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image manipulation detection using contourlet transform

Image manipulation detection using contourlet transform

引用

IEEE signal processing and Communications applications (SIU)

作者： Levent Özparlak İsmail Avcıbaş Elektrik ve Elektronik Mühendisliği Bölümü BAŞKENT Üniversitesi Turkey Elektrik ve Elektronik Mühendisliği Bölümü Turgut Özal Üniversitesi Turkey

In this paper, we use contourlet transform for digital image manipulation detection. We extract contourlet and wavelet features and test these obtained features on a controlled image data set. Results show that contou... 详细信息

关键词： wavelet transforms wavelet domain wavelet analysis Feature extraction Forgery

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Maritime Surveillance with Synthetic Aperture Radar (SAR) and Automatic Identification System (AIS) Onboard a Microsatellite Constellation

Maritime Surveillance with Synthetic Aperture Radar (SAR) an...

引用

conference on image and signal processing for Remote Sensing XviiI

作者： Peterson, E. H. Zee, R. E. Fotopoulos, G. Univ Toronto Inst Aerosp Studies Space Flight Lab 4925 Dufferin St Toronto ON Canada Univ Texas Dallas Dept Geosci Richardson TX 75083 USA

ISBN: (纸本)9780819492777

New developments in small spacecraft capabilities will soon enable formation-flying constellations of small satellites, performing cooperative distributed remote sensing at a fraction of the cost of traditional large spacecraft missions. As part of ongoing research into applications of formation-flight technology, recent work has developed a mission concept based on combining synthetic aperture radar (SAR) with automatic identification system (AIS) data. Two or more microsatellites would trail a large SAR transmitter in orbit, each carrying a SAR receiver antenna and one carrying an AIS antenna. Spaceborne AIS can receive and decode AIS data from a large area, but accurate decoding is limited in high traffic areas, and the technology relies on voluntary vessel compliance. Furthermore, vessel detection amidst speckle in SAR imagery can be challenging. In this constellation, AIS broadcasts of position and velocity are received and decoded, and used in combination with SAR observations to form a more complete picture of maritime traffic and identify potentially non-cooperative vessels. Due to the limited transmit power and ground station downlink time of the microsatellite platform, data will be processed onboard the spacecraft. Herein we present the onboard data processing portion of the mission concept, including methods for automated SAR image registration, vessel detection, and fusion with AIS data. Georeferencing in combination with a spatial frequency domain method is used for image registration. wavelet-based speckle reduction facilitates vessel detection using a standard CFAR algorithm, while leaving sufficient detail for registration of the filtered and compressed imagery. Moving targets appear displaced from their actual position in SAR imagery, depending on their velocity and the image acquisition geometry;multiple SAR images acquired from different locations are used to determine the actual positions of these targets. Finally, a probabilistic inference mode

关键词： SAR AIS speckle filtering data fusion image registration moving object detection

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Real-time portable system for fabric defect detection using an ARM processor

Real-time portable system for fabric defect detection using ...

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conference on Optics, Photonics and Digital Technologies for Multimedia applications II

作者： Fernandez-Gallego, J. A. Yanez-Puentes, J. P. Ortiz-Jaramillo, B. Alvarez, J. Orjuela-Vargas, S. A. Philips, W. Univ Antonio Narino Ibague Colombia

ISBN: (纸本)9780819491282

Modern textile industry seeks to produce textiles as little defective as possible since the presence of defects can decrease the final price of products from 45% to 65%. Automated visual inspection (AVI) systems, based on image analysis, have become an important alternative for replacing traditional inspections methods that involve human tasks. An AVI system gives the advantage of repeatability when implemented within defined constrains, offering more objective and reliable results for particular tasks than human inspection. Costs of automated inspection systems development can be reduced using modular solutions with embedded systems, in which an important advantage is the low energy consumption. Among the possibilities for developing embedded systems, the ARM processor has been explored for acquisition, monitoring and simple signal processing tasks. In a recent approach we have explored the use of the ARM processor for defects detection by implementing the wavelet transform. However, the computation speed of the preprocessing was not yet sufficient for real time applications. In this approach we significantly improve the preprocessing speed of the algorithm, by optimizing matrix operations, such that it is adequate for a real time application. The system was tested for defect detection using different defect types. The paper is focused in giving a detailed description of the basis of the algorithm implementation, such that other algorithms may use of the ARM operations for fast implementations.

关键词： ARMv4 defect detection embedded system floating point texture analysis wavelet transform

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A novel multimodal compression scheme based on a spiral insertion function in the wavelet domain

A novel multimodal compression scheme based on a spiral inse...

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International Symposium on signal processing and Its applications (ISSPA)

作者： Larbi Boubchir Tahar Brahimi Régis Fournier Amine Naït-ali Laboratoire Image Signaux et Systèmes Intelligents (EA.3659) Université Paris-Est Créteil Créteil France Laboratoire Image Signaux et Systèmes Intelligents (EA.3659) Université Paris-Est Créteil 61 avenue du Général de Gaulle 94010 France

This paper presents a new variant of multimodal compression scheme based on a spiral insertion function in the wavelet domain. The main idea consists of inserting a decomposed signal into a decomposed image prior to a joint SPIHT compression process. The insertion phase is achieved in the detail sub-bands of the wavelet decomposition. The evaluation is assessed on both natural and medical images using objective and subjective comparison criteria. The experimental results showed the effectiveness of the proposed approach to obtain significant gains in terms of reconstructed signal (PRD %) and image (PSNR).

关键词： image coding wavelet transforms image reconstruction Biomedical imaging PSNR Spirals

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Experimental investigation for practical sparsity number for image reconstruction based on SL0 algorithm in discrete frequency domain

Experimental investigation for practical sparsity number for...

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26th IEEE International conference on Advanced Information Networking and applications Workshops, WAINA 2012

作者： Tancharoen, Datchakorn Ha, Pham Hong Thakulsukanant, Kornkamol Patanavijit, Vorapoj Faculty of Engineering and Technology Panyapiwat Institute of Management Bangkok Thailand Department of Computer and Network Engineering Faculty of Engineering Assumption University Bangkok Thailand Department of Telecommunications Science Faculty of Science and Technology Assumption University Bangkok Thailand

ISBN: (纸本)9780769546520

Compressive Sensing (CS) is known as a new sampling theory that use small number of basis elements for constructing of signals (or images) and these basis elements (so called sparsity number) are very important parameters that approximate how sparsify the image is. Due to several characteristics of each image groups, the sparsity number could be varied and there is unfortunately very little research for this issue. This paper presents two main contributions: First, this paper proposes a practical sparsity number estimation technique using for an image reconstruction for SL0 algorithm based on Discrete Cosine Transform domain (DCT). Second the practical sparsity number of difference image groups is the experiment based on over 2000 images. The DCT is exclusively applied for a sparse representation of images because it is proven as a useful instrument for image analysis and processing. In general, images can be represented by a linear superposition of small number of wavelet elements selected from a suitable filter. The proposed models process the image with Smoothed norm algorithm. This algorithm stated that if signal or image is sufficiently sparse, we can reconstruct it from small amount of none zero basis components. The experiment is comprehensively tested under 2000 sampling images that are categorized in 18 groups by their characteristics. Moreover this sparsity number is practically used for any CS algorithm based on DCT domain. © 2012 IEEE.

关键词： Discrete cosine transforms

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image super-resolution reconstruction based on self-similarity and neural networks

Image super-resolution reconstruction based on self-similari...

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International Symposium on signal processing and Its applications (ISSPA)

作者： Yan Xu Xue M. Li Tian Gao Ching Y. Suen Beijing Key Laboratory of Network System and Network Culture University of Posts and Telecommunications 100876 China Shandong Jianzhu University Jinan Shandong CN Concordia University Montreal QC CA

ISBN: (纸本)9781467303811

A novel super-resolution approach is presented. An image pyramid has been built based on the framework of wavelet transform, and the detailed coefficients are explored for training the neural networks. The initial high resolution image is estimated by the trained networks and the inverse wavelet transform, and then is constrained with prior knowledge of the error function by iteration. For a factor of 2n, repeat this process and update the networks. The experimental results show that our method reconstructs the more reliable image without obvious visual artifacts.

关键词： image resolution image reconstruction wavelet transforms Feature extraction Strontium Biological neural networks

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Single image superresolution using sparsity and dictionary learning in wavelet domain

Single image superresolution using sparsity and dictionary l...

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IEEE signal processing and Communications applications (SIU)

作者： Mahmoud Nazzal Hüseyin Özkaramanli Elektrik ve Elektronik Mühendisliği Bölümü Doğu Akdeniz Üniversitesi Turkey

Recently sparse representation is proven to be very successful for image processing applications. This paper proposes a superresolution approach that utilizes the decorrelating and sparsifying property of discrete wavelet transform, with the signal-fitting capability of sparse representation over learned dictionaries. Two dictionaries are learned (using K-SVD algorithm) for each wavelet subband: one for the low resolution one for the high resolution images. In the training set, noisy variants of the high a resolution image obtained by interpolating its low resolution counter-part, are intentionally included. A patch based approach is employed and different patch sizes are studied. The sparse representation coefficients for the respective low and high resolution images are assumed to be the same. Experiments are conducted using the Kodak24-image set. The proposed algorithm is proven to be competitive with the leading super-resolution techniques both visually and quantitatively. With a patch size of 11×11 the proposed method is 0.89 dB better than the method proposed by Elad ***. and 2.19 dB better than bicubic interpolation.

关键词： image resolution signal resolution PSNR Dictionaries Matching pursuit algorithms Interpolation Standards

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Segmentation of color texture images with artificial bee colony algorithm and wavelet transform

Segmentation of color texture images with artificial bee col...

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IEEE signal processing and Communications applications (SIU)

作者： Kazım Hanbay M. Fatih Talu Ali Karci Bilgisayar Programcılıgı Bölümü Bingöl Üniversitesi Turkey Bilgisayar Mühendisliği Bölümü İnönü Üniversitesi Turkey

Segmentation is an important application in the analysis of gray level and color images numerically. The task of image segmentation is to separate a given image into different regions having homogenous properties. Although the most basic segmentation is thresholding, this method has been insufficient in time. In this paper, the segmentation of color texture images is carried out with the approach of wavelet transform and artificial bee colony algorithm. Through this approach, the segmentation of typical natural scenes, including no uniform statistical color and texture characteristics is achieved. The superior and weak points of the developed method are evaluated.

关键词： Argon image segmentation image color analysis wavelet transforms Reactive power Optimization

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Performance analysis of fast DOA estimation using wavelet denoising over Rayleigh fading channel on MIMO system

Performance analysis of fast DOA estimation using wavelet de...

引用

2nd International conference on Computer Science, Engineering and applications, ICCSEA 2012

作者： Meenakshi, A.V. Kayalvizhi, R. Asha, S. ECE Periyar Maniammai University Thanjavur India

ISBN: (纸本)9783642301100

This paper presents a tool for the analysis, and simulation of direction-of-arrival estimation in wireless mobile communication systems over the Rayleigh fading channel. It reviews three subspace based methods of Direction of arrival estimation algorithms. The standard Multiple signal Classification (MUSIC) can be obtained from the subspace based methods. In improved MUSIC procedure called Cyclic MUSIC, it can automatically classify the signals as desired and undesired based on the known spectral correlation property and estimate only the desired signal's DOA. The next method is an extension of the Cyclic MUSIC algorithm called Extended Cyclic MUSIC by using an extended array data vector. By exploiting cyclostationarity, the signal's DOA estimation can be significantly improved. In this paper, the DOA estimation algorithm using the de-noising pre-processing based on time-frequency conversion analysis is proposed, and the performances are analyzed. This is focused on the improvement of DOA estimation at a lower SNR and interference environment. This paper provides a fairly complete image of the performance and statistical efficiency of each of above three methods with QPSK signal model for coherent system. © 2012 Springer-Verlag GmbH.

关键词： Multiple signal classification

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image Compression Using wavelet Based Compressed Sensing and Vector Quantization

Image Compression Using Wavelet Based Compressed Sensing and...

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2012 IEEE 11th International conference on signal processing(ICSP 2012)

作者： Mohit Kalra D.Ghosh Dept.of Electronics ＆ Computer Engg. Indian Institute of Technology RoorkeeIndia

Sparse signal representations and compressed sensing have found use in a large number of applications including image compression. Compressed sensing exploits the sparsity of naturally occurring images to reduce the volume of the data by using less number of measurements. Inspired by this, we propose a new framework for image compression that combines compressed sensing theory with wavelet and vector quantization. wavelet transform is used to sparsify the input image while measurement vectors generated from the sparse vectors are transmitted using vector quantization. Simulation experiments are carried out to analyze the effects of various parameters on the image reconstruction quality. Results obtained have been found to be quite promising.

关键词： image compression sparse representation compressed sensing wavelet transform vector quantization

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