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Fuzzy Inference System Based Robust Digital image Watermarking Technique Using Discrete Cosine Transform

Procedia Computer Science 2015年 46卷 1618-1625页

作者： B. Jagadeesh P. Rajesh Kumar P. Chenna Reddy E.C.E.Department G.V.P.College of Engineering Visakhapatnam A.P. India E.C.E.Department Andhra University Visakhapatnam A.P. India C.S.E.Department JNTUA College of Engineering Pulivendula Kadapa A.P. India

Digital Watermarking has evolved as one of the latest technologies for digital media copyright protection. Watermarking of images can be done in many ways and one of the proposed algorithms for image watermarking is by utilizing Fuzzy Logic. It is similar to the concept of a Fuzzy set, each element can be defined by an ordered pair, in which one is the value and other is the membership function value. Fuzzy logic systems can explain inaccurate information and explain their decisions. Fuzzy inference system is the simplest way of performing Fuzzy Logic. In the proposed method, three Fuzzy inference models are used to generate the weighing factor for embedding the watermark and input to the Fuzzy Inference System is taken from the Human visual System model. The Performance measures used in the Process are Peak Signal to Noise Ratio, Normalized Cross Correlation. The Proposed algorithm is immune to various image processing attacks.

关键词： Digital image Watermarking Discrete Cosine Transform Human visual System Fuzzy Inference System Normalized Cross Correlation Peak Signal to Noise Ratio.

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A case study of OpenCL-based parallel programming for low-power remote sensing applications

A case study of OpenCL-based parallel programming for low-po...

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International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)

作者： A. Castro Angulo R. Carrasco Alvarez J. Ortegón Aguilar J. vázquez Castillo O. Palma Marrufo A. Castillo Atoche Department of Mechatronics Universidad Autónoma de Yucatán Mérida Yuc. México Department of Electronics Universidad de Guadalajara Guadalajara Jal. México Science Division and Engineering Universidad de Quintana Roo Chetumal Quintana Roo México

With the advent of high-performance embedded computing (HPEC) systems, many digital processing algorithms are now implemented by special-purpose massively parallel processors. In this paper, a low-power ARM/GPU co-design architecture is addressed using OpenCL-based parallel programming for implementing complex reconstructive signal processing operations. Such operations are accelerated using data-parallel functions on the GPU and ARM processor, in a HW/SW co-design scheme via OpenCL API calls. Experimental results shows the achieved computational performance and the effectiveness of the OpenCL standard comparing the framework against traditional parallel embedded versions.

关键词： Graphics processing units image reconstruction Computer architecture Remote sensing Signal processing algorithms Real-time systems

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A MULTILEvEL MEMORY-ASSISTED LOSSLESS COMPRESSION ALGORITHM FOR MEDICAL imageS

A MULTILEVEL MEMORY-ASSISTED LOSSLESS COMPRESSION ALGORITHM ...

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European Signal processing Conference

作者： Zhinoos Razavi Hesabi Borhan Kazimipour Mohamed Deriche Antonio Navarro School of Computer Science and IT RMIT University Department of Electrical Engineering King Fahd University of Petroleum & Minerals Institute of Telecommunication- University of Aveiro

ISBN: (纸本)9781479988518

As medical imaging facilities move towards film-less imaging technology, robust image compression systems are starting to play a key role. Conventional storage and transmission of large-scale raw medical image datasets can be very expensive and time-consuming. Recently, we proposed a memory-assisted lossless image compression algorithm based on Principal Component Analysis (PCA). In this paper, we further improve the performance of the algorithm in two different directions: Firstly, we replace PCA with NMF (Non Negative Matrix Factorization). NMF has several advantages in representing images with an image-like basis, results in sparse factors, and provides better user control over iterations. Secondly, we expand the single-level model with a new multi-level decomposition/projection framework to further reduce entropy of residual images. Our experimental results on X-ray images confirm that both modifications provide significant improvements over the single level PCA based algorithm as well as existing non-memory based techniques.

关键词： Lossless Compression Medical Imaging Non-negative Matrix Factorization Unsupervised Learning Medical imaging lossless compression Unsupervised learning images principal components analysis Passive Cutaneous Anaphylaxis algorithms NMF protocol image compression algorithm

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Cellular neural networks for welding arc thermograms segmentation

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INFRARED PHYSICS & TECHNOLOGY 2014年 66卷 18-28页

作者： Jamrozik, Wojciech Silesian Tech Univ Inst Fundamentals Machinery Design PL-44100 Gliwice Poland

Machine vision systems are used in many areas for monitoring of technological processes. Among this processes welding takes important place, where often infrared cameras are used. Besides reliable hardware, successful application of vision systems requires suitable software based on proper algorithms. One of most important group of image processing algorithms is connected to image segmentation. Obtainment of exact boundary of an object that changes shape in time, such as the welding arc, represented on a thermogram is not a trivial task. In the paper a segmentation method using supervised approach based on a cellular neural networks is presented. Simulated annealing and genetic algorithm were used for training of the network (template optimization). Comparison of proposed method to a well elaborated segmentation method based on region growing approach was made. Obtained results prove that the cellular neural network can be a valuable tool for infrared welding pool images segmentation. (C) 2014 Elsevier B.v. All rights reserved.

关键词： Thermovision image segmentation Cellular neural networks Welding

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video processing for v2v communications: A case study with traffic lights and plate recognition

Video processing for V2V communications: A case study with t...

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IEEE International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI)

作者： G. Russo E. Baccaglini L. Boulard D. Brevi R. Scopigno Istituto Superiore Mario Boella Turin Italy

In recent years there have been changes in the way cars are designed. Car manufactures put a lot of effort on safety and systems that provide information to the driver with the long-term objective of achieve a complete self-driving car. Nowadays the most effective approach relies on data fusion of information, coming from a plethora of different sensors like RADARs and videocameras. While some of these sensors are already available on commercial cars, others will be introduced step-by-step. Therefore, data fusion algorithms should address the possibility to manage duplicated information and using this redundant information to validate the received data. In this work we describe two near-real time algorithms which exploit the video stream acquired by on-board camera. One allows for the identification of traffic light status and the second one addresses vehicles tracking and plate recognition.

关键词： Cameras Streaming media Color Automobiles image edge detection Real-time systems

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A decision based switching median filter for restoration of images corrupted by high density impulse noise

A decision based switching median filter for restoration of ...

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International Conference on Robotics, Automation, Control and Embedded systems (RACE)

作者： J Jezebel Priestley v Nandhini v Elamaran Department of Electronics and Communication Engg. SASTRA University Thanjavur Tamilnadu India Dept. of ECE SASTRA University Thanjavur India

Digital images are corrupted by impulse noise mainly due to sensor faults of image acquisition devices and adverse channel environment which in turn degrades the image quality. A decision based switching median filter (DBSMF) to restore images corrupted with high density impulse noise is proposed in this paper. The global use of standard median filters for impulse noise removal from corrupted images provide good results but the filtering operation may affect fine pixels in addition to noisy pixels which leaves a blurred effect on the filtered image. In order to address this issue the proposed algorithm makes use of an efficient detection scheme to identify the noise pixels and noise free pixels. The detection algorithm clusters the pixels in the corrupted image so as to fall under three categories which states whether the pixels are corrupted or uncorrupted. The proposed switching median filter processes only on those pixels that are classified as corrupted and replaces the processing pixel by the median value. Under high noise densities the filtering window consists of more number of corrupted pixels. For such cases, the proposed algorithm restricts certain conditions on the expansion of the filtering window size to effectively choose the median value. The performance of this decision based algorithm is tested against four noise models for different levels of noise densities and is evaluated in terms of performance metrics which include Peak Signal to Noise ratio (PSNR) and image Enhancement Factor (IEF). It gives better results for images that are extremely corrupted up to 90% noise density and outperforms classic filters in terms of handling image corruption.

关键词： Noise Standards Filtering algorithms image restoration Switches Information filtering

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Weighted Fast Dynamic Time Warping based multi-view human activity recognition using a RGB-D sensor

Weighted Fast Dynamic Time Warping based multi-view human ac...

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National Conference on Computer vision, Pattern Recognition, image processing and Graphics (NCvPRIPG)

作者： Ishan Agarwal Alok Kumar Singh Kushwaha Rajeev Srivastava Department of Computer Sc. and Engineering Jaypee Institute of Information Technology Noida UP India Indian Institute of Technology Delhi New Delhi Delhi IN Department of Computer Sc. and Engineering Indian Institute of Technology (BHU) Varanasi UP India

In this paper, a real time multi-view human activity recognition model using a RGB-D (Red Green Blue-Depth) sensor is proposed. The method receives as input RGB-D data streams in real time from a Kinect for Windows v2 sensor. Initially, a skeleton-tracking algorithm is applied which gives 3D joint information of 25 unique joints. The presented approach uses a weighted version of the Fast Dynamic Time Warping that weighs the importance of each skeleton joint towards the Dynamic Time Warping (DTW) similarity cost. To recognize multi-view human activities, the weighted Dynamic Time Warping warps a time sequence of joint positions to reference time sequences and produces a similarity value. Experimental results demonstrate that the proposed method is robust, flexible and efficient with respect to multiple views activity recognition, scale and phase variations activities at different realistic scenes.

关键词： Time series analysis Heuristic algorithms Real-time systems Cameras Robustness Computers image recognition

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Question Answering Search engine Short Review and Road-Map to Future QA Search Engine (QA Search Engine)

Question Answering Search engine Short Review and Road-Map t...

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International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO)

作者： Kadam, Aniket D. Joshi, Shashank D. Shinde, Sachin v. Medhane, Sampat P. BVDUCOEP Dept Info Tech Pune Maharashtra India BVDUCOEP Dept Comp Pune Maharashtra India

ISBN: (纸本)9781479976782

Search Engines are based on bivalent logic and probability theory with lack of real world conceptual knowledge, higher precision and reasoning ability. Ranked links or snippets are less effective with big data Analysis and web of information. State of art Search Engine Google endeavor to retrieve good quality document, Msn Engine i.e. Bing endeavor decision boosting. Advanced algorithms account in user intents semantics and societal patterns on Web with recommendation system as product (recommendation Engines). Search engines have advanced from Text based to voice based (Dialogue based) to image based (multimedia as input Question to search). Major advancement analysis of search Engine Enhancement, search Engines have advanced from traditional data base retrieval machine to web based machines from horizontal engines to vertical Engines (***, ***) with dedicated crawler Technology at heart. Information of web is structured instructed and posses a huge challenge to big data analytics. All though Retrieval results are optimistic yet they Lack in ability to interpret user Question. Precise answering from relevant Informatics is Search Engine Enhancement. Time complexity and memory are Algorithmic and Machine design parameters that support optimized search result. Question Answering Search Engine (QA Engine) is machine with deductive reasoning capability ability to amalgamate information from various knowledge datasets. QA Engine is front linear area in advanced information retrieval techniques, state of art technology to future of search engines, expert systems. QA search engines have advanced from Shallow Technique (keyword technique) to template based Structured Knowledge processing Engines, profile based engines, and context based machines to cross language machines to Multimedia QA Engines. Community based QA Yahoo answer, stack overflow to specialized search engines like ***, ***, true knowledge. Question Answering system have been embedded

关键词： Question-AnsweringSearchEngine Answer Engine Future- Engine Search Engine Natural Language Engine

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Research on corn region extraction of remote sensing image based on fusion of PSO parameters optimization and adaboost_SvM

International Journal of Signal Processing, Image Processing...

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International Journal of Signal processing, image processing and Pattern Recognition 2015年第8期8卷 361-372页

作者： Li, Dawei Yang, Fengbao Wei, Hong School of Information and Communication Engineering North University of China TaiyuanShanxi China School of Systems Engineering University of Reading United Kingdom

High resolution remote sensing image contains abundant information, and the amount of data expands rapidly , it is challenge and advantage to the corn information extraction. For high resolution images, the spectral characteristics are the most important characteristics, but remote sensing classification only according to the spectral ignores the texture information as positive factors. This paper introduced texture features, establishes joint spectrum - texture feature set. Support v ector Machine (SvM) classification method relies on the selected parameters, this paper used Particle swarm optimization (PSO) to optimize penalty and kernel function parameters. Under the condition of a large number of high resolution remote sensing data, the increase of samples will influence the algorithm efficiency greatly . This paper improved SvM, namely Adaboost_SvM, extracted corn planting information. The method can be used as support for corn area prediction and yield estimation. The experimental results show that the proposed method improves extraction accuracy, improves efficiency compared with classic algorithms significantly. © 2015 SERSC.

关键词： Remote sensing

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Multi-objective differential evolution algorithm for underwater image restoration

Multi-objective differential evolution algorithm for underwa...

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Congress on Evolutionary Computation

作者： Camilo Sánchez-Ferreira Helon v. H. Ayala Leandro dos S. Coelho Daniel Muñoz Mylène C. Q. Farias Carlos H. Llanos Department of Mechanical Engineering University of Brasilia DF Brazil Automation and Systems Laboratory LAS/PPGEPS Pontifical Catholic University of Parana Curitiba Parana Brazil Faculty of Engineering University of Brasilia - Gama DF Brazil Electrical Engineering Department University of Brasilia Brasilia Brazil

Underwater image processing area has been considered an important topic within the last decades with important achievements. This kind of images are essentially characterized by their poor visibility because light is exponentially attenuated as it travels in the water and the scenes result poorly contrasted and hazy. On the other hand, image restoration takes into account the influence of the environment on the image in order to achieve an image with an improved quality. This technique consist of inverting the physical model of image formation. That model contains parameters which represent variables such as coefficients of absorption, scattering, among others. In this case, the quality of the restored image depends on the correct estimation of these parameters. In this work, an approach based on evolutionary optimization algorithms is proposed, for restoring underwater images by estimating the model parameters, and using two metrics for quality assessment. The degradation in the images has been simulated by using an image formation model. Results show that image restoration based on a Multi-Objective Differential Evolution (MODE) algorithm achieves images with good contrast and sharpness, being even better than the original image.

关键词： image restoration Mathematical model Optimization Measurement Degradation Scattering Cameras

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