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Digital image watermarking using uncorrelated color space

Digital image watermarking using uncorrelated color space

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International conference on Computer applications and Industrial Electronics (ICCAIE)

作者： Manish Gupta Rajeev Gupta Girish Parmar Mukesh Saraswat University College of Engineering Rajasthan Technical University Kota India University College of Engineering Rajasthan Technical University Sambalpur Odisha IN Motilal Nehru National Institute of Technology Allahabad India

The exponential growths in electronic data over internet have increase the demand of a robust and high quality watermarking method for authentication and copyright protection. The available digital image watermarking methods generally use the correlated color spaces which impose the limitations to researchers for using only one color component at a time for embedding the watermark. Therefore, in this paper, a novel DWT based digital image watermarking method has been proposed which embeds the gray scale watermark into color host image using uncorrelated color space (UCS) and genetic algorithm (GA) method. The result shows that the proposed method outperforms other existing methods against the various signal processing attacks. The proposed method is a first inline report which uses uncorrelated color space for digital image watermarking.

关键词： Watermarking image color analysis Robustness Discrete wavelet transforms Digital images Educational institutions Color

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An FPGA Based Facial Component Extractor using wavelet-Based Salient Points

An FPGA Based Facial Component Extractor using Wavelet-Based...

引用

International conference on Information and Communication Technologies: From Theory to applications, ICTTA

作者： I. Hosseini M. Rahmaty M.S. Sadri N. Shams R.A. Fattahi ECE Department I.U.T. Iran

Facial component extraction is an active area of research and numerous methods are available for it. Some of these methods are slightly inaccurate because of vast dynamic characteristics of human face and its shape variations. However, these methods are widely used in real-time applications due to their low computational cost. In this paper we propose a new method that uses wavelet decomposition to extract salient points of the given image. Statistical analysis of the obtained salient points can exactly locate facial components. The proposed method tries to achieve computational efficiency and accuracy simultaneously. Simulation results present a detection rate of 95.6% for facial components. A complete FPGA implementation of the system is also proposed. It is shown that the logic resource usage for entire system is so low that the entire system can fit into a 300K gates Sparatan-iiE FPGA. The designed hardware is capable of extracting facial components for up to 5 frames per second

关键词： Field programmable gate arrays Principal component analysis Face detection Discrete wavelet transforms Computational efficiency Statistical analysis Hardware Face recognition Pattern recognition signal processing algorithms

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An efficient VLSI architecture for fingerprint recognition using O2D-DWT architecture and modified CORDIC-FFT

An efficient VLSI architecture for fingerprint recognition u...

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IEEE International conference on signal processing, Informatics, Communication and Energy Systems (SPICES)

作者： Satish S Bhairannawar Sayantam Sarkar K B Raja K R Venugopal Department of Electronics and Communication Engineering Dayananda Sagar College of Engineering Bangalore India Department of Electronics and Communication Engineering UVCE Banglore India Visvesvaraya College of Engineering Bangalore University Bangalore India

The real time biometric systems are used to authenticate persons for various applications. In this paper, we propose an efficient VLSI architecture for fingerprint recognition using O2D-DWT architecture and modified CORDIC-FFT. The O2D-DWT architecture is designed using only adders and shifters for high speed operation and is applied on fingerprint image to generate four sub-bands. The optimized Fast Fourier Transform (OFFT) architecture is designed by computing different twiddle factor angles using modified CORDIC processor and is applied on LL sub-band coefficients to generate final fingerprint features. The test fingerprint features are compared with features of fingerprint images in database using Euclidean Distance. It is observed that the performance of proposed architecture is better compared to existing architectures.

关键词： Computer architecture Fingerprint recognition Discrete wavelet transforms Finite impulse response filters Databases Very large scale integration

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A novel configuration context cache structure of reconfigurable systems

A novel configuration context cache structure of reconfigura...

引用

International conference on ASIC

作者： Yu Gong Bo Liu Chen Mei Ruihe Wang Southeast University Nanjing Jiangsu CN National ASIC System Engineering Technology Research Center Southeast University Nanjing China Integrated Circuits Technology Research Institute Southeast University Wuxi China

The coarse-grained reconfigurable architecture (CGRA) is proven to be energy efficient in many domains, such as multi-media and signal processing. However, the continuous updating of configuration context limits the performance of reconfigurable systems severely. Configuration context cache is usually introduced in CGRAs to solve this problem. This paper proposes several methods to improve the performance of the configuration context cache. Firstly, the configuration context is classified into three levels. Secondly, a three-level hierarchical cache structure, based on the Pre-fetch and Hybrid-Priority strategies, is adopted to reduce the cache accessing time. Finally, a novel Two-Side-View design, including the physical view and the logic view, is implemented to reduce the cache size. The experimental results show that the proposed structure is 2.23 times and 2.06 times better than the up-to-date centralized and distributed structures. The proposed cache structure has been integrated in a reconfigurable computing processor called REMUS-ii. When processing multi-media applications, REMUS-ii can realize 1080p HiP@30fps decoding at frequency of 200MHz.

关键词： Context Indexes Table lookup Decoding Kernel Continuous wavelet transforms signal processing algorithms

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Overview of Artifacts Detection and Elimination Methods for BCI Using EEG

Overview of Artifacts Detection and Elimination Methods for ...

引用

International conference on image, Vision and Computing (ICIVC)

作者： Clifford Maswanganyi Chunling Tu Pius Owolawi Shengzhi Du Department of Computer Systems Engineering Tshwane University of Technology Pretoria Gauteng RSA Department of Electrical Engineering Tshwane University of Technology Pretoria Gauteng RSA

ISBN: (纸本)9781538649923

Electroencephalogram (EEG) is the most commonly utilized method of mapping brain wave patterns from the scalp. It is a process of extracting electrical activities of the brain in the form of EEG signals using non-invasive methods. In EEG based techniques, a challenge exists in the form of signal contamination caused by internal and external factors in the recorded EEG signals., such as the artifacts, interferences among multiple EEG sources, and noise. signal pre-processing is the most significant phase for EEG based applications, for instance, the brain computer interface (BCI) systems. This is because the quality of data acquired from the brain through EEG measurements is dependent on the effectiveness of artifact detection and removal methods. During EEG recording there are several factors that influence the quality of data. One of the factors of signal contamination occurs as the result of physiological and non-physiological artifacts. To detect and remove such contaminations from EEG signals, there are several efficient artifact detection and elimination techniques based on certain pre-processing methods. The main objective of this study is to demonstrate various types of artifacts in EEG recordings as results of external and internal factors. Furthermore, the study will also present an extensive review of existing artifacts identification and elimination methods applied on EEG signals.

关键词： Electroencephalography Physiology Discrete wavelet transforms Electrodes Contamination Electric potential

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Projected Improved Fista And Application To image Deblurring

Projected Improved Fista And Application To Image Deblurring

引用

IEEE International conference on image processing

作者： Praveen Kumar Pokala Chandra Sekhar Seelamantula Department of Electrical Engineering Indian Institute of Science Bangalore India

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

The analysis-sparse model has been shown to be efficient as compared to the synthesis-sparse model when the sparsifying transform is redundant particularly for image restoration applications. We pose the image deblurring problem as an optimization problem based on the analysis-sparse model considering the sparsifying basis to be a tight frame, more specifically, the shift-invariant discrete wavelet transform (SIDWT). We propose two algorithms, namely, projected improved fast iterative soft-thresholding algorithm (piFISTA) and projected improved fast iterative soft-thresholding algorithm beyond Nesterov's momentum (piFISTA-BN). The proposed algorithms are the analysis counterparts of the improved fast iterative soft-thresholding algorithm (iFISTA) and improved fast iterative soft-thresholding algorithm beyond Nesterov's momentum (iFISTA-BN), respectively, both of which consider the synthesis-sparse model. We demonstrate that piFISTA and piFISTA-BN significantly outperform FISTA, pFISTA, iFISTA, and iFISTA-BN considering standard objective metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM). Further, we demonstrate empirically that the proposed algorithms converge faster than the state-of-the-art techniques.

关键词： Analytical models image restoration Transforms image reconstruction Iterative algorithms Convergence Standards

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Robot control with graph based edge measure in real time image frames

Robot control with graph based edge measure in real time ima...

引用

IEEE signal processing and Communications applications (SIU)

作者： Emrah Dönmez Adnan Fatih Kocamaz Mahmut Di̇ri̇k Inonu Universitesi Malatya Malatya TR

Robotic path plan extraction is one of the major study area focused with image processing techniques except from estimation based methods, in real time robotic systems. In this study; I. First phase: It is aimed to track a mobile robot and target point by detecting start and target points in real time image frames, continually. ii. Second phase: It is aimed to exhibit a novel sensor free kinematic model to determine power ratio transferred to the left and right wheels of the robot with decision tree method by utilizing a graph based method on detected object points. Right and left wheel position, robot center and labeled target positions are acquired with thresholding method by using labels placed on robot. A graph has been created on an image by admitting all detected areas as nodes. The processes of orientating and delivering mobile robot to the target position has been modelled according to distance values of edges between wheels and target.

关键词： Robot sensing systems Mobile robots Real-time systems Wheels Path planning Cameras

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Robust Point Tracking Based On image Matching And Machine Learning On Video images Taken from Fast Moving Camera

Robust Point Tracking Based On Image Matching And Machine Le...

引用

IEEE signal processing and Communications applications (SIU)

作者： Ali Güven İmam Şamil Yetik Elektrik ve Elektronik Mühendisliği Bölümü TOBB Ekonomi ve Teknoloji Üniversitesi Ankara

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Recently, providing real-time navigation of unmanned aerial vehicles independent of global positioning systems has become of great importance. The state-of-the-art methods based on deep learning, which give good results in certain datasets, and the existing methods can not provide real-time and good solutions on images with dynamic and fast moving. Moreover, the methods, were developed so far, were focused on object-based tracking algorithms. In this paper, the tracking of the points belonging to the target pattern, found by image matching, was performed with the machine learning model we developed for 10 sequential video images. The features extracted for the machine learning model are: (i) the change between the points of the previous image and the image before that, (ii) the points of interest in the previous image, (iii) the changes found with the homography matrix between sequential images. It was experimentally shown that, point tracking can be achieved with the least error, on avarage about 23 pixels for a 2 mega-pixel resolution image, among the algorithms in the literature that can process more than 30 images per second in a CPU environment of 2 GHz or above.

关键词： Target tracking Machine learning algorithms image resolution Navigation image matching signal processing algorithms Feature extraction

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FPGA based real-time image segmentation for medical systems and data processing

FPGA based real-time image segmentation for medical systems ...

引用

IEEE NPSS conference on Real Time

作者： P. Dillinger J.F. Vogelbruch J. Leinen S. Suslov R. Patzak H. Winkler K. Schwan Research Center Jülich Central Institute for Electronics Julich Germany Array Electronics GmbH Egmating Germany

3D image segmentation is one of the most demanding tasks in image processing. The applications comprise industrial and scientific tasks. Due to the high data volume advanced algorithms cannot be processed on standard hardware in real time. We propose the perfectly parallelizable 3D grey-value structure code (3D-GSC) for image segmentation on a new FPGA custom machine. This 128-bit FPGA co-processing board features an up-to-date Virtex-ii Pro architecture, two large independent DDR-SDRAM channels, two fast independent ZBT-SRAM channels, and PCI-X bus and CameraLink interfaces. The hardware speeds up the segmentation algorithm and allows processing of a 512 3 image (16 bpv) in about 2 sec

关键词： Field programmable gate arrays Real time systems image segmentation Biomedical imaging Data processing Hardware image processing image edge detection Digital signal processing Medical services

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Estimation of Motor Imagination Based on Consumer-Grade EEG Device 3rd

Estimation of Motor Imagination Based on Consumer-Grade EEG ...

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3rd International conference on Machine Learning for Cyber Security, ML4CS 2020

作者： Luo, Zhenzhen Hu, Zhongyi Li, Zuoyong College of Computer and Artificial Intelligence Wenzhou University Wenzhou China Intelligent Information Systems Institute Wenzhou University Wenzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

ISBN: (纸本)9783030624590

Nowadays, classifying electroencephalogram (EEG) signals based motor imagery tasks is extensively used to control brain-computer interface applications, as a communication bridge between humans and computers. In this paper, we propose signal-to-image transformation and feature extraction methods for the classification of motor imagery. Specifically, a continuous wavelet transform is applied to decompose EEG signals into five rhythms and generate time-frequency images. Then, a gray-level co-occurrence matrix is used to extract global texture features on time-frequency images. Finally, the SVM classification model is optimized by using a grid search algorithm to select optimal parameter pairs. To confirm the validity of the proposed methods, we experimented on self-collected data, which is obtained using a consumer-grade EEG device. The experimental result showed that this proposed method can achieve an acceptable classification accuracy of 90% for a two-class problem (left/right-hand motor imagery). © 2020, Springer Nature Switzerland AG.

关键词： Electroencephalography

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