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image classification of Kiziltepe cropland by using Gabor wavelet Transform

Image classification of Kiziltepe cropland by using Gabor Wa...

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2012 20th signal processing and Communications applications conference, SIU 2012

作者： Acar, Emrullah Özerdem, Mehmet Siraç Elektrik Ve Elektronik Mühendisliǧi Bölümü Batman Üniversitesi Turkey Elektrik Ve Elektronik Mühendisliǧi Bölümü Dicle Üniversitesi Turkey

ISBN: (纸本)9781467300568

In this research, images of Kiziltepe (Mardin) agricultural land are classified based on development in different period of crops (1-sowing term, 2-little ripe term, 3-ripe term and 4-harvest term). The digital crop images are derived from TARIT (Agricultural Crop Forecast and Following Project) stations. The texture feature vectors are obtained from images of different crop periods with using Gabor wavelet Transform (GWT) which is very popular method for image texture extraction. GWT based feature vectors are applied to different type of classifiers as inputs and the performance results of each system are compared. For classification the crop images, Multilayer Perceptron (MLP) neural network and k-Nearest Neighbor (k- NN) methods are used. Finally, the best average performance is observed as 98.01 % in 24-35-4 network structure of MLP neural network classifier. © 2012 IEEE.

关键词： Crops

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2012 20th signal processing and Communications applications conference, SIU 2012, Proceedings

2012 20th Signal Processing and Communications Applications ...

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2012 20th signal processing and Communications applications conference, SIU 2012

ISBN: (纸本)9781467300568

The proceedings contain 428 papers. The topics discussed include: HCI remixed: at the intersection of graphics, vision, cool hardware, and mixed reality;future of mobile devices and experiences;Spatial hearing issues in sound reproduction;intelligent user interfaces;Introduction to programming with Kinect: understanding hand/arm/head motion and spoken commands;MIMO communication theory, algorithms, and prototyping;towards 5G wireless cellular networks: views on emerging concepts and technologies;large-scale human behavior and smartphone data;wavelet transform approach to energy based spectrum sensing;performance analysis of analog-to-digital converters;test and calibration of sonobuoys in the air environment;efficient image mosaicing for optical underwater mapping;and performances of Zigzag coding with high-level modulations on WiMAX system.

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Visual enhancement of underwater images using empirical mode decomposition and wavelet denoising

Visual enhancement of underwater images using empirical mode...

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2012 20th signal processing and Communications applications conference, SIU 2012

作者： Çelebi, Aysun Taşyapi Ertürk, Sarp Elektronik Ve Haberleşme Mühendisliǧi Bölümü Kocaeli Üniversitesi Turkey

ISBN: (纸本)9781467300568

In recent years, most underwater vehicles are equipped with optical cameras to capture underwater images. But underwater images acquired using optic cameras have poor visual quality due to propagation of properties of light in water. So it is useful to apply image enhancement methods to increase visual quality of the images as well as enhance interpretability and visibility. In this paper, an Empirical Mode Decomposition (EMD) based underwater image enhancement algorithm is presented for this purpose. In the proposed approach, initially each color channel (R, G, B) of an underwater image is decomposed into Intrinsic Mode Functions (IMFs) using EMD. The first IMF of each component is applied to wavelet denoising. Because this IMF includes all local high spatial frequency components. Then the enhanced image is constructed by combining the IMFs of spectral channels with different weights in order to obtain an enhanced image with increased visual quality. The weight estimation process is carried out automatically using a genetic algorithm that computes the weights of IMFs so as to optimize the sum of the entropy and average gradient of the reconstructed image. © 2012 IEEE.

关键词： Intrinsic mode functions

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Change detection of MODIS time series using a wavelet transform

Change detection of MODIS time series using a wavelet transf...

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2012 International conference on Systems and Informatics, ICSAI 2012

作者： Piao, Yingchao Yan, Baoping Guo, Shan Guan, Yanning Li, Jianhui Cai, Danlu Computer Network Information Center Chinese Academy of Sciences Beijing 100190 China Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China Graduate University Chinese Academy of Sciences Beijing lO0049 China

ISBN: (纸本)9781467301992

This paper presents a rapid and easy-to-use approach for change detection of NDVI time series using wavelet transform. wavelet transform has a long history in signal and image processing field. However, the research on the large-scale remote sensing images by wavelet transform is rarely. The method in this paper is practical by using wavelet analysis on the large scale remote sensing time series. The time series based on Normalized Difference Vegetation Index (NDVI) are fundamental to analyze the dynamic nature of the vegetation. The trend of NDVI time series is extracted by using wavelet transform. For more accurate results, a seeded region growing algorithm is used for the detailed study on the certain areas. Results from the Tibetan Plateau show the wavelet transform in combination with the region growing procedure provides a efficient approach to estimate the areas where vegetation changed. © 2012 IEEE.

关键词： wavelet transforms

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A spatially adaptive denoising algorithm based on curvelet transform

A spatially adaptive denoising algorithm based on curvelet t...

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2012 2nd International conference on Computer Application and System Modeling, ICCASM 2012

作者： Feng, Peng Yang, Feng Wei, Biao Mi, Deling Key Laboratory of Opto-electronics Technology and System Ministry of Education Chongqing University Chongqing 400044 China

ISBN: (纸本)9789491216008

A new approach for image denoising based on the Curvelet transform is presented in this paper. The existing theory for Curvelet and Ridgelet suggests that these new approaches can outperform wavelet method in certain image processing including image denoising, edge detection and image enhancement. However in original digital Curvelet transform it uses a simple hard-thresholding rule for filtering the noisy Curvelet coefficient which of course causes some problems such as killing too many signal Curvelet coefficients that might contain useful image information. Here we introduce BayesShrink denoising scheme into Curvelet domain that is an adaptive, data-driven thresholding approach for image denoising, namely CurShrink. The threshold is derived in a Bayesian framework, and the prior used on the Curvelet coefficients is the generalized Gaussian distribution (GGD) widely used in image processing applications. The approach is valid because Curvelet transform produce correlated Curvelet coefficients, along the edge or curve of the image;the large Curvelet coefficients maybe have large Curvelet coefficients as it neighbors. Experimental results show that the proposed method is better than hard-thresholding denoising scheme in wavelet and curvelet domain. © the authors.

关键词： image denoising

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Atmospheric correction of remotely sensed multispectral satellite images in transform domain

Atmospheric correction of remotely sensed multispectral sate...

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4th International conference on Advanced Computing, ICoAC 2012

作者： Gandhimathi Usha, S. Vasuki, S. Ariputhiran, Gowri Department of ECE Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9781467355827

Remote sensing data provides much essential and critical information for monitoring many applications such as change detection, image fusion and land cover classification. Remotely sensed images are degraded due to the atmospheric effects. The atmospheric correction is one of the important pre processing steps to extract full spectral information from the remotely sensed images. In this paper, transform domain approaches are presented for the removal of atmospheric influences. Soft thresholding technique is adopted in wavelet transform method and gaussian high pass filter is used in homomorphic filtering. The results are tested on Landsat image consisting of 7 multispectral bands and their performance is evaluated using visual and statistical measures. The comparative analysis is done based on statistical parameters such as mean square error (MSE), peak signal to noise ratio (PSNR). Our result shows that wavelet transform is better for the removal of atmospheric effects than homomorphic filtering. © 2012 IEEE.

关键词： Discrete wavelet transforms

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Proceedings: Turing 100 - International conference on Computing Sciences, ICCS 2012

Proceedings: Turing 100 - International Conference on Comput...

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Turing 100 - International conference on Computing Sciences, ICCS 2012

ISBN: (纸本)9780769548173

The proceedings contain 73 papers. The topics discussed include: a novel approach based on wavelet and non-linear transfer function-based local approach for real color image enhancement;efficient application of gabor filters with nonlinear support vector machines;on the design of interpretable evolutionary fuzzy systems (I-EFS) with improved accuracy;analysis of joins and semi-joins in centralized and distributed database queries;theory of markov's normal algorithms;symbol manipulating techniques;normal algorithmic signal processing;SPL(A) - an elementary formal system for signal processing;rewriting cyclic normal automata and their applications;constructive sets and extended topological filters;cellular automata and their realizations;normal algorithmetic implementation of cellular automata;and a user friendly GUI based benchmark for image watermarking.

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Block-based video compressive sensing with exploration of local sparsity

Block-based video compressive sensing with exploration of lo...

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2012 International conference on image processing, Computer Vision, and Pattern Recognition, IPCV 2012

作者： Famodimu, Akintunde Cui, Suxia Wang, Yonghui Akujuobi, Cajetan M. Chaparral Energy Oklahoma City OK United States ECE Department Prairie View A and M University Prairie View TX United States ET Department Prairie View A and M University Prairie View TX United States College of CSMT Alabama State University Montgomery AL United States

ISBN: (纸本)9781601322258

Nowadays, compressive sensing is becoming an increasingly popular method for signal processing. The fact that it supersedes the Shannon-Nyquist criterion for sampling makes it very attractive for processing image/video signals in the increasingly media rich environment. Features individual measurement package in transmission, compressive sensing based coding system is more robust to channel noise. The block-based approach lends itself to a parallel processing and makes it capable for applications such as surveillance camera. The purpose of this paper is to improve the pioneering work done in compressive sensing by developing and implementing a block sparsity detection system. Furthermore, different transform based local sparsity decision methods are applied to the above mentioned system to compare the effectiveness of sparsity detection. Experimental results show that the Gini index calculated in Discrete wavelet Transform domain is a promising method for detecting local sparsity.

关键词： Discrete wavelet transforms

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A GPGPU accelerated compressed sensing with tight wavelet frame transform technique for MR imaging reconstruction

A GPGPU accelerated compressed sensing with tight wavelet fr...

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2012 IEEE International conference on Imaging Systems and Techniques, IST 2012

作者： Hu, B. Ma, X. Joyce, M. Glover, Paul Naleem, Buhary Department of Engineering Lancaster University Lancaster United Kingdom Sir Peter Mansfield Magnetic Resonance Centre University of Nottingham Nottingham United Kingdom Department of Electrical and Electronic Engineering University of Nottingham Nottingham United Kingdom

ISBN: (纸本)9781457717741

High resolution Magnetic Resonance Imaging (MRI) requires long acquisition time to obtain the fully sampled k-space data for image reconstruction. Compressed Sensing (CS) theory has recently been utilized to accelerate the image reconstruction speed by sparsely sampling the k-space. In this work, the CS framework was combined with the Tight wavelet Frame (TWF) transform to further enhance edges/boundaries of MR images and reduce their noise levels. Because the TWF coefficients at finer scale correspond to important image boundary features, the proposed algorithm is able to effectively enhance the signal to noise ratio of MR images without blurring their edges or create artifacts. The resulting constrained minimization problem is then solved iteratively and requires extensive computational resources. To accelerate the reconstruction for real-time medical image processing purpose, the algorithm is implemented on the General Purpose Graphic processing Units (GPGPU). The effects of various factors, including the register counts and block size, on the GPU occupancy have been investigated to tune the hardware for the optimum performance. The proposed algorithm demonstrates great potential to accelerate the MR imaging acquisition by 8-fold without noticeable artifacts. Comparisons with other two l1 minimization methods with traditional wavelet transforms further confirm the competitiveness of the proposed algorithm. Moreover, a speedup of 45 times was achieved by the GPGPU implementation compared with the CPU version, and therefore making this algorithm suitable for applications in a clinical MRI setting. © 2012 IEEE.

关键词： Magnetic resonance imaging

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Digital image watermarking using multilevel wavelet decomposition and human visual system

Digital image watermarking using multilevel wavelet decompos...

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2012 International conference on Information Science and applications, ICISA 2012

作者： Jan, Zahoor Khalid, Muhammad Mirza, Anwar M. Jabeen, Fouzia Abbasi, Almas Tarin, Zabie Durrani, Alwina Department of Computer Science Abdul Wali Khan University Mardan Palosa Campus Charsadda Pakistan Department of Computer Science Government College of Management Sciences Peshawar Pakistan Department of Computer Engineering College of Computer and Information Sciences King Saud University P.O. Box 51178 Riyadh 11543 Saudi Arabia Shaheed Benazir Bhutto Women University Peshawar Pakistan Faculty of Computer Science and Information Technology University of Malaya Malaysia

ISBN: (纸本)9781467321860

In this modern world Digital watermarking is of prime importance. This has increased the demand for copyright protection. Digital watermarking is a solution to the problem of copyright protection and authentication of multimedia data while working in a networked environment. In this paper a non-blind watermarking has been proposed that embed a randomly generated binary numbers as watermark. Human Visual System (HVS) has been used to embed the watermark below the detection threshold (JND). High quality of robustness and imperceptibility are achieved through Just Noticeable Difference (JND) base selection of the detail coefficients. The experimental results show that the proposed technique is more resistant against all types of common signal processing operations and geometric attacks. © 2012 IEEE.

关键词： Discrete wavelet transforms

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