Search results for: image analysis

Authors: Abhinav Kumar, Raja Sekhar Bandaru, B Madhusudan Rao, Saket Kulkarni, Nilesh Ghatpande

Abstract:

This paper proposes an algorithm which automatically aligns and stitches the component medical images (fluoroscopic) with varying degrees of overlap into a single composite image. The alignment method is based on similarity measure between the component images. As applied here the technique is intensity based rather than feature based. It works well in domains where feature based methods have difficulty, yet more robust than traditional correlation. Component images are stitched together using the new triangular averaging based blending algorithm. The quality of the resultant image is tested for photometric inconsistencies and geometric misalignments. This method cannot correct rotational, scale and perspective artifacts.

Keywords: Histogram Matching, Image Alignment, ImageStitching, Medical Imaging.

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Authors: Nagaraj V. Dharwadkar, B.B.Amberker

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In this paper, a new secure watermarking scheme for color image is proposed. It splits the watermark into two shares using (2, 2)- threshold Visual Cryptography Scheme (V CS) with Adaptive Order Dithering technique and embeds one share into high textured subband of Luminance channel of the color image. The other share is used as the key and is available only with the super-user or the author of the image. In this scheme only the super-user can reveal the original watermark. The proposed scheme is dynamic in the sense that to maintain the perceptual similarity between the original and the watermarked image the selected subband coefficients are modified by varying the watermark scaling factor. The experimental results demonstrate the effectiveness of the proposed scheme. Further, the proposed scheme is able to resist all common attacks even with strong amplitude.

Keywords: VCS, Dithering, HVS, DWT.

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Authors: Sepehr M.H.Jamarani, M.H.Moradi, H.Behnam, G.A.Rezai Rad

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This paper presents an approach for early breast cancer diagnostic by employing combination of artificial neural networks (ANN) and multiwaveletpacket based subband image decomposition. The microcalcifications correspond to high-frequency components of the image spectrum, detection of microcalcifications is achieved by decomposing the mammograms into different frequency subbands,, reconstructing the mammograms from the subbands containing only high frequencies. For this approach we employed different types of multiwaveletpacket. We used the result as an input of neural network for classification. The proposed methodology is tested using the Nijmegen and the Mammographic Image Analysis Society (MIAS) mammographic databases and images collected from local hospitals. Results are presented as the receiver operating characteristic (ROC) performance and are quantified by the area under the ROC curve.

Keywords: Breast cancer, neural networks, diagnosis, multiwavelet packet, microcalcification.

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Authors: Vahid Reza Nafisi, Mohammad Hasan Moradi, Mohammad Hosain Nasr-Esfahani

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The conventional assessment of human semen is a highly subjective assessment, with considerable intra- and interlaboratory variability. Computer-Assisted Sperm Analysis (CASA) systems provide a rapid and automated assessment of the sperm characteristics, together with improved standardization and quality control. However, the outcome of CASA systems is sensitive to the method of experimentation. While conventional CASA systems use digital microscopes with phase-contrast accessories, producing higher contrast images, we have used raw semen samples (no staining materials) and a regular light microscope, with a digital camera directly attached to its eyepiece, to insure cost benefits and simple assembling of the system. However, since the accurate finding of sperms in the semen image is the first step in the examination and analysis of the semen, any error in this step can affect the outcome of the analysis. This article introduces and explains an algorithm for finding sperms in low contrast images: First, an image enhancement algorithm is applied to remove extra particles from the image. Then, the foreground particles (including sperms and round cells) are segmented form the background. Finally, based on certain features and criteria, sperms are separated from other cells.

Keywords: Computer-Assisted Sperm Analysis (CASA), Sperm identification, Tail detection, Elliptic shape model.

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Authors: Peter U. Eze, P. Udaya, Robin J. Evans

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Data hiding can be achieved by Steganography or invisible digital watermarking. For digital watermarking, both accurate retrieval of the embedded watermark and the integrity of the cover image are important. Medical image security in Teleradiology is one of the applications where the embedded patient record needs to be extracted with accuracy as well as the medical image integrity verified. In this research paper, the Constant Correlation Spread Spectrum digital watermarking for medical image tamper detection and accurate embedded watermark retrieval is introduced. In the proposed method, a watermark bit from a patient record is spread in a medical image sub-block such that the correlation of all watermarked sub-blocks with a spreading code, W, would have a constant value, p. The constant correlation p, spreading code, W and the size of the sub-blocks constitute the secret key. Tamper detection is achieved by flagging any sub-block whose correlation value deviates by more than a small value, ℇ, from p. The major features of our new scheme include: (1) Improving watermark detection accuracy for high-pixel depth medical images by reducing the Bit Error Rate (BER) to Zero and (2) block-level tamper detection in a single computational process with simultaneous watermark detection, thereby increasing utility with the same computational cost.

Keywords: Constant correlation, medical image, spread spectrum, tamper detection, watermarking.

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Authors: Mana Tarjoman, Shaghayegh Zarei

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Using efficient classification methods is necessary for automatic fingerprint recognition system. This paper introduces a new structural approach to fingerprint classification by using the directional image of fingerprints to increase the number of subclasses. In this method, the directional image of fingerprints is segmented into regions consisting of pixels with the same direction. Afterwards the relational graph to the segmented image is constructed and according to it, the super graph including prominent information of this graph is formed. Ultimately we apply a matching technique to compare obtained graph with the model graphs in order to classify fingerprints by using cost function. Increasing the number of subclasses with acceptable accuracy in classification and faster processing in fingerprints recognition, makes this system superior.

Keywords: Classification, Directional image, Fingerprint, Graph, Super graph.

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Authors: Somayeh Sadeghi, Hamid A. Jalab, Sajjad Dadkhah

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Due to availability of powerful image processing software and improvement of human computer knowledge, it becomes easy to tamper images. Manipulation of digital images in different fields like court of law and medical imaging create a serious problem nowadays. Copy-move forgery is one of the most common types of forgery which copies some part of the image and pastes it to another part of the same image to cover an important scene. In this paper, a copy-move forgery detection method proposed based on Fourier transform to detect forgeries. Firstly, image is divided to same size blocks and Fourier transform is performed on each block. Similarity in the Fourier transform between different blocks provides an indication of the copy-move operation. The experimental results prove that the proposed method works on reasonable time and works well for gray scale and colour images. Computational complexity reduced by using Fourier transform in this method.

Keywords: Copy-Move forgery, Digital Forensics, Image Forgery.

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Authors: Abhijit Mitra

Abstract:

An effective approach for extracting document images from a noisy background is introduced. The entire scheme is divided into three sub- stechniques – the initial preprocessing operations for noise cluster tightening, introduction of a new thresholding method by maximizing the ratio of stan- dard deviations of the combined effect on the image to the sum of weighted classes and finally the image restoration phase by image binarization utiliz- ing the proposed optimum threshold level. The proposed method is found to be efficient compared to the existing schemes in terms of computational complexity as well as speed with better noise rejection.

Keywords: Document image extraction, Preprocessing, Ratio of stan-dard deviations, Bi-level adaptive thresholding.

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Authors: Jorge Martinez-Garcia, Ingrid Stelzner, Joerg Stelzner, Damian Gwerder, Philipp Schuetz

Abstract:

Tree-ring analysis is an important part of the quality assessment and the dating of (archaeological) wood samples. It provides quantitative data about the whole anatomical ring structure, which can be used, for example, to measure the impact of the fluctuating environment on the tree growth, for the dendrochronological analysis of archaeological wooden artefacts and to estimate the wood mechanical properties. Despite advances in computer vision and edge recognition algorithms, detection and counting of annual rings are still limited to 2D datasets and performed in most cases manually, which is a time consuming, tedious task and depends strongly on the operator’s experience. This work presents an image processing approach to detect the whole 3D tree-ring structure directly from X-ray computed tomography imaging data. The approach relies on a modified Canny edge detection algorithm, which captures fully connected tree-ring edges throughout the measured image stack and is validated on X-ray computed tomography data taken from six wood species.

Keywords: Ring recognition, edge detection, X-ray computed tomography, dendrochronology.

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Authors: Omaima N. Ahmad AL-Allaf

Abstract:

Over communication networks, images can be easily copied and distributed in an illegal way. The copyright protection for authors and owners is necessary. Therefore, the digital watermarking techniques play an important role as a valid solution for authority problems. Digital image watermarking techniques are used to hide watermarks into images to achieve copyright protection and prevent its illegal copy. Watermarks need to be robust to attacks and maintain data quality. Therefore, we discussed in this paper two approaches for image watermarking, first is based on Particle Swarm Optimization (PSO) and the second approach is based on Genetic Algorithm (GA). Discrete wavelet transformation (DWT) is used with the two approaches separately for embedding process to cover image transformation. Each of PSO and GA is based on co-relation coefficient to detect the high energy coefficient watermark bit in the original image and then hide the watermark in original image. Many experiments were conducted for the two approaches with different values of PSO and GA parameters. From experiments, PSO approach got better results with PSNR equal 53, MSE equal 0.0039. Whereas GA approach got PSNR equal 50.5 and MSE equal 0.0048 when using population size equal to 100, number of iterations equal to 150 and 3×3 block. According to the results, we can note that small block size can affect the quality of image watermarking based PSO/GA because small block size can increase the search area of the watermarking image. Better PSO results were obtained when using swarm size equal to 100.

Keywords: Image watermarking, genetic algorithm, particle swarm optimization, discrete wavelet transform.

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Authors: Yongquan Zhao, Bo Huang

Abstract:

Spatial, Temporal, and Spectral Resolution (STSR) are three key characteristics of Earth observation satellite sensors; however, any single satellite sensor cannot provide Earth observations with high STSR simultaneously because of the hardware technology limitations of satellite sensors. On the other hand, a conflicting circumstance is that the demand for high STSR has been growing with the remote sensing application development. Although image fusion technology provides a feasible means to overcome the limitations of the current Earth observation data, the current fusion technologies cannot enhance all STSR simultaneously and provide high enough resolution improvement level. This study proposes a Hybrid Spatial-Temporal-Spectral image Fusion Model (HSTSFM) to generate synthetic satellite data with high STSR simultaneously, which blends the high spatial resolution from the panchromatic image of Landsat-8 Operational Land Imager (OLI), the high temporal resolution from the multi-spectral image of Moderate Resolution Imaging Spectroradiometer (MODIS), and the high spectral resolution from the hyper-spectral image of Hyperion to produce high STSR images. The proposed HSTSFM contains three fusion modules: (1) spatial-spectral image fusion; (2) spatial-temporal image fusion; (3) temporal-spectral image fusion. A set of test data with both phenological and land cover type changes in Beijing suburb area, China is adopted to demonstrate the performance of the proposed method. The experimental results indicate that HSTSFM can produce fused image that has good spatial and spectral fidelity to the reference image, which means it has the potential to generate synthetic data to support the studies that require high STSR satellite imagery.

Keywords: Hybrid spatial-temporal-spectral fusion, high resolution synthetic imagery, least square regression, sparse representation, spectral transformation.

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Authors: Julia Moehrmann, Gunter Heidemann, Oliver Siemoneit, Christoph Hubig, Uwe-Philipp Kaeppeler, Paul Levi

Abstract:

Image data holds a large amount of different context information. However, as of today, these resources remain largely untouched. It is thus the aim of this paper to present a basic technical framework which allows for a quick and easy exploitation of context information from image data especially by non-expert users. Furthermore, the proposed framework is discussed in detail concerning important social and ethical issues which demand special requirements in system design. Finally, a first sensor prototype is presented which meets the identified requirements. Additionally, necessary implications for the software and hardware design of the system are discussed, rendering a sensor system which could be regarded as a good, acceptable and justifiable technical and thereby enabling the extraction of context information from image data.

Keywords: Context-aware computing, ethical and social issues, image recognition, requirements in system design.

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Authors: Veni.S , K.A.Narayanankutty

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For about two decades scientists have been developing techniques for enhancing the quality of medical images using Fourier transform, DWT (Discrete wavelet transform),PDE model etc., Gabor wavelet on hexagonal sampled grid of the images is proposed in this work. This method has optimal approximation theoretic performances, for a good quality image. The computational cost is considerably low when compared to similar processing in the rectangular domain. As X-ray images contain light scattered pixels, instead of unique sigma, the parameter sigma of 0.5 to 3 is found to satisfy most of the image interpolation requirements in terms of high Peak Signal-to-Noise Ratio (PSNR) , lower Mean Squared Error (MSE) and better image quality by adopting windowing technique.

Keywords: Hexagonal lattices, Gabor filter, Interpolation, imageprocessing.

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Authors: Asmatullah Chaudhry, Anwar M. Mirza

Abstract:

We present a general comparison of punctual kriging based image restoration for different neighbourhood sizes. The formulation of the technique under consideration is based on punctual kriging and fuzzy concepts for image restoration in spatial domain. Three different neighbourhood windows are considered to estimate the semivariance at different lags for studying its effect in reduction of negative weights resulted in punctual kriging, consequently restoration of degraded images. Our results show that effect of neighbourhood size higher than 5x5 on reduction in negative weights is insignificant. In addition, image quality measures, such as structure similarity indices, peak signal to noise ratios and the new variogram based quality measures; show that 3x3 window size gives better performance as compared with larger window sizes.

Keywords: Image restoration, punctual kriging, semi-variance, structure similarity index, negative weights in punctual kriging.

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Authors: Lina, Arlends Chris, Bagus Mulyawan, Agus B. Dharmawan

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Blood cell analysis plays a significant role in the diagnosis of human health. As an alternative to the traditional technique conducted by laboratory technicians, this paper presents an automatic white blood cell (leukocyte) detection system using Image Stitching and Color Overlapping Windows. The advantage of this method is to present a detection technique of white blood cells that are robust to imperfect shapes of blood cells with various image qualities. The input for this application is images from a microscope-slide translation video. The preprocessing stage is performed by stitching the input images. First, the overlapping parts of the images are determined, then stitching and blending processes of two input images are performed. Next, the Color Overlapping Windows is performed for white blood cell detection which consists of color filtering, window candidate checking, window marking, finds window overlaps, and window cropping processes. Experimental results show that this method could achieve an average of 82.12% detection accuracy of the leukocyte images.

Keywords: Color overlapping windows, image stitching, leukocyte detection.

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Authors: Zacharenia I. Garofalaki, John T. Amorginos, John N. Ellinas

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This paper presents the development of a robot car that can track the motion of an object by detecting its color through an Android device. The employed computer vision algorithm uses the OpenCV library, which is embedded into an Android application of a smartphone, for manipulating the captured image of the object. The captured image of the object is subjected to color conversion and is transformed to a binary image for further processing after color filtering. The desired object is clearly determined after removing pixel noise by applying image morphology operations and contour definition. Finally, the area and the center of the object are determined so that object’s motion to be tracked. The smartphone application has been placed on a robot car and transmits by Bluetooth to an Arduino assembly the motion directives so that to follow objects of a specified color. The experimental evaluation of the proposed algorithm shows reliable color detection and smooth tracking characteristics.

Keywords: Android, Arduino Uno, Image processing, Object motion detection, OpenCV library.

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Authors: D. R. Awang Rambli, S. Sulaiman, M.Y. Nayan, A.R. Asoruddin

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Existing image-based virtual reality applications allow users to view image-based 3D virtual environment in a more interactive manner. User could “walkthrough"; looks left, right, up and down and even zoom into objects in these virtual worlds of images. However what the user sees during a “zoom in" is just a close-up view of the same image which was taken from a distant. Thus, this does not give the user an accurate view of the object from the actual distance. In this paper, a simple technique for zooming in an object in a virtual scene is presented. The technique is based on the 'hotspot' concept in existing application. Instead of navigation between two different locations, the hotspots are used to focus into an object in the scene. For each object, several hotspots are created. A different picture is taken for each hotspot. Each consecutive hotspot created will take the user closer to the object. This will provide the user with a correct of view of the object based on his proximity to the object. Implementation issues and the relevance of this technique in potential application areas are highlighted.

Keywords: Hotspots, image-based VR, camera zooms, virtualreality.

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Authors: Kasthurirangan Gopalakrishnan, Siddhartha Khaitan, Anshu Manik

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Cluster analysis is the name given to a diverse collection of techniques that can be used to classify objects (e.g. individuals, quadrats, species etc). While Kohonen's Self-Organizing Feature Map (SOFM) or Self-Organizing Map (SOM) networks have been successfully applied as a classification tool to various problem domains, including speech recognition, image data compression, image or character recognition, robot control and medical diagnosis, its potential as a robust substitute for clustering analysis remains relatively unresearched. SOM networks combine competitive learning with dimensionality reduction by smoothing the clusters with respect to an a priori grid and provide a powerful tool for data visualization. In this paper, SOM is used for creating a toroidal mapping of two-dimensional lattice to perform cluster analysis on results of a chemical analysis of wines produced in the same region in Italy but derived from three different cultivators, referred to as the “wine recognition data" located in the University of California-Irvine database. The results are encouraging and it is believed that SOM would make an appealing and powerful decision-support system tool for clustering tasks and for data visualization.

Keywords: Artificial neural networks, cluster analysis, Kohonen maps, wine recognition.

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Authors: D.Gnanadurai, V.Sadasivam

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This frame work describes a computationally more efficient and adaptive threshold estimation method for image denoising in the wavelet domain based on Generalized Gaussian Distribution (GGD) modeling of subband coefficients. In this proposed method, the choice of the threshold estimation is carried out by analysing the statistical parameters of the wavelet subband coefficients like standard deviation, arithmetic mean and geometrical mean. The noisy image is first decomposed into many levels to obtain different frequency bands. Then soft thresholding method is used to remove the noisy coefficients, by fixing the optimum thresholding value by the proposed method. Experimental results on several test images by using this method show that this method yields significantly superior image quality and better Peak Signal to Noise Ratio (PSNR). Here, to prove the efficiency of this method in image denoising, we have compared this with various denoising methods like wiener filter, Average filter, VisuShrink and BayesShrink.

Keywords: Wavelet Transform, Gaussian Noise, ImageDenoising, Filter Banks and Thresholding.

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Authors: Swetha Kurup, Sridhar G., Sridhar V.

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In this paper we present a novel technique for data hiding in binary document images. We use the concept of entropy in order to identify document specific least distortive areas throughout the binary document image. The document image is treated as any other image and the proposed method utilizes the standard document characteristics for the embedding process. Proposed method minimizes perceptual distortion due to embedding and allows watermark extraction without the requirement of any side information at the decoder end.

Keywords: Entropy, Steganography, Watermarking.

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Authors: A. Mitra, Y. V. Subba Rao, S. R. M. Prasanna

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This paper proposes a new approach for image encryption using a combination of different permutation techniques. The main idea behind the present work is that an image can be viewed as an arrangement of bits, pixels and blocks. The intelligible information present in an image is due to the correlations among the bits, pixels and blocks in a given arrangement. This perceivable information can be reduced by decreasing the correlation among the bits, pixels and blocks using certain permutation techniques. This paper presents an approach for a random combination of the aforementioned permutations for image encryption. From the results, it is observed that the permutation of bits is effective in significantly reducing the correlation thereby decreasing the perceptual information, whereas the permutation of pixels and blocks are good at producing higher level security compared to bit permutation. A random combination method employing all the three techniques thus is observed to be useful for tactical security applications, where protection is needed only against a casual observer.

Keywords: Encryption, Permutation, Good key, Combinationalpermutation, Pseudo random index generator.

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Authors: Yanhua Jin, Won Suk Lee

Abstract:

The two-dimensional gel electrophoresis method (2-DE) is widely used in Proteomics to separate thousands of proteins in a sample. By comparing the protein expression levels of proteins in a normal sample with those in a diseased one, it is possible to identify a meaningful set of marker proteins for the targeted disease. The major shortcomings of this approach involve inherent noises and irregular geometric distortions of spots observed in 2-DE images. Various experimental conditions can be the major causes of these problems. In the protein analysis of samples, these problems eventually lead to incorrect conclusions. In order to minimize the influence of these problems, this paper proposes a partition based pair extension method that performs spot-matching on a set of gel images multiple times and segregates more reliable mapping results which can improve the accuracy of gel image analysis. The improved accuracy of the proposed method is analyzed through various experiments on real 2-DE images of human liver tissues.

Keywords: Proteomics, spot-matching, two-dimensionalelectrophoresis.

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Authors: Hossein Mirzaee, Farhad Besharati

Abstract:

Solar sunspot rotation, latitudinal bands are studied based on intelligent computation methods. A combination of image fusion method with together tree decomposition is used to obtain quantitative values about the latitudes of trajectories on sun surface that sunspots rotate around them. Daily solar images taken with SOlar and Heliospheric (SOHO) satellite are fused for each month separately .The result of fused image is decomposed with Quad Tree decomposition method in order to achieve the precise information about latitudes of sunspot trajectories. Such analysis is useful for gathering information about the regions on sun surface and coordinates in space that is more expose to solar geomagnetic storms, tremendous flares and hot plasma gases permeate interplanetary space and help human to serve their technical systems. Here sunspot images in September, November and October in 2001 are used for studying the magnetic behavior of sun.

Keywords: Quad tree decomposition, sunspot image.

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Authors: Shang-Lin Hsieh, Lung-Yao Hsu, I-Ju Tsai

Abstract:

This paper proposes a copyright protection scheme for color images using secret sharing and wavelet transform. The scheme contains two phases: the share image generation phase and the watermark retrieval phase. In the generation phase, the proposed scheme first converts the image into the YCbCr color space and creates a special sampling plane from the color space. Next, the scheme extracts the features from the sampling plane using the discrete wavelet transform. Then, the scheme employs the features and the watermark to generate a principal share image. In the retrieval phase, an expanded watermark is first reconstructed using the features of the suspect image and the principal share image. Next, the scheme reduces the additional noise to obtain the recovered watermark, which is then verified against the original watermark to examine the copyright. The experimental results show that the proposed scheme can resist several attacks such as JPEG compression, blurring, sharpening, noise addition, and cropping. The accuracy rates are all higher than 97%.

Keywords: Color image, copyright protection, discrete wavelet transform, secret sharing, watermarking.

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Authors: Ching-Sheng Hsu, Young-Chang Hou

Abstract:

In this paper, a novel copyright protection scheme for digital images based on Visual Cryptography and Statistics is proposed. In our scheme, the theories and properties of sampling distribution of means and visual cryptography are employed to achieve the requirements of robustness and security. Our method does not need to alter the original image and can identify the ownership without resorting to the original image. Besides, our method allows multiple watermarks to be registered for a single host image without causing any damage to other hidden watermarks. Moreover, it is also possible for our scheme to cast a larger watermark into a smaller host image. Finally, experimental results will show the robustness of our scheme against several common attacks.

Keywords: Copyright protection, digital watermarking, samplingdistribution, visual cryptography.

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Authors: Mario Mastriani

Abstract:

This paper describes a novel projection algorithm, the Projection Onto Span Algorithm (POSA) for wavelet-based superresolution and removing speckle (in wavelet domain) of unknown variance from Synthetic Aperture Radar (SAR) images. Although the POSA is good as a new superresolution algorithm for image enhancement, image metrology and biometric identification, here one will use it like a tool of despeckling, being the first time that an algorithm of super-resolution is used for despeckling of SAR images. Specifically, the speckled SAR image is decomposed into wavelet subbands; POSA is applied to the high subbands, and reconstruct a SAR image from the modified detail coefficients. Experimental results demonstrate that the new method compares favorably to several other despeckling methods on test SAR images.

Keywords: Projection, speckle, superresolution, synthetic aperture radar, thresholding, wavelets.

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Authors: Shannon Steinmetz, Ellen Gethner

Abstract:

In this paper a theoretical foundation is developed to segment, analyze and associate patterns within audio. We explore this on imagery via sonified audio applied to our segmentation framework. The approach involves a geodesic estimator within the statistical manifold, parameterized by musical centricity. We demonstrate viability by processing a database of random imagery to produce statistically significant clusters of similar imagery content.

Keywords: Sonification, musical information geometry, image content extraction, automated quantification, audio segmentation, pattern recognition.

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Authors: M. Almas Anjum, M. Younus Javed, A. Basit

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In this paper a new approach to face recognition is presented that achieves double dimension reduction, making the system computationally efficient with better recognition results and out perform common DCT technique of face recognition. In pattern recognition techniques, discriminative information of image increases with increase in resolution to a certain extent, consequently face recognition results change with change in face image resolution and provide optimal results when arriving at a certain resolution level. In the proposed model of face recognition, initially image decimation algorithm is applied on face image for dimension reduction to a certain resolution level which provides best recognition results. Due to increased computational speed and feature extraction potential of Discrete Cosine Transform (DCT), it is applied on face image. A subset of coefficients of DCT from low to mid frequencies that represent the face adequately and provides best recognition results is retained. A tradeoff between decimation factor, number of DCT coefficients retained and recognition rate with minimum computation is obtained. Preprocessing of the image is carried out to increase its robustness against variations in poses and illumination level. This new model has been tested on different databases which include ORL , Yale and EME color database.

Keywords: Biometrics, DCT, Face Recognition, Illumination, Computation, Feature extraction.

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Authors: Jayadevan R., Jayant V. Kulkarni, Suresh N. Mali, Hemant K. Abhyankar

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An important step in studying the statistics of fingerprint minutia features is to reliably extract minutia features from the fingerprint images. A new reliable method of computation for minutiae feature extraction from fingerprint images is presented. A fingerprint image is treated as a textured image. An orientation flow field of the ridges is computed for the fingerprint image. To accurately locate ridges, a new ridge orientation based computation method is proposed. After ridge segmentation a new method of computation is proposed for smoothing the ridges. The ridge skeleton image is obtained and then smoothed using morphological operators to detect the features. A post processing stage eliminates a large number of false features from the detected set of minutiae features. The detected features are observed to be reliable and accurate.

Keywords: Minutia, orientation field, ridge segmentation, textured image.

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Authors: Fahmi Kammoun, Mohamed Salim Bouhlel

Abstract:

In the framework of the image compression by Wavelet Transforms, we propose a perceptual method by incorporating Human Visual System (HVS) characteristics in the quantization stage. Indeed, human eyes haven-t an equal sensitivity across the frequency bandwidth. Therefore, the clarity of the reconstructed images can be improved by weighting the quantization according to the Contrast Sensitivity Function (CSF). The visual artifact at low bit rate is minimized. To evaluate our method, we use the Peak Signal to Noise Ratio (PSNR) and a new evaluating criteria witch takes into account visual criteria. The experimental results illustrate that our technique shows improvement on image quality at the same compression ratio.

Keywords: Contrast Sensitivity Function, Human Visual System, Image compression, Wavelet transforms.

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