Search results for: Image processing
2238 A Data Hiding Model with High Security Features Combining Finite State Machines and PMM method
Authors: Souvik Bhattacharyya, Gautam Sanyal
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Recent years have witnessed the rapid development of the Internet and telecommunication techniques. Information security is becoming more and more important. Applications such as covert communication, copyright protection, etc, stimulate the research of information hiding techniques. Traditionally, encryption is used to realize the communication security. However, important information is not protected once decoded. Steganography is the art and science of communicating in a way which hides the existence of the communication. Important information is firstly hidden in a host data, such as digital image, video or audio, etc, and then transmitted secretly to the receiver.In this paper a data hiding model with high security features combining both cryptography using finite state sequential machine and image based steganography technique for communicating information more securely between two locations is proposed. The authors incorporated the idea of secret key for authentication at both ends in order to achieve high level of security. Before the embedding operation the secret information has been encrypted with the help of finite-state sequential machine and segmented in different parts. The cover image is also segmented in different objects through normalized cut.Each part of the encoded secret information has been embedded with the help of a novel image steganographic method (PMM) on different cuts of the cover image to form different stego objects. Finally stego image is formed by combining different stego objects and transmit to the receiver side. At the receiving end different opposite processes should run to get the back the original secret message.Keywords: Cover Image, Finite state sequential machine, Melaymachine, Pixel Mapping Method (PMM), Stego Image, NCUT.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22512237 An Adaptive Mammographic Image Enhancement in Orthogonal Polynomials Domain
Authors: R. Krishnamoorthy, N. Amudhavalli, M.K. Sivakkolunthu
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X-ray mammography is the most effective method for the early detection of breast diseases. However, the typical diagnostic signs such as microcalcifications and masses are difficult to detect because mammograms are of low-contrast and noisy. In this paper, a new algorithm for image denoising and enhancement in Orthogonal Polynomials Transformation (OPT) is proposed for radiologists to screen mammograms. In this method, a set of OPT edge coefficients are scaled to a new set by a scale factor called OPT scale factor. The new set of coefficients is then inverse transformed resulting in contrast improved image. Applications of the proposed method to mammograms with subtle lesions are shown. To validate the effectiveness of the proposed method, we compare the results to those obtained by the Histogram Equalization (HE) and the Unsharp Masking (UM) methods. Our preliminary results strongly suggest that the proposed method offers considerably improved enhancement capability over the HE and UM methods.Keywords: mammograms, image enhancement, orthogonalpolynomials, contrast improvement
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20022236 Using Self Organizing Feature Maps for Classification in RGB Images
Authors: Hassan Masoumi, Ahad Salimi, Nazanin Barhemmat, Babak Gholami
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Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feedforward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on selforganizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification.Keywords: Classification, SOFM, neural network, RGB images.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23062235 Performance Improvement in the Bivariate Models by using Modified Marginal Variance of Noisy Observations for Image-Denoising Applications
Authors: R. Senthilkumar
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Most simple nonlinear thresholding rules for wavelet- based denoising assume that the wavelet coefficients are independent. However, wavelet coefficients of natural images have significant dependencies. This paper attempts to give a recipe for selecting one of the popular image-denoising algorithms based on VisuShrink, SureShrink, OracleShrink, BayesShrink and BiShrink and also this paper compares different Bivariate models used for image denoising applications. The first part of the paper compares different Shrinkage functions used for image-denoising. The second part of the paper compares different bivariate models and the third part of this paper uses the Bivariate model with modified marginal variance which is based on Laplacian assumption. This paper gives an experimental comparison on six 512x512 commonly used images, Lenna, Barbara, Goldhill, Clown, Boat and Stonehenge. The following noise powers 25dB,26dB, 27dB, 28dB and 29dB are added to the six standard images and the corresponding Peak Signal to Noise Ratio (PSNR) values are calculated for each noise level.Keywords: BiShrink, Image-Denoising, PSNR, Shrinkage function
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13342234 Real Time Speed Estimation of Vehicles
Authors: Azhar Hussain, Kashif Shahzad, Chunming Tang
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this paper gives a novel approach towards real-time speed estimation of multiple traffic vehicles using fuzzy logic and image processing techniques with proper arrangement of camera parameters. The described algorithm consists of several important steps. First, the background is estimated by computing median over time window of specific frames. Second, the foreground is extracted using fuzzy similarity approach (FSA) between estimated background pixels and the current frame pixels containing foreground and background. Third, the traffic lanes are divided into two parts for both direction vehicles for parallel processing. Finally, the speeds of vehicles are estimated by Maximum a Posterior Probability (MAP) estimator. True ground speed is determined by utilizing infrared sensors for three different vehicles and the results are compared to the proposed algorithm with an accuracy of ± 0.74 kmph.
Keywords: Defuzzification, Fuzzy similarity approach, lane cropping, Maximum a Posterior Probability (MAP) estimator, Speed estimation
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 27932233 A Genetic Algorithm for Clustering on Image Data
Authors: Qin Ding, Jim Gasvoda
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Clustering is the process of subdividing an input data set into a desired number of subgroups so that members of the same subgroup are similar and members of different subgroups have diverse properties. Many heuristic algorithms have been applied to the clustering problem, which is known to be NP Hard. Genetic algorithms have been used in a wide variety of fields to perform clustering, however, the technique normally has a long running time in terms of input set size. This paper proposes an efficient genetic algorithm for clustering on very large data sets, especially on image data sets. The genetic algorithm uses the most time efficient techniques along with preprocessing of the input data set. We test our algorithm on both artificial and real image data sets, both of which are of large size. The experimental results show that our algorithm outperforms the k-means algorithm in terms of running time as well as the quality of the clustering.
Keywords: Clustering, data mining, genetic algorithm, image data.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20332232 Improved Posterized Color Images based on Color Quantization and Contrast Enhancement
Authors: Oh-Yeol Kwon, Sung-Il Chien
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A conventional image posterization method occasionally fails to preserve the shape and color of objects due to the uneffective color reduction. This paper proposes a new image posterizartion method by using modified color quantization for preserving the shape and color of objects and color contrast enhancement for improving lightness contrast and saturation. Experiment results show that our proposed method can provide visually more satisfactory posterization result than that of the conventional method.Keywords: Color contrast enhancement, color quantization, color segmentation, image posterization
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 26692231 Data Hiding by Vector Quantization in Color Image
Authors: Yung-Gi Wu
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With the growing of computer and network, digital data can be spread to anywhere in the world quickly. In addition, digital data can also be copied or tampered easily so that the security issue becomes an important topic in the protection of digital data. Digital watermark is a method to protect the ownership of digital data. Embedding the watermark will influence the quality certainly. In this paper, Vector Quantization (VQ) is used to embed the watermark into the image to fulfill the goal of data hiding. This kind of watermarking is invisible which means that the users will not conscious the existing of embedded watermark even though the embedded image has tiny difference compared to the original image. Meanwhile, VQ needs a lot of computation burden so that we adopt a fast VQ encoding scheme by partial distortion searching (PDS) and mean approximation scheme to speed up the data hiding process. The watermarks we hide to the image could be gray, bi-level and color images. Texts are also can be regarded as watermark to embed. In order to test the robustness of the system, we adopt Photoshop to fulfill sharpen, cropping and altering to check if the extracted watermark is still recognizable. Experimental results demonstrate that the proposed system can resist the above three kinds of tampering in general cases.Keywords: Data hiding, vector quantization, watermark.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17672230 Intelligent Assistive Methods for Diagnosis of Rheumatoid Arthritis Using Histogram Smoothing and Feature Extraction of Bone Images
Authors: SP. Chokkalingam, K. Komathy
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Advances in the field of image processing envision a new era of evaluation techniques and application of procedures in various different fields. One such field being considered is the biomedical field for prognosis as well as diagnosis of diseases. This plethora of methods though provides a wide range of options to select from, it also proves confusion in selecting the apt process and also in finding which one is more suitable. Our objective is to use a series of techniques on bone scans, so as to detect the occurrence of rheumatoid arthritis (RA) as accurately as possible. Amongst other techniques existing in the field our proposed system tends to be more effective as it depends on new methodologies that have been proved to be better and more consistent than others. Computer aided diagnosis will provide more accurate and infallible rate of consistency that will help to improve the efficiency of the system. The image first undergoes histogram smoothing and specification, morphing operation, boundary detection by edge following algorithm and finally image subtraction to determine the presence of rheumatoid arthritis in a more efficient and effective way. Using preprocessing noises are removed from images and using segmentation, region of interest is found and Histogram smoothing is applied for a specific portion of the images. Gray level co-occurrence matrix (GLCM) features like Mean, Median, Energy, Correlation, Bone Mineral Density (BMD) and etc. After finding all the features it stores in the database. This dataset is trained with inflamed and noninflamed values and with the help of neural network all the new images are checked properly for their status and Rough set is implemented for further reduction.
Keywords: Computer Aided Diagnosis, Edge Detection, Histogram Smoothing, Rheumatoid Arthritis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24732229 Basic Study of Mammographic Image Magnification System with Eye-Detector and Simple EEG Scanner
Authors: A. Umemuro, M. Sato, M. Narita, S. Hori, S. Sakurai, T. Nakayama, A. Nakazawa, T. Ogura
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Mammography requires the detection of very small calcifications, and physicians search for microcalcifications by magnifying the images as they read them. The mouse is necessary to zoom in on the images, but this can be tiring and distracting when many images are read in a single day. Therefore, an image magnification system combining an eye-detector and a simple electroencephalograph (EEG) scanner was devised, and its operability was evaluated. Two experiments were conducted in this study: the measurement of eye-detection error using an eye-detector and the measurement of the time required for image magnification using a simple EEG scanner. Eye-detector validation showed that the mean distance of eye-detection error ranged from 0.64 cm to 2.17 cm, with an overall mean of 1.24 ± 0.81 cm for the observers. The results showed that the eye detection error was small enough for the magnified area of the mammographic image. The average time required for point magnification in the verification of the simple EEG scanner ranged from 5.85 to 16.73 seconds, and individual differences were observed. The reason for this may be that the size of the simple EEG scanner used was not adjustable, so it did not fit well for some subjects. The use of a simple EEG scanner with size adjustment would solve this problem. Therefore, the image magnification system using the eye-detector and the simple EEG scanner is useful.
Keywords: EEG scanner, eye-detector, mammography, observers.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3302228 A New Approach for Image Segmentation using Pillar-Kmeans Algorithm
Authors: Ali Ridho Barakbah, Yasushi Kiyoki
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This paper presents a new approach for image segmentation by applying Pillar-Kmeans algorithm. This segmentation process includes a new mechanism for clustering the elements of high-resolution images in order to improve precision and reduce computation time. The system applies K-means clustering to the image segmentation after optimized by Pillar Algorithm. The Pillar algorithm considers the pillars- placement which should be located as far as possible from each other to withstand against the pressure distribution of a roof, as identical to the number of centroids amongst the data distribution. This algorithm is able to optimize the K-means clustering for image segmentation in aspects of precision and computation time. It designates the initial centroids- positions by calculating the accumulated distance metric between each data point and all previous centroids, and then selects data points which have the maximum distance as new initial centroids. This algorithm distributes all initial centroids according to the maximum accumulated distance metric. This paper evaluates the proposed approach for image segmentation by comparing with K-means and Gaussian Mixture Model algorithm and involving RGB, HSV, HSL and CIELAB color spaces. The experimental results clarify the effectiveness of our approach to improve the segmentation quality in aspects of precision and computational time.Keywords: Image segmentation, K-means clustering, Pillaralgorithm, color spaces.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 33572227 Super Resolution Blind Reconstruction of Low Resolution Images using Wavelets based Fusion
Authors: Liyakathunisa, V. K. Ananthashayana
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Crucial information barely visible to the human eye is often embedded in a series of low resolution images taken of the same scene. Super resolution reconstruction is the process of combining several low resolution images into a single higher resolution image. The ideal algorithm should be fast, and should add sharpness and details, both at edges and in regions without adding artifacts. In this paper we propose a super resolution blind reconstruction technique for linearly degraded images. In our proposed technique the algorithm is divided into three parts an image registration, wavelets based fusion and an image restoration. In this paper three low resolution images are considered which may sub pixels shifted, rotated, blurred or noisy, the sub pixel shifted images are registered using affine transformation model; A wavelet based fusion is performed and the noise is removed using soft thresolding. Our proposed technique reduces blocking artifacts and also smoothens the edges and it is also able to restore high frequency details in an image. Our technique is efficient and computationally fast having clear perspective of real time implementation.Keywords: Affine Transforms, Denoiseing, DWT, Fusion, Image registration.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 26582226 Electronic System Design for Respiratory Signal Processing
Authors: C. Matiz C., N. Olarte L., A. Rubiano F.
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This paper presents the design related to the electronic system design of the respiratory signal, including phases for processing, followed by the transmission and reception of this signal and finally display. The processing of this signal is added to the ECG and temperature sign, put up last year. Under this scheme is proposed that in future also be conditioned blood pressure signal under the same final printed circuit and worked.Keywords: Conditioning, Respiratory Signal, Storage, Teleconsultation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23382225 An Edge-based Text Region Extraction Algorithm for Indoor Mobile Robot Navigation
Authors: Jagath Samarabandu, Xiaoqing Liu
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Using bottom-up image processing algorithms to predict human eye fixations and extract the relevant embedded information in images has been widely applied in the design of active machine vision systems. Scene text is an important feature to be extracted, especially in vision-based mobile robot navigation as many potential landmarks such as nameplates and information signs contain text. This paper proposes an edge-based text region extraction algorithm, which is robust with respect to font sizes, styles, color/intensity, orientations, and effects of illumination, reflections, shadows, perspective distortion, and the complexity of image backgrounds. Performance of the proposed algorithm is compared against a number of widely used text localization algorithms and the results show that this method can quickly and effectively localize and extract text regions from real scenes and can be used in mobile robot navigation under an indoor environment to detect text based landmarks.
Keywords: Landmarks, mobile robot navigation, scene text, text localization and extraction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 29112224 Rural Women’s Skill Acquisition in the Processing of Locust Bean in Ipokia Local Government Area of Ogun State, Nigeria
Authors: A. A. Adekunle, A. M. Omoare, W. O. Oyediran
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This study was carried out to assess rural women’s skill acquisition in the processing of locust bean in Ipokia Local Government Area of Ogun State, Nigeria. Simple random sampling technique was used to select 90 women locust bean processors for this study. Data were analyzed with descriptive statistics and Pearson Product Moment Correlation. The result showed that the mean age of respondents was 40.72 years. Most (70.00%) of the respondents were married. The mean processing experience was 8.63 years. 93.30% of the respondents relied on information from fellow locust beans processors and friends. All (100%) the respondents did not acquire improved processing skill through trainings and workshops. It can be concluded that the rural women’s skill acquisition on modernized processing techniques was generally low. It is hereby recommend that the rural women processors should be trained by extension service providers through series of workshops and seminars on improved processing techniques.
Keywords: Locust bean, processing, skill acquisition, rural women.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 28092223 Evaluation of Ultrasonic C-Scan Images by Fractal Dimension
Authors: S. Samanta, D. Datta, S. S. Gautam
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In this paper, quantitative evaluation of ultrasonic Cscan images through estimation of their Fractal Dimension (FD) is discussed. Necessary algorithm for evaluation of FD of any 2-D digitized image is implemented by developing a computer code. For the evaluation purpose several C-scan images of the Kevlar composite impacted by high speed bullet and glass fibre composite having flaw in the form of inclusion is used. This analysis automatically differentiates a C-scan image showing distinct damage zone, from an image that contains no such damage.Keywords: C-scan, Impact, Fractal Dimension, Kevlar composite and Inclusion Flaw
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17352222 Unsupervised Segmentation by Hidden Markov Chain with Bi-dimensional Observed Process
Authors: Abdelali Joumad, Abdelaziz Nasroallah
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In unsupervised segmentation context, we propose a bi-dimensional hidden Markov chain model (X,Y) that we adapt to the image segmentation problem. The bi-dimensional observed process Y = (Y 1, Y 2) is such that Y 1 represents the noisy image and Y 2 represents a noisy supplementary information on the image, for example a noisy proportion of pixels of the same type in a neighborhood of the current pixel. The proposed model can be seen as a competitive alternative to the Hilbert-Peano scan. We propose a bayesian algorithm to estimate parameters of the considered model. The performance of this algorithm is globally favorable, compared to the bi-dimensional EM algorithm through numerical and visual data.
Keywords: Image segmentation, Hidden Markov chain with a bi-dimensional observed process, Peano-Hilbert scan, Bayesian approach, MCMC methods, Bi-dimensional EM algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15992221 Multichannel Image Mosaicing of Stem Cells
Authors: Alessandro Bevilacqua, Alessandro Gherardi, Filippo Piccinini
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Image mosaicing techniques are usually employed to offer researchers a wider field of view of microscopic image of biological samples. a mosaic is commonly achieved using automated microscopes and often with one “color" channel, whether it refers to natural or fluorescent analysis. In this work we present a method to achieve three subsequent mosaics of the same part of a stem cell culture analyzed in phase contrast and in fluorescence, with a common non-automated inverted microscope. The mosaics obtained are then merged together to mark, in the original contrast phase images, nuclei and cytoplasm of the cells referring to a mosaic of the culture, rather than to single images. The experiments carried out prove the effectiveness of our approach with cultures of cells stained with calcein (green/cytoplasm and nuclei) and hoechst (blue/nuclei) probes.
Keywords: Microscopy, image mosaicing, fluorescence, stem cells.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14762220 A Differential Calculus Based Image Steganography with Crossover
Authors: Srilekha Mukherjee, Subha Ash, Goutam Sanyal
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Information security plays a major role in uplifting the standard of secured communications via global media. In this paper, we have suggested a technique of encryption followed by insertion before transmission. Here, we have implemented two different concepts to carry out the above-specified tasks. We have used a two-point crossover technique of the genetic algorithm to facilitate the encryption process. For each of the uniquely identified rows of pixels, different mathematical methodologies are applied for several conditions checking, in order to figure out all the parent pixels on which we perform the crossover operation. This is done by selecting two crossover points within the pixels thereby producing the newly encrypted child pixels, and hence the encrypted cover image. In the next lap, the first and second order derivative operators are evaluated to increase the security and robustness. The last lap further ensures reapplication of the crossover procedure to form the final stego-image. The complexity of this system as a whole is huge, thereby dissuading the third party interferences. Also, the embedding capacity is very high. Therefore, a larger amount of secret image information can be hidden. The imperceptible vision of the obtained stego-image clearly proves the proficiency of this approach.Keywords: Steganography, Crossover, Differential Calculus, Peak Signal to Noise Ratio, Cross-correlation Coefficient.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13752219 Disparity Estimation for Objects of Interest
Authors: Yen San Yong, Hock Woon Hon
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An algorithm for estimating the disparity of objects of interest is proposed. This algorithm uses image shifting and overlapping area to estimate the disparity value; thereby depth of the objects of interest can be obtained. The algorithm is able to perform at different levels of accuracy. However, as the accuracy increases the processing speed decreases. The algorithm is tested with static stereo images and sequence of stereo images. The experimental results are presented in this paper.Keywords: stereo vision, binocular parallax
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12172218 Threshold Based Region Incrementing Secret Sharing Scheme for Color Images
Authors: P. Mohamed Fathimal, P. Arockia Jansi Rani
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In this era of online communication, which transacts data in 0s and 1s, confidentiality is a priced commodity. Ensuring safe transmission of encrypted data and their uncorrupted recovery is a matter of prime concern. Among the several techniques for secure sharing of images, this paper proposes a k out of n region incrementing image sharing scheme for color images. The highlight of this scheme is the use of simple Boolean and arithmetic operations for generating shares and the Lagrange interpolation polynomial for authenticating shares. Additionally, this scheme addresses problems faced by existing algorithms such as color reversal and pixel expansion. This paper regenerates the original secret image whereas the existing systems regenerates only the half toned secret image.Keywords: Threshold Secret Sharing Scheme, Access Control, Steganography, Authentication, Secret Image Sharing, XOR, Pixel Expansion.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 11302217 Multifunctional Cell Processing with Plasmonic Nanobubbles
Authors: Ekaterina Y. Lukianova-Hleb, Dmitri O. Lapotko
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Cell processing techniques for gene and cell therapies use several separate procedures for gene transfer and cell separation or elimination, because no current technology can offer simultaneous multi-functional processing of specific cell sub-sets in heterogeneous cell systems. Using our novel on-demand nonstationary intracellular events instead of permanent materials, plasmonic nanobubbles, generated with a short laser pulse only in target cells, we achieved simultaneous multifunctional cell-specific processing with the rate up to 50 million cells per minute.
Keywords: Delivery, cell separation, graft, laser, plasmonic nanobubble, cell therapy, gold nanoparticle.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17172216 Design of Auto Exposure Unit Based On 2-Way Histogram Equalization
Authors: Junghwan Choi, Seongsoo Lee
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Histogram equalization is often used in image enhancement, but it can be also used in auto exposure. However, conventional histogram equalization does not work well when many pixels are concentrated in a narrow luminance range.This paper proposes an auto exposure method based on 2-way histogram equalization. Two cumulative distribution functions are used, where one is from dark to bright and the other is from bright to dark. In this paper, the proposed auto exposure method is also designed and implemented for image signal processors with full-HD images.
Keywords: Histogram equalization, Auto exposure, Image signal processor, Low-cost, Full HD Video.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 33822215 Mathematical Reconstruction of an Object Image Using X-Ray Interferometric Fourier Holography Method
Authors: M. K. Balyan
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The main principles of X-ray Fourier interferometric holography method are discussed. The object image is reconstructed by the mathematical method of Fourier transformation. The three methods are presented – method of approximation, iteration method and step by step method. As an example the complex amplitude transmission coefficient reconstruction of a beryllium wire is considered. The results reconstructed by three presented methods are compared. The best results are obtained by means of step by step method.
Keywords: Dynamical diffraction, hologram, object image, X-ray holography.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14112214 A Way of Converting Color Images to Gray Scale Ones for the Color-Blind -Applying to the Part of the Tokyo Subway Map-
Authors: Katsuhiro Narikiyo, Shota Hashikawa
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This paper proposes a way of removing noises and reducing the number of colors contained in a JPEG image. Main purpose of this project is to convert color images to monochrome images for the color-blind. We treat the crispy color images like the Tokyo subway map. Each color in the image has an important information. But for the color blinds, similar colors cannot be distinguished. If we can convert those colors to different gray values, they can distinguish them. Therefore we try to convert color images to monochrome images.
Keywords: Color-blind, JPEG, Monochrome image, Denoise.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15082213 A Method for Quality Inspection of Motors by Detecting Abnormal Sound
Authors: Tadatsugu Kitamoto
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Recently, a quality of motors is inspected by human ears. In this paper, I propose two systems using a method of speech recognition for automation of the inspection. The first system is based on a method of linear processing which uses K-means and Nearest Neighbor method, and the second is based on a method of non-linear processing which uses neural networks. I used motor sounds in these systems, and I successfully recognize 86.67% of motor sounds in the linear processing system and 97.78% in the non-linear processing system.Keywords: Acoustical diagnosis, Neural networks, K-means, Short-time Fourier transformation
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16912212 A Dynamic RGB Intensity Based Steganography Scheme
Authors: Mandep Kaur, Surbhi Gupta, Parvinder S. Sandhu, Jagdeep Kaur
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Steganography meaning covered writing. Steganography includes the concealment of information within computer files [1]. In other words, it is the Secret communication by hiding the existence of message. In this paper, we will refer to cover image, to indicate the images that do not yet contain a secret message, while we will refer to stego images, to indicate an image with an embedded secret message. Moreover, we will refer to the secret message as stego-message or hidden message. In this paper, we proposed a technique called RGB intensity based steganography model as RGB model is the technique used in this field to hide the data. The methods used here are based on the manipulation of the least significant bits of pixel values [3][4] or the rearrangement of colors to create least significant bit or parity bit patterns, which correspond to the message being hidden. The proposed technique attempts to overcome the problem of the sequential fashion and the use of stego-key to select the pixels.
Keywords: Steganography, Stego Image, RGB Image, Cryptography, LSB.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21022211 Local Linear Model Tree (LOLIMOT) Reconfigurable Parallel Hardware
Authors: A. Pedram, M. R. Jamali, T. Pedram, S. M. Fakhraie, C. Lucas
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Local Linear Neuro-Fuzzy Models (LLNFM) like other neuro- fuzzy systems are adaptive networks and provide robust learning capabilities and are widely utilized in various applications such as pattern recognition, system identification, image processing and prediction. Local linear model tree (LOLIMOT) is a type of Takagi-Sugeno-Kang neuro fuzzy algorithm which has proven its efficiency compared with other neuro fuzzy networks in learning the nonlinear systems and pattern recognition. In this paper, a dedicated reconfigurable and parallel processing hardware for LOLIMOT algorithm and its applications are presented. This hardware realizes on-chip learning which gives it the capability to work as a standalone device in a system. The synthesis results on FPGA platforms show its potential to improve the speed at least 250 of times faster than software implemented algorithms.
Keywords: LOLIMOT, hardware, neurofuzzy systems, reconfigurable, parallel.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 38712210 CT Medical Images Denoising Based on New Wavelet Thresholding Compared with Curvelet and Contourlet
Authors: Amir Moslemi, Amir Movafeghi, Shahab Moradi
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One of the most important challenging factors in medical images is nominated as noise. Image denoising refers to the improvement of a digital medical image that has been infected by Additive White Gaussian Noise (AWGN). The digital medical image or video can be affected by different types of noises. They are impulse noise, Poisson noise and AWGN. Computed tomography (CT) images are subjects to low quality due to the noise. Quality of CT images is dependent on absorbed dose to patients directly in such a way that increase in absorbed radiation, consequently absorbed dose to patients (ADP), enhances the CT images quality. In this manner, noise reduction techniques on purpose of images quality enhancement exposing no excess radiation to patients is one the challenging problems for CT images processing. In this work, noise reduction in CT images was performed using two different directional 2 dimensional (2D) transformations; i.e., Curvelet and Contourlet and Discrete Wavelet Transform (DWT) thresholding methods of BayesShrink and AdaptShrink, compared to each other and we proposed a new threshold in wavelet domain for not only noise reduction but also edge retaining, consequently the proposed method retains the modified coefficients significantly that result good visual quality. Data evaluations were accomplished by using two criterions; namely, peak signal to noise ratio (PSNR) and Structure similarity (Ssim).Keywords: Computed Tomography (CT), noise reduction, curve-let, contour-let, Signal to Noise Peak-Peak Ratio (PSNR), Structure Similarity (Ssim), Absorbed Dose to Patient (ADP).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 29092209 Hyperspectral Mapping Methods for Differentiating Mangrove Species along Karachi Coast
Authors: Sher Muhammad, Mirza Muhammad Waqar
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It is necessary to monitor and identify mangroves types and spatial extent near coastal areas because it plays an important role in coastal ecosystem and environmental protection. This research aims at identifying and mapping mangroves types along Karachi coast ranging from 24.790 to 24.850 in latitude and 66.910 to 66.970 in longitude using hyperspectral remote sensing data and techniques. Image acquired during February, 2012 through Hyperion sensor have been used for this research. Image pre processing includes geometric and radiometric correction followed by Minimum Noise Fraction (MNF) and Pixel Purity Index (PPI). The output of MNF and PPI has been analyzed by visualizing it in n-dimensions for end member extraction. Well distributed clusters on the n-dimensional scatter plot have been selected with the region of interest (ROI) tool as end members. These end members have been used as an input for classification techniques applied to identify and map mangroves species including Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF) and Spectral Information Diversion (SID). Only two types of mangroves namely Avicennia Marina (White Mangroves) and Avicennia germinans (Black Mangroves) have been observed throughout the study area.
Keywords: Mangrove, Hyperspectral, SAM, SFF, SID.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2897