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3rd International Conference on Communications, signal processing, and Systems (CSPS)

作者： Tong, Ying Liu, Leilei Zhao, Meirong Wei, Zilong Tianjin Normal Univ Coll Elect & Commun Engn Tianjin Peoples R China Tianjin Univ State Key Lab Precis Measuring Technol & Instrume Tianjin 300072 Peoples R China

ISBN: (纸本)9783319089911;9783319089904

Compressive sensing (CS) has given us a new idea at data acquisition and signal processing. It has proposed some novel solutions in many practical applications. Focusing on the image compressive sensing problem, the paper proposes an algorithm of compressive image sensing based on the multi-resolution analysis. We present the method to decompose the images by nonsubsampled contourlet transform (NSCT) and wavelet transform successively. It means that the images can be sparse represented by more than one basis functions. We named this process as blended basis functions representation. Since the NSCT and wavelet basis functions have complementary advantages in the image multi-resolution analysis, and the signals are more sparse after decomposed by two kinds of basis functions, the proposed algorithm has perceived advantages in comparison with compressive sensing in the wavelet domain which is widely reported by literatures. The simulations show that our method provides promising results.

关键词： Blended basis functions Compressive sensing NSCT wavelet transform

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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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Higher-order graph wavelets and sparsity on circulant graphs 16

Higher-order graph wavelets and sparsity on circulant graphs

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Conference on wavelets and Sparsity XVI

作者： Kotzagiannidis, Madeleine S. Dragotti, Pier Luigi Imperial Coll London Dept Elect & Elect Engn London England

ISBN: (纸本)9781628417630

The notion of a graph wavelet gives rise to more advanced processing of data on graphs due to its ability to operate in a localized manner, across newly arising data-dependency structures, with respect to the graph signal and underlying graph structure, thereby taking into consideration the inherent geometry of the data. In this work, we tackle the problem of creating graph wavelet filterbanks on circulant graphs for a sparse representation of certain classes of graph signals. The underlying graph can hereby be data-driven as well as fixed, for applications including image processing and social network theory, whereby clusters can be modelled as circulant graphs, respectively. We present a set of novel graph wavelet filterbank constructions, which annihilate higher-order polynomial graph signals (up to a border effect) defined on the vertices of undirected, circulant graphs, and are localised in the vertex domain. We give preliminary results on their performance for non-linear graph signal approximation and denoising. Furthermore, we provide extensions to our previously developed segmentationinspired graph wavelet framework for non-linear image approximation, by incorporating notions of smoothness and vanishing moments, which further improve performance compared to traditional methods.

关键词： signal processing on graphs graph wavelet circulant graph sparsity

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Classification of Breast Masses in Mammogram images using KNN 23

Classification of Breast Masses in Mammogram Images using KN...

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23nd signal processing and Communications applications Conference (SIU)

作者： Alpaslan, Nuh Kara, Asuman Zencir, Busra Hanbay, Davut Inonu Univ Bilgisayar Muhendisligi Bolumu Malatya Turkey

ISBN: (纸本)9781467373869

Breast cancer is one of the most deadly diseases for women. Mammogram is very important imaging tecnique used diagnosis in early stages of breast cancer. In this study, a decision support system which helps experts to examine mammogram images in the fight against breast cancer is developed. In this study, firstly several preprocesses are applied to mammogram to make image clear and segmentation of mass is provided with an appropriate threshold value. After the segmentation processes, features of the tumor mass are obtained. The obtained features are classified as normal, benign or malignant using kNN (k-nearest neighbours) classifiers. In this study, its have been were shown that, effect of kurtosis, skewness and wavelet energy features on classification performance is shown. As a result, it has been seen that, these features improve the classification performance.

关键词： Digital Mammography Breast Lump Breast Cancer wavelet energy decision support systems Imaging breast cancer stage images segmentation Cancer fight

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A Novel Approach for the Detection of Diabetic Retinopathy Disease 23

A Novel Approach for the Detection of Diabetic Retinopathy D...

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23nd signal processing and Communications applications Conference (SIU)

作者： Alver, Seyfullah Ay, Sinan Tetik, Yusuf Engin Kara Harp Okulu Bilgisayar Muhendisligi Bolumu Ankara Turkey

ISBN: (纸本)9781467373869

Diabetic retinopathy is the most common cause of blindness of the eye depend on diabetes. In this work, a novel approach is presented for the detection of diabetic retinopathy diseases from the retina images. For this purpose, firstly regions which are probably diseased are found and features are extracted from these regions by applying Discrete wavelet Transform. Afterwards the number of found features is reduced by Principal Component Analysis and Naive Bayes is used for the classification of them. This approach differs from the similar works by the way Region of Interest is found and the automatic selection of features instead of using hand-picked ones. It has been shown that the proposed system achieves an accuracy rate up to the 95% in the detection of the diseased retinas.

关键词： Detection of Diabetic Retinopathy Medical image Classification Region of Interest Discrete wavelet Transform Haar Features Principal Component Analysis Naive Bayes Classifier

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Guide to signals and Patterns in image processing: Foundations, Methods and applications 1

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2015年

作者： Apurba Das (auth.)

ISBN: (数字)9783319141725

ISBN: (纸本)9783319141718;9783319141725

This text reviews the field of digital image processing from the different perspectives offered by the separate domains of signal processing and pattern recognition. The book describes a rich array of applications, representing the latest trends in industry and academic research. To inspire further interest in the field, a selection of worked-out numerical problems is also included in the text. The content is presented in an accessible manner, examining each topic in depth without assuming any prior knowledge from the reader, and providing additional background material in the appendices. Features: covers image enhancement techniques in the spatial domain, the frequency domain, and the wavelet domain; reviews compression methods and formats for encoding images; discusses morphology-based image processing; investigates the modeling of object recognition in the human visual system; provides supplementary material, including MATLAB and C++ code, and interactive GUI-based modules, at an associated website.

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Application of Fuzzy Sets to Calculate the Value of Soft Threshold for image Denoising 2

Application of Fuzzy Sets to Calculate the Value of Soft Thr...

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2nd International Conference on signal processing and Integrated Networks (SPIN)

作者： Roy, Soumava Kumar Agrawal, Utkarsh Singh, Brijraj IIIT Allahabad Allahabad Uttar Pradesh India

ISBN: (纸本)9781479959914

Fuzzy Logic is a system that nearly represents human activities of thinking and linguistics. Fuzzy logic is very useful in handling uncertainty and is used in various other applications. In this paper, fuzzy logic is used to handle wavelet transformed image statistics which is embedded with Gaussian noise spread variably across the image. Adaptive Multilevel Soft Thresholding of the noisy image was done, which removed noise from almost all types of images. We also display that Fuzzy Logic System (FLS) attains good efficiency. The remodelling of wavelet coefficient proved effective a contemporary in image denoising horizon.

关键词： Type-1 Fuzzy Systems Sugeno Fuzzy Inference Engine Adaptive Multilevel Soft Threshold Wiener Filter Lee Filter

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Lanna Dharma Handwritten Character Recognition on Palm Leaves Manuscript based on wavelet Transform 4

Lanna Dharma Handwritten Character Recognition on Palm Leave...

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

作者： Inkeaw, Papangkorn Chueaphun, Chutima Chaijaruwanich, Jeerayut Klomsae, Atcharin Marukatat, Sanparith Chiang Mai Univ Fac Sci Dept Comp Sci Chiang Mai 50200 Thailand Chiang Mai Univ Fac Engn Dept Comp Engn Chiang Mai 50200 Thailand Natl Elect & Comp Technol Ctr Pathum Thani 12120 Thailand

ISBN: (纸本)9781479989966

Lanna Dharma alphabet is used in the past in the North of Thailand, mainly for religious communication. Most of handwritten Lanna Dharma is found in form of old palm leaves manuscripts. These documents have not been properly preserved, still unprotected and damaged by the time. To preserve these valuable documents, handwritten optical character recognition is one of the first choices. This paper proposes an efficient method for Lanna Dharma handwritten character recognition from palm leaves manuscript image. In recent years, research towards Dharma Lanna character recognition from printed document is proposed. However, the proposed method cannot be applied to handwritten documents. This research aims to compare the different feature extraction methods for Lanna Dharma handwritten recognition. The first step in the proposed method is image preprocessing that binarized, enhanced, line segmented, level segmented and character segmented. The next step, each character image was extracted as feature vector using various feature extraction method based on wavelet transform. Then several alternative feature extraction methods were compared by evaluating their effect on character recognition performance using K-Nearest Neighbor algorithm. The experimental results show that the best feature extraction is 2D, 1D wavelet transform and region properties feature extraction. The recognition rates of 10-fold crosses validation are 93.22 % for upper level, 95.48% for middle level, and 97.77% for lower level.

关键词： wavelet transforms

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Accelerating Discrete Haar wavelet Transform on GPU Cluster 9

Accelerating Discrete Haar Wavelet Transform on GPU Cluster

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9th International Conference on Electrical and Electronics Engineering (ELECO)

作者： Aslan, Selcuk Badem, Hasan Karaboga, Dervis Basturk, Alper Ozcan, Tayyip Erciyes Univ Fac Engn Dept Comp Engn Melikgazi Turkey

ISBN: (纸本)9786050107371

The Discrete Haar wavelet Transform has a wide range of applications from signal processing to video and image processing. Data-intensive structure and easy of implementation make Discrete Haar wavelet Transform convenient to distribute fundamental operations to multi-CPU and multiGPU systems. In this paper, the wavelet transform was ported in a compute-efficient way to CPU cluster and programmable GPU cluster by utilizing MPI and CUDA respectively. Experimental studies conducted as part of the parallelization strategies for two-dimensional Discrete Haar wavelet Transform show that the total running time required to process all rows and columns of an image with different size is significantly decreased on the GPU cluster when compared to the its counterparts on a single CPU, single GPU and CPU cluster. Besides the speedup of the GPU based transform, preliminary analysis also showed that the size of the image is an important parameter on the scalability of the GPU cluster.

关键词： Graphics processing unit

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Comparison of MRI Under-Sampling Techniques for Compressed Sensing with Translation Invariant wavelets Using FastTestCS: A Flexible Simulation Tool

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Journal of signal and Information processing 2016年第4期7卷 252-271页

作者： Christopher Baker Departments of Computer Science and Electrical Engineering University of Wisconsin-Milwaukee Milwaukee Wisconsin USA

A sparsifying transform for use in Compressed Sensing (CS) is a vital piece of image reconstruction for Magnetic Resonance Imaging (MRI). Previously, Translation Invariant wavelet Transforms (TIWT) have been shown to perform exceedingly well in CS by reducing repetitive line pattern image artifacts that may be observed when using orthogonal wavelets. To further establish its validity as a good sparsifying transform, the TIWT is comprehensively investigated and compared with Total Variation (TV), using six under-sampling patterns through simulation. Both trajectory and random mask based under-sampling of MRI data are reconstructed to demonstrate a comprehensive coverage of tests. Notably, the TIWT in CS reconstruction performs well for all varieties of under-sampling patterns tested, even for cases where TV does not improve the mean squared error. This improved image Quality (IQ) gives confidence in applying this transform to more CS applications which will contribute to an even greater speed-up of a CS MRI scan. High vs low resolution time of flight MRI CS re-constructions are also analyzed showing how partial Fourier acquisitions must be carefully addressed in CS to prevent loss of IQ. In the spirit of reproducible research, novel software is introduced here as FastTestCS. It is a helpful tool to quickly develop and perform tests with many CS customizations. Easy integration and testing for the TIWT and TV minimization are exemplified. Simulations of 3D MRI datasets are shown to be efficiently distributed as a scalable solution for large studies. Comparisons in reconstruction computation time are made between the Wavelab toolbox and Gnu Scientific Library in FastTestCS that show a significant time savings factor of 60×. The addition of FastTestCS is proven to be a fast, flexible, portable and reproducible simulation aid for CS research.

关键词： Compressed Sensing Translation Invariant wavelet Simulation Software Total Variation l1 Minimization

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