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Exploring the Impact of wavelet-based Denoising in the Classification of Remote Sensing Hyperspectral images 22

Exploring the Impact of Wavelet-based Denoising in the Class...

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conference on image and signal processing for Remote Sensing XXII

作者： Quesada-Barriuso, Pablo Heras, Dora B. Arguello, Francisco Univ Santiago de Compostela Ctr Singular Invest Tecnoloxias Informac CiTIUS Santiago Spain Univ Santiago de Compostela Dept Elect & Comp Santiago Spain

ISBN: (数字)9781510604131

ISBN: (纸本)9781510604124;9781510604131

The classification of remote sensing hyperspectral images for land cover applications is a very intensive topic. In the case of supervised classification, Support Vector Machines (SVMs) play a dominant role. Recently, the Extreme Learning Machine algorithm (ELM) has been extensively used. The classification scheme previously published by the authors, and called WT-EMP, introduces spatial information in the classification process by means of an Extended Morphological Profile (EMP) that is created from features extracted by wavelets. In addition, the hyperspectral image is denoised in the 2-D spatial domain, also using wavelets and it is joined to the EMP via a stacked vector. In this paper, the scheme is improved achieving two goals. The first one is to reduce the classification time while preserving the accuracy of the classification by using ELM instead of SVM. The second one is to improve the accuracy results by performing not only a 2-D denoising for every spectral band, but also a previous additional 1-D spectral signature denoising applied to each pixel vector of the image. For each denoising the image is transformed by applying a 1-D or 2-D wavelet transform, and then a NeighShrink thresholding is applied. Improvements in terms of classification accuracy are obtained, especially for images with close regions in the classification reference map, because in these cases the accuracy of the classification in the edges between classes is more relevant.

关键词： Remote sensing Land cover classification Hyperspectral analysis wavelet transform Feature extraction Morphological profiles Denoising Spectral-Spatial processing

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Local Gabor wavelet-Based Feature Extraction and Evaluation 3rd

Local Gabor Wavelet-Based Feature Extraction and Evaluation

引用

3rd International conference on Advanced Computing, Networking, and Informatics (ICACNI)

作者： Malathi, T. Bhuyan, M. K. Indian Inst Technol Dept Elect & Elect Engn Gauhati 781039 India

ISBN: (纸本)9788132225386;9788132225379

Feature extraction is an essential step in many image processing and computer vision applications. It is quite desirable that the extracted features can effectively represent an image. Furthermore, the dominant information visually perceived by human beings should be efficiently represented by the extracted features. Over the last few decades, different algorithms are proposed to address the major issues of image representations by the efficient features. Gabor wavelet is one of the most widely used filters for image feature extraction. Existing Gabor wavelet-based feature extraction methodologies unnecessarily use both the real and the imaginary coefficients, which are subsequently processed by dimensionality reduction techniques such as PCA, LDA etc. This procedure ultimately affects the overall performance of the algorithm in terms of memory requirement and the computational complexity. To address this particular issue, we proposed a local image feature extraction method by using a Gabor wavelet. In our method, an image is divided into overlapping image blocks, and subsequently each of the image blocks are separately filtered out by Gabor wavelet. Finally, the extracted coefficients are concatenated to get the proposed local feature vector. The efficacy and effectiveness of the proposed feature extraction method is evaluated using the estimation of mean square error (MSE), peak signal-to-noise ratio (PSNR), and the correlation coefficient (CC) by reconstructing the original image using the extracted features, and compared it with the original input image. All these performance evaluation measures clearly show that real coefficients of the Gabor filter alone can effectively represent an image as compared to the methods which utilize either the imaginary coefficients or the both. The major novelty of our method lies on our claim-capability of the real coefficients of a Gabor filter for image representation.

关键词： image feature extraction Gabor filter image reconstruction

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Automated Detection of Current Fetal Head in Ultrasound Sequences 2

Automated Detection of Current Fetal Head in Ultrasound Sequ...

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2nd International conference on image processing, applications and Systems (IPAS)

作者： Sahli, Hanene Ben Slama, Amine Zaafouri, Ahmed Sayadi, Mounir Rachdi, Rathwen Univ Tunis ENSIT Lab SIME Tunis Tunisia Mil Hosp Dept Matern Tunis Tunisia

ISBN: (纸本)9781509016457

Accurate diagnostic and prognostic of fetus detects is an important challenge based on fetal head formation to supply much critical information that requires more attention in evaluating the abnormal heads. One of the fundamental problems currently faced, is how to limit the low signal to noise ratio with respect to the complexity of small fetal head ultrasound images dimension. This paper deals with a fully automatic detection system of subsequent fetal head composition from ultrasound images. In the preprocessing task, two filters have been used for speckle noise reducing. Using the Hough transform technique, fetal head structure detection is achieved, giving 97% as segmentation accuracy. Experimental results are analyzed using five ultrasound sequences that illustrate the effectiveness and the accuracy of the proposed method for a factual diagnostic of fetal heads.

关键词： fetal head ultrasound images Hough transform anisotropic diffusion discrete wavelet transform

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image Sharpness Assessment Based on Local Phase Coherence and LAD Criterion 23

Image Sharpness Assessment Based on Local Phase Coherence an...

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23rd International conference on Systems, signals and image processing (IWSSIP)

作者： Krzic, Ana Sovic Donlic, Matea Pejcinovic, Mihaela Sersic, Damir Univ Zagreb Fac Elect Engn & Comp Unska 3 Zagreb 10000 Croatia Poslovna Inteligencija Zelinska 4 Zagreb 10000 Croatia

ISBN: (纸本)9781467395557

Edge preservation and precise restoration of details and sharp parts of images is an important issue in modern multimedia systems, 3D imaging, medical imaging and laser microscopy. Objective evaluation of image restoration algorithms in the sense of sharpness is crucial for choice of method to be used. Moreover, automated focus stacking, selective deblurring and other smart restoration methods rely on the proper sharpness measure. In this paper, a novel approach for image sharpness assessment is proposed. The approach is based on local phase coherence (LPC) in the complex wavelet domain. Using maximum pooling of eight LPC maps in eight spatial directions around each pixel, an image sharpness map is generated. Least absolute deviation criterion applied on sorted non-zero elements of the sharpness map results in the proposed global image sharpness measure. The proposed measure is better suited for applications, in comparison with competitive measures.

关键词： Sharpness Assessment Sharpness Measure Blurriness Local Phase Coherence

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Custom Instruction for NIOS II processor FFT implementation for image processing

Custom Instruction for NIOS II processor FFT implementation ...

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conference on Sensing and Analysis Technologies for Biomedical and Cognitive applications

作者： Sundararajan, Sindhuja Meyer-Baese, Uwe Botella, Guillermo FAMU FSU ECE Dept 2525 Pottsdamer St Tallahassee FL 32310 USA Univ Complutense Madrid Dept Comp Architecture E-28040 Madrid Spain

ISBN: (纸本)9781510601123

image processing can be considered as signal processing in two dimensions (2D). Filtering is one of the basic image processing operation. Filtering in frequency domain is computationally faster when compared to the corresponding spatial domain operation as the complex convolution process is modified as multiplication in frequency domain. The popular 2D transforms used in image processing are Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT) and Discrete wavelet Transform (DWT). The common values for resolution of an image are 640x480, 800x600, 1024x768 and 1280x1024. As it can be seen, the image formats are generally not a power of 2. So power of 2 FFT lengths are not required and these cannot be built using shorter Discrete Fourier Transform (DFT) blocks. Split radix based FFT algorithms like Good-Thomas FFT algorithm simplifies the implementation logic required for such applications and hence can be implemented in low area and power consumption and also meet the timing constraints thereby operating at high frequency. The Good-Thomas FFT algorithm which is a Prime Factor FFT algorithm (PFA) provides the means of computing DFT with least number of multiplication and addition operations. We will be providing an Altera FPGA based NIOS II custom instruction implementation of Good-Thomas FFT algorithm to improve the system performance and also provide the comparison when the same algorithm is completely implemented in software.

关键词： DFT FFT DCT DWT PFA FPGA NIOS image processing

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An Anatomization of Noise removal techniques on Medical images 1

An Anatomization of Noise removal techniques on Medical Imag...

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1st International conference on Innovation and Challenges in Cyber Security (ICICCS)

作者： Pandey, Madhulika Bhatia, Madhulika Bansal, Abhay Amity Univ Dept Comp Sci & Engn Noida India Amity Univ Int Collaborat Noida India

ISBN: (纸本)9781509020843

In applications of signal processing such as medicine, communications and satellites, preprocessing is considered as a vital step which focuses on reduction or removal of the level of the noise contained in the image. The process of denoising helps in preserving the finer details and useful information. Medical images like MRI, CT and X-ray contain very fine details that need to be correct and free from noise so that the information and features of interests are not lost during the diagnosis. In this paper, various noise reduction techniques such as wavelet transform, Neural Network, PCA, ICA and mean and median filters over medical images has been discussed. In this paper we tried to highlight the strength and weakness of various noise removal techniques over processing of the medical images.

关键词： noise removal medical images wavelet transform PCA ICA median filter average median filter laplacian pyramid transform contourlet transform

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Hilbert-Huang Transform and its Application in Seismic signal processing

Hilbert-Huang Transform and its Application in Seismic Signa...

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International conference on New Trends in signal processing (NTSP)

作者： Beresik, Roman Armed Forces Acad Gen MR Stefanik Dept Elect Liptovsky Mikulas Slovakia

ISBN: (纸本)9788080405298

The detection of targets in military and security applications involves the usage of sensor systems which consist of a variety of sensors such as seismic, acoustic, magnetic and image ones as well. In order to extract signal features, which characterize particular targets, using of appropriate signal processing algorithms is required. Seismic signals can be considered as nonstationary and nonlinear signals especially in near-field seismic zone. Most of the signal processing algorithms assumed that signals are linear and stationary. However, in many cases this assumption is not valid, especially in case of seismic signals generated by moving vehicles, walking persons or gunfire activity. There are several methods which can be used for seismic signal processing, like short-time Fourier transform (STFT), wavelet transform (WT) and Wigner-Ville distribution (WVD). The paper presents the concept of the seismic sensor system based on Micro-Electro-Mechanical-System (MEMS) sensor SF1500S.A dedicated to vehicle detection. The main part of the paper deals with application of the Hilbert-Huang transform (HHT) to seismic signal processing in time and time-frequency domain. In conclusion, the outcomes of experiments provide comparison of HHT and STFT efficiency in terms of seismic features description of moving vehicle.

关键词： seismic signal signal decomposition Hilbert-Huang transform time-frequency analysis vehicle detection

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RDWT and SVD based secure digital image watermarking using ACM 24

RDWT and SVD based secure digital image watermarking using A...

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24th signal processing and Communication Application conference, SIU 2016

作者： Çerkezi, Llukman Çetinel, Gökçen Elektrik - Elektronik Mühendisliǧi Bölümü Sakarya Üniversitesi Sakarya Turkey

ISBN: (纸本)9781509016792

In this study, digital image watermarking method based on Redundant Discrete wavelet Transform (RDWT) and Singular value Decomposition (SVD) is proposed. By exploiting the complexity property of the chaotic signals, which is the main reason of using them in the security applications, the robustness and the invisibility of the proposed method is improved. In the proposed watermarking scheme, RDWT is performed to decompose the cover image into four sub-bands (LL, LH, HL and HH). Then SVD is applied to the LL sub-band of the cover image. The chaotic watermark is generated by applying Arnold Cat Map (ACM) to the original watermark. Finally, singular values of the LL sub-band of the cover image are modified with the singular values of the chaotic watermark according to the embedding process. It is demonstrated via computer simulations that the proposed RDWT, SVD and ACM based digital image watermarking scheme provides better watermark concealment and high robustness against both geometric and image processing attacks. Furthermore, the robustness of the proposed scheme is investigated for different dimensions of the binary watermark logo. It is shown that the robustness of our method is independent of the watermark size. © 2016 IEEE.

关键词： Singular value decomposition

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Pipeline and Parallel Processor Architecture for Fast Computation of 3D-DWT using Modified Lifting Scheme

Pipeline and Parallel Processor Architecture for Fast Comput...

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IEEE International conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Kumar, C. Ashok Madhavi, B. K. Lalkishore, K. CMR Engn Coll Dept ECE Hyderabad Andhra Pradesh India Sridevi Women Engn Coll Dept ECE Hyderabad Andhra Pradesh India JNTU Anantapur Dept ECE Anantapur Andhra Pradesh India

ISBN: (纸本)9781467393379

A pipelined parallel processing 3D DWT architecture is designed in this paper based on lifting scheme algorithm with 9/7 wavelet filters. The 3D DWT architecture process a 512x512 image with 8 groups of frames sequentially with improved throughput. The first stage computes 1D-DWT along the rows with 4 parallel processors, the memory interface with FIFO reduces latency between first stage and second stage that computes 2D DWT computation. 3D DWT computes wavelet coefficients in the temporal direction and designed to operate from 512*4 clock cycles in sequence along with 1D and 2D DWT computation. The FIFO designed synchronizes the data movement. Memories at every stage are designed to store the parallel processed data. The proposed architecture has high performance and is suitable for high speed, low power and portable applications. With utilization of 51% of slice registers 3D-DWT architecture implemented on Virtex-5 FPGA and frequency of operation is 373 MHz. The designed DWT-IDWT can be used as IP Core.

关键词： Lifting scheme pipelined architecture 3D DWT parallel processing FPGAs

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Translation based Face Recognition using Fusion of LL and SV Coefficients 12

Translation based Face Recognition using Fusion of LL and SV...

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12th International conference on Communication Networks (ICCN) / 12th International conference on Data Mining and Warehousing (ICDMW) / 12th International conference on image and signal processing (ICISP)

作者： Sujatha, B. M. Venukumar, B. V. Madiwalar, Chetan Tippanna Munna, N. C. Abidali Babu, K. Suresh Raja, K. B. Venugopal, K. R. Visveswaraya Tech Univ Acharya Inst Technol Bangalore Karnataka India Bangalore Univ Univ Visvesvaraya Coll Engn Bangalore Karnataka India

The face is a physiological trait used to identify a person effectively for various biometric applications. In this paper we propose Translation based Face Recognition using Fusion of LL and SV coefficients. The novel concept of translating many sample images of a single person into one sample per person is introduced. The face database images are preprocessed using Gaussian filter and DWT to generate LL coefficients. The support vectors (SV) are obtained from support vector machine (SVM) for LL coefficients. The LL and SVs are fused using arithmetic addition to generate final features. The face database and test face image features are compared using Euclidean Distance (ED) to compute the performance parameters. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： Biometrics Discrete wavelet Transform (DWT) Face Recognition (FR) Fusion Support Vector Machine (SVM)

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