture classification traffic engineering computing

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Fast and robust skew estimation in document images through bilinear filtering model

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IET image processing 2012年第6期6卷 761-769页

作者： Guan, Y-P. Shanghai Univ Sch Commun & Informat Engn Key Lab Adv Displays & Syst Applicat Minist Educ Shanghai 200072 Peoples R China

Skew estimation of scanned document is important for document analysis and recognition. Owing to the complexity inherent in the document, some methods only process the documents with a small skew angle or specified content, layout and have high computational cost. A fast and robust skew estimation method is proposed based on a bilinear filtering model, which is used to detect edges existing in the document. Some foreground areas in the document have been extracted without considering document layouts or contents. The proposed approach enhances the structure of the document and reduces the effects of the noises in the document. It combines filtering operators into a single approach, so noise filtering which is an unavoidable pre-processing in the previous skew detection methods has been overcome. A dominant angle has been estimated based on the detected edges. According to the estimated dominant angle, a skew angle can be determined efficiently without confining the search space or making assumptions including skew angle range and layout of the document in advance. The proposal greatly reduces the computational time and works in an unsupervised style. Comparative tests with the state-of-the-art skew estimation methods indicate the superior performance of the developed approach.

关键词： document image recognition edge detection filtering theory document analysis image recognition skew detection methods image denoising robust skew estimation method computer vision and image processing techniques noise filtering approach feature extraction Document processing techniques Filtering methods in signal processing dominant angle estimation document image processing small skew angle bilinear filtering model noise effect reduction

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Noise-resistant hemispherical basis for image-based relighting

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IET image processing 2012年第1期6卷 72-86页

作者： Lam, P. -M. Leung, C. -S. Wong, T. -T. City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China

The spherical harmonic (SH) basis has been widely used for representing relighting data. However, under some situations, the magnitudes of the estimated SH coefficients could be very large. Hence, relit images are very sensitive to quantisation (or compression) noise of these estimated SH coefficients. To tackle this issue, this study proposes a new spherical basis, namely eigen hemispherical harmonic (EHSH) basis. Its approximation ability is the same as that of the SH basis. With this new basis, the magnitudes of the estimated coefficients are controllable. Hence, the artefacts in the relit images can be suppressed. Besides, the transform from the classical hemispherical SH coefficients to the EHSH coefficients is discussed. Finally, the authors present the way to relight images based on the EHSH basis.

关键词： SH coefficient estimation quantisation (signal) image representation eigen hemispherical harmonic basis image denoising computer vision and image processing techniques relighting data representation Optical, image and video signal processing spherical harmonic basis quantisation noise image-based relighting SH basis relit images noise-resistant hemispherical basis EHSH basis

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Kolmogorov superposition theorem for image compression

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IET image processing 2012年第8期6卷 1114-1123页

作者： Leni, P. -E. Fougerolle, Y. D. Truchetet, F. Univ Burgundy IUT Le Creusot CNRS UMR 6306 LE2I Lab F-71200 Le Creusot France

The authors present a novel approach for image compression based on an unconventional representation of images. The proposed approach is different from most of the existing techniques in the literature because the compression is not directly performed on the image pixels, but is rather applied to an equivalent monovariate representation of the wavelet-transformed image. More precisely, the authors have considered an adaptation of Kolmogorov superposition theorem proposed by Igelnik and known as the Kolmogorov spline network (KSN), in which the image is approximated by sums and compositions of specific monovariate functions. Using this representation, the authors trade the local connectivity and the traditional line-per-line scanning, in exchange of a more adaptable and univariate representation of images, which allows to tackle the compression tasks in a fundamentally different representation. The contributions lie in the several strategies presented to adapt the KSN algorithm, including the monovariate construction, various simplification strategies, the proposal of a more suitable representation of the original image using wavelets and the integration of this scheme as an additional layer in the JPEG 2000 compression engine, illustrated for numerous images at different bit rates.

关键词： image representation splines (mathematics) data compression KSN algorithm JPEG 2000 compression computer vision and image processing techniques monovariate construction image and video coding image pixel image coding wavelet-transformed image monovariate representation wavelet transforms Interpolation and function approximation (numerical analysis) compression task image compression Kolmogorov spline network Kolmogorov superposition theorem line-per-line scanning Integral transforms in numerical analysis Igelnik monovariate function

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Multiobjective clustering with metaheuristic: current trends and methods in image segmentation

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IET image processing 2012年第1期6卷 1-10页

作者： Bong, C. W. Rajeswari, M. Univ Sains Malaysia Sch Comp Sci George Town Malaysia

This study reviews the state-of-the-art multiobjective optimisation (MOO) techniques with metaheuristic through clustering approaches developed specifically for image segmentation problems. The authors treat image segmentation as a real-life problem with multiple objectives;thus, focusing on MOO methods that allow a trade-off among multiple objectives. A reasonable solution to a multiobjective (MO) problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. The primary difference of MOO methods from traditional image segmentation is that instead of a single solution, their output is a set of solutions called Pareto-optimal solution. This study discusses the evolutionary and non-evolutionary MO clustering techniques for image segmentation. It diagnoses the requirements and issues for modelling MOO via MO clustering technique. In addition, the potential challenges and the directions for future research are presented.

关键词： real-life problem evolutionary computation image recognition multiobjective problem multiobjective clustering Pareto-optimal solution state-of-the-art multiobjective optimisation technique computer vision and image processing techniques MOO method Optical, image and video signal processing Pareto optimisation nonevolutionary MO clustering technique Optimisation techniques image segmentation pattern clustering

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Learning-based super-resolution method with a combining of both global and local constraints

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IET image processing 2012年第4期6卷 337-344页

作者： Guo, K. Yang, X. Lin, W. Zhang, R. Yu, S. Shanghai Jiao Tong Univ Inst Image Commun & Informat Proc Shanghai 200240 Peoples R China

Learning-based super-resolution (SR) methods are popular in many applications recently. In these methods, the high-frequency details are usually found or combined through patch matching from training database. However, the representation ability of small patch is limited and it is difficult to guarantee that the super-resolved image is the best under the global view. To this end, the authors propose a statistical learning method for SR with both global and local constraints. More specifically, they introduce a mixture model into maximum a posteriori (MAP) estimation, which combines a global parametric constraint with a patch-based local non-parametric constraint. The global parametric constraint guarantees the super-resolved global image to agree with the sparse property of natural images, and the local non-parametric constraint is used to infer the residues between the image derived from the global constraint and the ground truth high-resolution (HR) image. Compared with the traditional patch-based learning methods without the global constraint, our method can not only preserve global image structure, but also restore the local details more effectively. Experiments verify the effectiveness of the proposed method.

关键词： image resolution image representation patch matching global parametric constraint patch-based local nonparametric constraint Spatial and pictorial databases ground truth high-resolution image mixture model computer vision and image processing techniques Knowledge engineering techniques Optical, image and video signal processing maximum a posteriori estimation global image structure image matching maximum likelihood estimation statistical learning method super-resolved global image natural images learning-based super-resolution method representation ability SR methods visual databases learning (artificial intelligence) MAP estimation training database Other topics in statistics HR image

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