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Modified wavelet kernel methods for hyperspectral image classification 21

Modified wavelet kernel methods for hyperspectral image clas...

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conference on image and signal processing for Remote Sensing XXI

作者： Hsu, Pai-Hui Huang, Xiu-Man Natl Taiwan Univ Dept Civil Engn Taipei 10617 Taiwan

ISBN: (纸本)9781628418538

Hyperspectral images have the capability of acquiring images of earth surface with several hundred of spectral bands. Providing such abundant spectral data should increase the abilities in classifying land use/ cover type. However, due to the high dimensionality of hyperspectral data, traditional classification methods are not suitable for hyperspectral data classification. The common method to solve this problem is dimensionality reduction by using feature extraction before classification. Kernel methods such as support vector machine (SVM) and multiple kernel learning (MKL) have been successfully applied to hyperspectral images classification. In kernel methods applications, the selection of kernel function plays an important role. The wavelet kernel with multidimensional wavelet functions can find the optimal approximation of data in feature space for classification. The SVM with wavelet kernels (called WSVM) have been also applied to hyperspectral data and improve classification accuracy. In this study, wavelet kernel method combined multiple kernel learning algorithm and wavelet kernels was proposed for hyperspectral image classification. After the appropriate selection of a linear combination of kernel functions, the hyperspectral data will be transformed to the wavelet feature space, which should have the optimal data distribution for kernel learning and classification. Finally, the proposed methods were compared with the existing methods. A real hyperspectral data set was used to analyze the performance of wavelet kernel method. According to the results the proposed wavelet kernel methods in this study have well performance, and would be an appropriate tool for hyperspectral image classification.

关键词： Hyperspectral image Kernel Method wavelet Kernel Support Vector Machine Classification

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wavelet analysis deformation monitoring data of high-speed railway bridge

Wavelet analysis deformation monitoring data of high-speed r...

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International conference on Intelligent Earth Observing and applications (IEOAs)

作者： Tang, ShiHua Huang, Qing Zhou, Conglin Xu, HongWei Liu, YinTao Li, FeiDa Guilin Univ Technol Coll Surveying & Geoinformat Guilin Guangxi Peoples R China Spatial Informat & Key Lab Surveying & Mapping Gu Guilin Guangxi Peoples R China

ISBN: (纸本)9781510600492

Deformation monitoring data of high-speed railway bridges will inevitably be affected because of noise pollution, A deformation monitoring point of high-speed railway bridge was measurd by using sokkia SDL30 electronic level, which got a large number of deformation monitoring data. Based on the characteristics of the deformation monitoring data, which contain lots of noise. Based on the MATLAB software platform, 120 groups of deformation monitoring data were applied to analysis of wavelet denoising. sym6, db6 wavelet basis function were selected to analyze and remove the noise. high frequency coefficient that was removed much of noise combined with low frequency coefficient to reconstitute and obtain reconstruction wavelet signal. Root Mean Square Error (RMSE) and signal-To-Noise Ratio (SNR) were regarded as evaluation index of denoising, The smaller the RMSE and the greater SNR indicate that them have a good effect in denoising. We can surely draw some conclusions in the experimental analysis: the db6 wavelet basis function has a good effect in wavelet denoising by using a adaptive soft threshold method, which RMSE is minimum and SNR is maximum. Moreover, the reconstructed image are more smooth than original signal denoising after wavelet denoising, which removed noise and useful signal are obtained in the original signal. Compared to the other three methods, this method has a good effect in denoising, which not only retain useful signal in the original signal, but aiso reach the goal of removing noise. So, it has a strong practical value in a actual deformation monitoring project.

关键词： High-speed railway Bridges SDL30 electronic level Deformation monitoring wavelet analysis The wavelet basis function Denoising reconstructing evaluation index

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wavelet Based Imperceptible Medical image Watermarking Using Spread-Spectrum 38

Wavelet Based Imperceptible Medical Image Watermarking Using...

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38th International conference on Telecommunications and signal processing (TSP)

作者： Kumar, Basant Kumar, Shishu Bind Chauhan, Digvijay Singh Motilal Nehru Natl Inst Technol Allahabad Uttar Pradesh India Feroze Gandhi Inst Engn & Technol Raebareli India

ISBN: (纸本)9781479984985

This paper presents a spread-spectrum watermarking algorithm for embedding text watermark in to digital images in discrete wavelet transform (DWT) domain. The algorithm is applied for embedding text file represented in binary arrays using ASCII code into host digital radiological image for potential telemedicine applications. In order to enhance the robustness of text watermarks like patient identity code, BCH (Bose, Ray-Chaudhuri, Hocquenghem) error correcting code (ECC) is applied to the ASCII representation of the text watermark before embedding. Performance of the algorithm is analysed by varying the gain factor, subband decomposition levels, and length of watermark. Robustness of the scheme is tested against various attacks like compression, filtering, noise, sharpening, scaling and histogram equalization. Simulation results show that the proposed method achieves imperceptible watermarking for string watermarks. It is also observed that the use of BCH code improves the performance by reducing bit error rate (BER) performance.

关键词： BER BCH discrete wavelet transform HVS spread-spectrum Watermarking

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A New Approach for an Effective Eye Movement Artifact Elimination from EEG signal 4

A New Approach for an Effective Eye Movement Artifact Elimin...

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IEEE 2015 International conference on signal and image processing applications (ICSIPA)

作者： Ibrahim, Ibrahim Amer Santhosh, Jayasree Moghavvemi, Mahmoud Univ Malaya Dept Elect Engn Kuala Lumpur 50603 Malaysia Univ Baghdad ALKHWARIZMI Engn Baghdad Iraq Univ Malaya Dept Biomed Engn Kuala Lumpur 50603 Malaysia Univ Sci & Culture CRAE Tehran Iran

ISBN: (纸本)9781479989966

EEG signals collected non-invasively from the cerebral cortex are often contaminated with signals from eye movement [EOG], which considerably degrades the reliability of extracting proper information from the actual signals. This study attempts a two-step algorithm to obtain an EOG artifact free EEG signal with best performance index. Two types of decomposing methods namely Discrete wavelet Transform (DWT) and Empirical Mode Decomposition (EMD) were used initially to decompose the contaminated signal. The resultant signal further undergoes two processes each;first by Canonical Correlation analysis (CCA) technique and the second one by reduced frequency components using threshold level to identify the unwanted frequency components (TH.-FFT). The output signal from each of these two processes are reconstructed to obtain a clean signal. Comparison between the reconstructed cleaned signals and the contaminated EEG signal reveal that the reconstructed signal is cleaner when (CCA) technique was used as compared with that of (TH.-FFT). Thus the performance indexes of this experiment for the two methods, measured with RMSD and SDR, indicated that combined DWT, EMD and CCA method outperforms the combined DWT, EMD and TH.-FFT method in elimination of eye movement contamination from EEG signal. The present algorithm gave better performance results compared to other existing methods and hence would be better suited for various offline analyses involving the low frequency bands of EEG signals.

关键词： EEG (Electroencephalogram) EOG (Electrooculography) CCA (Canonical Correlation analysis) EMD (Empirical Mode Decomposition) DWT (Discrete wavelet Transform) Artifact Elimination

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wavelet merged multi-resolution super-pixels and their applications on fluorescent MSC images 23

Wavelet merged multi-resolution super-pixels and their appli...

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2015 23rd signal processing and Communications applications conference, SIU 2015

作者： Yorulmaz, Onur Oʇuz, Oʇuzhan Akhan, Ece Tuncel, Dönüş Atalay, Rengül C. Çetin, A. Enis Elektrik Ve Elektronik Mühendisliʇi BÖlümü Bilkent Üniversitesi Ankara Turkey Moleküler Biyoloji Ve Genetik BÖlümü Bilkent Üniversitesi Ankara Turkey Kimya BÖlümü Bilkent Üniversitesi Ankara Turkey Enformatik Enstitüsü Orta Doʇu Teknik Üniversitesi Ankara Turkey

ISBN: (纸本)9781467373869

A new multi-resolution super-pixel based algorithm is proposed to track cell size, count and motion in Mesenchymal Stem Cells (MSCs) images. Multi-resolution super-pixels are obtained by placing varying density seeds on the image. The density of the seeds are determined according to the local high frequency components of the MSCs image. In this way a multi-resolution super-pixels decomposition of the image is obtained. A second contribution of the paper is novel decision rule for merging similar neighboring super-pixels. An algorithm based on well known wavelet decomposition is developed and applied to the histograms of neighboring super pixels to exploit similarity. The proposed algorithm is experimentally shown to be successful in segmenting and tracking cells in MSCs images. © 2015 IEEE.

关键词： wavelet decomposition

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Fusion of Fuzzy Enhanced Overexposed and Underexposed images 11

Fusion of Fuzzy Enhanced Overexposed and Underexposed Images

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11th International conference on image and signal processing (ICISP)

作者： Dhingra, Naina Nandal, Amita Manchanda, Meenu Gambhir, Deepak NIT Hamirpur Dept ECE Hamirpur HP India Vaish Coll Engn Rohtak Rohtak Haryana India Amity Sch Engn & Technol Bijwasan New Delhi India

An image fusion algorithm based on DWT is used to combine fuzzy enhanced overexposed images and underexposed images. The proposed method provides fused image containing visual salient information present in both the input images along with significant contrast enhancement. The proposed algorithm is useful for a wide range of applications such as military applications, surveillance, medical diagnosis etc. The fused image obtained from DWT fusion of fuzzy enhanced images is visually better than those obtained from DWT fusion of: unenhanced images, gamma corrected images as well as histogram equalized images. Both subjective and objective assessment is performed to evaluate the performance of the proposed algorithm. The objective image quality measures: entropy and standard deviation, also shows that the proposed method performs better than the other method. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： Fuzzy image enhancement Gamma correction Illuminated images image fusion Discrete wavelet transform (DWT)

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MULTI-RESOLUTION SUPER-PIXELS AND THEIR applications ON FLUORESCENT MESENCHYMAL STEM CELLS imageS USING 1-D SIFT MERGING

MULTI-RESOLUTION SUPER-PIXELS AND THEIR APPLICATIONS ON FLUO...

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IEEE International conference on image processing (ICIP)

作者： Yorulmaz, Onur Oguz, Oguzhan Akhan, Ece Tuncel, Donus Atalay, Rengul C. Cetin, A. Enis Bilkent Univ Dept Elect & Elect Engn TR-06800 Ankara Turkey Bilkent Univ Dept Chem TR-06800 Ankara Turkey Bilkent Univ Dept Mol Biol & Genet TR-06800 Ankara Turkey Middle E Tech Univ Dept Informat TR-06800 Ankara Turkey

ISBN: (纸本)9781479983391

A new multi-resolution super-pixel based algorithm is proposed to track cell size, count and motion in Mesenchymal Stem Cells (MSCs) images. Multi-resolution super-pixels are obtained by placing varying density seeds on the image. The density of the seeds are determined according to the local high frequency components of the MSCs image. In this way a multi-resolution super-pixels decomposition of the image is obtained. A second contribution of the paper is novel decision rule for merging similar neighboring super-pixels. One-dimensional version of the well known scale invariant feature transform (SIFT) is developed and applied to the histograms of the neighboring super-pixels to determine similar regions. The proposed algorithm is experimentally shown to be successful in segmenting and tracking cells in MSCs images.

关键词： Stem cell tracking multi-resolution super-pixels wavelet SIFT decomposition fluorescent image

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Contrast Enhancement of Mammograms images Based on Hybrid processing 3

Contrast Enhancement of Mammograms Images Based on Hybrid Pr...

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3rd International conference on Frontiers in Intelligent Computing - Theory and applications (FICTA)

作者： Wani, Inam Ul Islam Hanumantharaju, M. C. Gopalkrishna, M. T. Dayananda Sagar Coll Engn Dept Informat Sci & Engn Bangalore Karnataka India BMS Inst Technol Dept Elect & Commun Engn Bangalore Karnataka India

ISBN: (纸本)9783319120126;9783319120119

This paper introduces a new enhancement algorithm based on combination of different processing techniques. The method uses different methods at different stages of processing. In the beginning input image given to the algorithm is a portable gray map image and then Gaussian low pass filter is used to decompose the input image into low and high frequency components. On low frequency components we apply mathematical morphological operations and on high frequency components we apply edge enhanced algorithm. After this we combine processed low and high frequency components to get an enhanced image. Enhanced image is having better contrast and edge visibility comparing to the original image, but it contains noises. wavelet transform is used to denoise the noisy image. The denoised image is then processed by using contrast limited adaptive histogram equalization(CLAHE) to have better edge preservation index (EPI) and contrast improvement index (EPI). The resulting image is then smoothed by passing the output image through a guided image filter(GIF). The edge preserve capacity and preservation of the naturalness of the GIF allows us to get better results. The efficiency of any service or product, especially those related to medical field depends upon its applicability. The applicability for any service or products can be achieved by applying the basic principles of Software Engineering. Applicability of enhanced algorithms depends on parameters like the peak signal to noise ratio(PSNR), edge preservation index(EPI), etc. This paper focuses on introducing a model which highlights on a prototyping approach for highlighting the necessary details that will aid radiologist for the earlier detection of breast cancer. This paper also presents Design of the model, Implementation of the model and finally the results are analyzed by considering the quality metrics values like PSNR, EPI, CII.

关键词： Gaussian Filter Mathematical Morphology Contrast Enhancement CLAHE wavelet Guided image Filtering Mammographic image Medical image processing

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image Denoising Using Multiresolution Singular value Decomposition Transform

Image Denoising Using Multiresolution Singular value Decompo...

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International conference on Information and Communication Technologies (ICICT)

作者： Malini, S. Moni, R. S. Marian Engn Coll Trivandrum Ctr LBS Inst Trivandrum 695582 Kerala India

images are often corrupted by noise. For visual quality as well as for satisfactory extraction of important features from the images, denoising of the images is necessary. It is an unavoidable pre-processing step for many applications such as image compression, segmentation, identification, fusion, object recognition etc. Many successful algorithms have been proposed over the past few decades for image denoising. A recent development in this area of research is the use of multiresolution principles. wavelet decomposition and denoising are milestones in multiresolution image signal processing. In this paper, multiresolution singular value decomposition is proposed as a new method for denoising of images. The new algorithm and its implementation using MATLAB is presented. Results show that it is a good method for image denoising. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： image denoising multiresolution multiresolution svd sub bands thresholding

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Hierarchical Lossless image Compression for Telemedicine applications 11

Hierarchical Lossless Image Compression for Telemedicine App...

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11th International conference on image and signal processing (ICISP)

作者： Sumalatha, R. Subramanyam, M. V. Santhiram Engn Coll Nandyal India G Pullaiah Coll Engn & Technol Kurnool 518002 India

The main aim of hierarchical lossless image compression is to improve accuracy, reduce the bit rate and improve the compression efficiency for the storage and transmission of the medical images while maintain an acceptable image quality for diagnosis purpose. The cost and limitation in bandwidth of wireless channels has made compression is necessity in today's era. In medical images, the contextual region is an area which contains an important information and must be transmitted without distortion. In this paper the selected region of the image is encoded with Adaptive Multiwavelet Transform AMWT) using Multi Dimensional Layered Zero Coding (MLZC). Experimental results shows that Peak signal to Noise Ratio (PSNR), Correlation Coefficient (CC), Mean Structural Similarity Index (MSSIM) performance is high and Root Mean Square Error (RMSE), Mean Absolute Error (MAE) values are low, and moderate Compression Ratio (CR) at high Bits Per Pixel (BPP) when compared to the integer wavelet and multiwavelet transform. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/). Peer-review under responsibility of Universal Society for Applied Research

关键词： AMWT Contextual region Hierarchical image coding MLZC Multiwavelet

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