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Optimal look-up table-based data hiding

IET SIGNAL processing 2011年第2期5卷 171-179页

作者： Wang, X. Zhang, X. -P. Ryerson Univ Dept Elect & Comp Engn Toronto ON M5B 2K3 Canada

In this study, the authors present a novel data hiding scheme using the minimum distortion look-up table (LUT) embedding that achieves good distortion-robustness performance. LUT-based data hiding is a simple and efficient way to embed information into multimedia content for various applications, such as transaction tracking and database annotation. The authors find it possible to optimally reduce the data hiding-introduced distortion by designing the LUT according to the distribution of the host at a given robustness level. The authors first analyse the distortion introduced by LUT embedding and formulate its relationship with run constraints of LUT to construct an optimal coding problem. Subsequently, a Viterbi algorithm is presented to find the minimum distortion LUT. Then a new practical data hiding scheme using the optimal LUT is applied in the wavelet domain. Theoretical analysis and numerical results show that the new LUT design achieves not only less distortion but also more robustness than the traditional LUT-based data embedding schemes under common attacks such as Gaussian noise and JPEG compression.

关键词： computer vision and image processing techniques data compression image and video coding wavelet transforms telecommunication security table lookup Codes image coding distortion Viterbi coding Viterbi decoding JPEG compression LUT-based data hiding database annotation wavelet domain look-up table Integral transforms Data handling techniques transaction tracking

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image enhancement by wavelet-based thresholding neural network with adaptive learning rate

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IET image processing 2011年第7期5卷 573-582页

作者： Bhutada, G. G. Anand, R. S. Saxena, S. C. Indian Inst Technol Roorkee Dept Elect Engn Roorkee Uttar Pradesh India

A new approach has been proposed to improve the computational performance of denoising in which adaptively defined learning step size has been used for tuning the parameter of the thresholding function of wavelet transform-based thresholding neural network (WT-TNN) methodology. In this approach, steepest gradient-based learning step size of WT-TNN methodology are changed to the proposed adaptively defined learning step size for tuning the parameters of thresholding function. The results of the image enhanced by such adaptive learning step size exhibit the increase in the speed of learning and improved edge preservation feature. Further, the learning time has also become independent of noise level and initial values of learning parameters.

关键词： edge detection Neural computing techniques WT-TNN methodology image denoising computer vision and image processing techniques gradient-based learning step size wavelet-based thresholding neural network adaptive learning rate telecommunication computing Optical, image and video signal processing image enhancement edge preservation feature neural nets

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Hand vein biometry based on geometry and appearance methods

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IET computer vision 2011年第6期5卷 398-406页

作者： Yuksel, A. Akarun, L. Sankur, B. Bogazici Univ Dept Comp Engn Istanbul Turkey Bogazici Univ Dept Elect & Elect Engn Istanbul Turkey

Many biometric systems, such as face, fingerprint and iris have been studied extensively for personal verification and identification purposes. Biometric identification with vein patterns is a more recent approach that uses the vast network of blood vessels underneath a person's skin. These patterns in the hands are assumed to be unique to each individual and they do not change over time except in size. As veins are under the skin and have a wealth of differentiating features, an attempt to copy an identity is extremely difficult. These properties of uniqueness, stability and strong immunity to forgery of the vein patterns make it a potentially good biometric trait which offers greater security and reliable features for personal identification. In this study, the authors present a novel hand vein database and a biometric technique based on the statistical processing of the hand vein patterns. The BOSPHORUS hand vein database has been collected under realistic conditions in that subjects had to undergo the procedures of holding a bag, pressing an elastic ball and cooling with ice, all exercises that force changes in the vein patterns. The applied recognition techniques are a combination of geometric and appearance-based techniques and good identification performances have been obtained on the database.

关键词： Spatial and pictorial databases visual databases vein recognition biometric trait biometric identification hand vein biometry BOSPHORUS hand vein database hand vein pattern statistical processing geometric technique appearance methods computer vision and image processing techniques image recognition blood vessels

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Adaptive bandwidth mode detection algorithm

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IET image processing 2011年第8期5卷 645-660页

作者： Dagher, I. Dahdah, K. Univ Balamand Dept Comp Engn El Koura Lebanon

In this study a new algorithm 'adaptive bandwidth mode detection' (ABMD) algorithm has been developed to recover the correct density function without the need to either specify the correct number of Gaussians in the model or the correct bandwidth. The ABMD is employed in modelling visual features in applications such as image segmentation and real-time visual tracking. A simple type of model for these visual features are the Gaussian mixtures, where the number of Gaussian components is variable, thus, making it a flexible method for multimodal representation. This algorithm is used at initialisation for target modelling, where the target update will be done based on the mode propagation with adaptive bandwidth tracker method. It is based on an optimisation technique where a gradient ascent method is used and the optimal solution is selected based on a log-likelihood function. The mode detection ability of ABMD algorithm is compared with both the expectation maximisation and mean-shift algorithms. Furthermore, different video sequences have been employed to show how this approach has the ability to track an object regardless of whether the target model is corrupted with unwanted data at new frames.

关键词： Gaussian component number video sequences Optimisation techniques video signal processing Gaussian mixtures computer vision and image processing techniques density function recovery Gaussian processes Optical, image and video signal processing expectation maximisation algorithms image segmentation object tracking log-likelihood function multimodal representation image sequences optimisation technique mode propagation visual feature modelling ABMD algorithm optimisation Interpolation and function approximation (numerical analysis) mean-shift algorithms real-time visual tracking Video signal processing adaptive bandwidth mode detection algorithm adaptive bandwidth tracker method target modelling Other topics in statistics gradient methods gradient ascent method expectation-maximisation algorithm

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Calibrated micropost arrays for biomechanical characterisation of cardiomyocytes

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MICRO & NANO LETTERS 2011年第5期6卷 317-322页

作者： Kim, K. Taylor, R. Sim, J. Y. Park, S. -J. Norman, J. Fajardo, G. Bernstein, D. Pruitt, B. L. Stanford Univ Dept Mech Engn Sch Engn Stanford CA 94305 USA Stanford Univ Sch Med Dept Pediat Cardiol Stanford CA 94305 USA Stanford Univ Stanford Cardiovasc Inst Stanford CA 94305 USA

The study of biomechanics of isolated cardiomyocytes can allow us to understand the cardiac function and disease development in the absence of viscoelastic or contractile properties of the surrounding tissue. However, popular techniques such as micropipettes and carbon fibre-based measurements require serial, single-cell measurements and limit the amount of data passing through a system or process. The authors utilise elastomer micropost arrays microfabricated by a replica molding technique for precise and quantitative force measurements of cardiomyocytes with the potential for high throughput. The authors also present a calibration system using a piezoresistive force sensor and video-analysis technique to improve the resolution and validate the analysis of these low-force measurements. Calibrated microposts arrays are applied to measure the contractile forces of rat neonatal cardiomyocytes. Using image processing, the contractile forces for a sample of cardiomyocytes are extracted. It was found that isolated rat neonatal myocytes generate 39 +/- 5 nN average contractile force per post and an integrated axial contractile force of 189 +/- 20 nN, which is four times smaller than isolated adult Wistar rat cardiomyocytes and 30 times smaller than isolated adult rat cardiomyocytes. With calibration and quantitative image analysis, this study demonstrates that micropost systems can provide precise high-throughput test beds for myocyte mechanics using pharmacologic, small peptide, gene therapies or heart disease models.

关键词： carbon fibre-based measurement computer vision and image processing techniques Biology and medical computing calibrated micropost array biomechanical characterisation replica molding calibration medical image processing Biomedical measurement and imaging diseases force sensors Optical, image and video signal processing biomechanics video-analysis technique Physics of subcellular structures biological tissues contractile force disease development image processing rat neonatal cardiomyocyte biomedical engineering cellular biophysics biological tissue Mechanical properties of tissues and organs adult Wistar rat piezoresistive force sensor Mechanical variables measurement cardiac function micropipette Measurement standards and calibration elastomer Biomedical engineering

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Human body and posture recognition system based on an improved thinning algorithm

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IET image processing 2011年第5期5卷 420-428页

作者： Xie, F. Xu, G. Cheng, Y. Tian, Y. Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 210016 Peoples R China

This study presents a robust and reliable method of human posture recognition for visual surveillance systems. In order to recognise the human body, a recognition method is developed based on the skeleton of moving object. To obtain the skeleton of object, the authors describe some thinning algorithms for binary images, including one pass thinning algorithm, Zhang's thinning algorithm, Rosenfeld's thinning algorithm and a new thinning algorithm. Three performance measurements are chosen to evaluate these thinning algorithms. Comparing the performance results the authors found that the proposed thinning algorithm had managed to produce several improvements, including high thinness, connectivity, robustness to noise and low time consuming. Moreover, the skeleton obtained by the proposed thinning algorithm is one-pixel width and more smooth. Next, three different postures such as standing, bending and crawling will be estimated by using support vector machines as a classifier, which the histograms of horizontal and vertical projections are selected to define the feature. Finally, experimental results demonstrate that the human body and posture estimation algorithm have a robust and real-time performance, and is useful for the discrimination of human postures.

关键词： real-time systems horizontal histograms image recognition computer vision and image processing techniques Zhang thinning algorithm human body vertical projections posture recognition binary images video surveillance Rosenfeld thinning algorithm object detection moving object skeleton Video signal processing support vector machines posture estimation algorithm bone real-time performance pose estimation visual surveillance

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Robust adaptive directional lifting wavelet transform for image denoising

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IET image processing 2011年第3期5卷 249-260页

作者： Wang, X. T. Shi, G. M. Niu, Y. Zhang, L. Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China

Recent researches have shown that the adaptive directional lifting (ADL) can represent edges and textures in images effectively. This makes it possible to separate noise from image signal distinctly in image denoising. However, a key issue named orientation estimation for ADL becomes inefficient and error prone in the noised circumstance. The authors propose a robust adaptive directional lifting-based (RADL) wavelet transform for image denoising by constructing ADL in an anti-noise way. In our method, a simple model of pixel pattern classification is incorporated into orientation estimation module to strengthen the robustness of this algorithm. Moreover, instead of determining the transform strategy based on sub-blocks, RADL is performed on pixel-level to pursue better denoising results. Experimental results show that the proposed technique demonstrates both PSNR and visual quality improvement on images with rich textures.

关键词： pixel pattern classification image representation noise signal orientation estimation image denoising wavelet transform computer vision and image processing techniques image classification wavelet transforms Optical, image and video signal processing Integral transforms robust adaptive directional lifting image signal

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Extracting local texture features for image-based coin recognition

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IET image processing 2011年第5期5卷 394-401页

作者： Shen, L. Jia, S. Ji, Z. Chen, W. -S. ShenZhen Univ Sch Comp Sci & Software Engn Shenzhen Guangdong Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100088 Peoples R China ShenZhen Univ Sch Math Shenzhen Peoples R China

The authors propose to extract local texture features for image-based coin recognition in this study. A set of Gabor wavelets and local binary pattern (LBP) operator are employed to represent texture information. Concentric ring structure is used to divide the coin image into a number of small sections. Statistics of Gabor coefficients or LBP values within each section is then concatenated into a feature vector to represent the image. A circular shift operator is proposed to make Gabor features robust against rotation variance. Matching between two coin images is done via distance measurement. The nearest-neighbour classifier is used to classify a given test coin. The public MUSCLE database consisting of over 10 000 images is used to test our algorithms;results show that significant improvements over edge distance-based methods have been achieved. The authors have also analysed the performance of the system on recognising unregistered coins and the analysis suggests further improvement could be achieved if physical properties like diameter and thickness are included for feature representation.

关键词： image recognition feature representation computer vision and image processing techniques local binary pattern operator Gabor filters MUSCLE database image based coin recognition feature extraction Gabor wavelets image texture local texture feature extraction circular shift operator

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image measurement of body position during side-approach transfer motion

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IET image processing 2011年第5期5卷 402-409页

作者： Tanimoto, Y. Nanba, K. Tokuhiro, A. Ukida, H. Yamamoto, H. Kibikogen Rehabil Ctr Employment Injuries Dept Med Engn Okayama Japan Univ Tokushima Fac Engn Tokushima 770 Japan

In order to clarify the side-transfer ability of spinal cord injury (SCI) patients, it is necessary to analyse the side-transfer motion of SCI patients. This study describes a method for easy detection of the body position during the transfer motion using a simple human body model and side- and front-view images taken by using two charge-coupled device (CCD) cameras. The proposed simple human body model consists of five cuboids, nine columns and two elliptical cylinders. First, the wheelchair size was measured at the 11 points on its right side using a side- view image. Next, the wheelchair position was detected from the centre position of the right back wheel, and the gap angle between the platform and the wheelchair was detected from the edge position of both back wheels on the side- and front-view images. The positions of the 11 parts of the body model were estimated by matching each part to the side- and front-view images, and then the gravity centre of the body was calculated. Finally, the two images and the centre of gravity calculated every 1 s during the transfer motion were used to realise the movement of the body during the transfer motion.

关键词： image measurement Patient diagnostic methods and instrumentation CCD image sensors elliptical cylinders image recognition Biology and medical computing computer vision and image processing techniques spinal cord injury patients charge coupled device cameras time 1 s body position detection CCD cameras SCI patients gap angle human body model edge detection medical image processing Biomedical measurement and imaging centre of gravity calculation image sensors side-transfer motion wheelchair position edge position detection

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Weld-pool image centroid algorithm for seam-tracking vision model in arc-welding process

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IET image processing 2011年第5期5卷 410-419页

作者： Gao, X. Ding, D. Bai, T. Katayama, S. Guangdong Univ Technol Fac Mech & Elect Engn Guangzhou 510006 Guangdong Peoples R China Osaka Univ Joining & Welding Res Inst Osaka 5670047 Japan

Visual sensing is an attractive approach to detect the weld position in an arc-welding process, which provides information for seam tracking. However, it is difficult to accurately detect the weld position adjacent to a molten pool because of strong arc disturbances. A novel algorithm based on the weld-pool image centroid is presented to improve the seam-tracking ability. The molten pool images are taken by a camera arranged ahead of the welding torch and the centroid is extracted as a parameter to detect the weld position. It is worth noting that the centroid corresponds to the thermal distribution of the molten pool affected by the offset between the arc and the seam centreline. Therefore the offset between the arc and the seam centreline can be estimated by this centroid. The least square linear regression method is employed to correlate the relationship between the centroid and the offset under different welding conditions. For further analysis of the centroid characteristics, a non-linear neural network is designed with three input variables which are the position, displacement and moving velocity of the centroid, respectively. This neural network model shows higher accuracy of weld detection. In comparison with directly detecting the weld position, the centroid can be measured and computed easily. This algorithm is expected to provide a promising vision model to improve the accuracy of seam tracking in real time, and subsequently to ensure good welding quality.

关键词： Neural computing techniques arc-welding process seam centreline visual sensing regression analysis neural nets weld position detection weld-pool image centroid algorithm computer vision and image processing techniques image processing target tracking molten pool images Numerical analysis Optical, image and video signal processing least squares approximations nonlinear neural network arc welding Interpolation and function approximation (numerical analysis) thermal distribution position least square linear regression method welding torch moving velocity Statistics displacement Joining processes and welding Other topics in statistics seam-tracking vision model

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