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International Conference on Innovation and Challenges in Cyber Security (ICICCS-INBUSH)

作者： Madhulika Pandey Madhulika Bhatia Abhay Bansal Computer Science and Engineering Department Amity University Noida India

ISBN: (纸本)9781509020850

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： Medical diagnostic imaging image edge detection Artificial neural networks Noise reduction Uncertainty Principal component analysis

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Saliency guided wavelet compression for low-bitrate image and Video coding

Saliency guided Wavelet compression for low-bitrate Image an...

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3rd IEEE Global Conference on signal and Information processing (GlobalSIP)

作者： Barua, Souptik Mitra, Kaushik Veeraraghavan, Ashok Rice Univ Houston TX 77251 USA Indian Inst Technol Madras Madras Tamil Nadu India

ISBN: (纸本)9781479975914

We propose an improved saliency guided wavelet compression scheme for low-bitrate image/video coding applications. Important regions (faces in security camera feeds, vehicles in traffic surveillance) get degraded significantly at low bitrates by existing compression standards, such as JPEG/JPEG-2000/MPEG-4, since these do not explicitly utilize any knowledge of which regions are salient. We design a compression algorithm which, given an image/video and a saliency value for each pixel, computes a corresponding saliency value in the wavelet transform domain. Our algorithm ensures wavelet coefficients representing salient regions have a high saliency value. The coefficients are transmitted in decreasing order of their saliency. This allows important regions in the image/video to have high fidelity even at very low bitrates. Further, our compression scheme can handle several salient regions with different relative importance. We compare the performance of our method with the JPEG/JPEG-2000 image standards and the MPEG-4 video standard through two experiments: face detection and vehicle tracking. We show improved detection rates and quality of reconstructed images/videos using our Saliency Based Compression (SBC) algorithm.

关键词： Saliency wavelet transform image coding Video coding

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Spline and spline wavelet methods with applications to signal and image processing. Volume 1, Per...

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2014年

作者： Averbuch Amir Z.

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Lagrangian support vector regression based image watermarking in wavelet domain 2

Lagrangian support vector regression based image watermarkin...

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

作者： Mehta, Rajesh Vishwakarma, Virendra P. Rajpal, Navin Amity Sch Engn & Technol Dept Comp Sci & Engn New Delhi India Gtrru Gobind Singh Indraprastha Univ Univ Sch Informat & Commun Technol Sect 16C New Delhi India Gtrru Gobind Singh Indraprastha Univ Univ Sch Informat & Commun Technol Dwarka Sect 16C New Delhi India

ISBN: (纸本)9781479959914

To enhance the imperceptibility and robustness against image processing operations, the advantage of artificial neural network (ANN) and machine learning algorithms such as support vector regression (SVR), extreme learning machine (ELM) etc. are employed into watermarking applications. In this paper, Lagrangian support vector regression (LSVR) based blind image watermarking scheme in wavelet domain is proposed. The good learning capability, high generalization property against noisy datasets and less computational cost of LSVR compared to traditional SVR and ANN based algorithms makes the proposed scheme more imperceptible and robustness. Firstly, four sub images of host image are obtained using sub sampling. Each sub image is decomposed using discrete wavelet transform (DWT) to obtain the low frequency subband. Low frequency coefficients of each sub image are used to form the dataset act as input to LSVR. The output obtained by trained LSVR is used to embed the binary watermark. The security of the watermark is enhanced by applying Arnold transformation. Experimental results show the imperceptibility and robustness of the proposed scheme against several image processing attacks. The visual quality of watermarked image is quantified by the peak-signal-to noise ratio (PSNR) and the similarity between the original and extracted watermark is evaluated using bit error rate (BER). Performance of the proposed scheme is verified by comparing with the state-of-art techniques.

关键词： wavelet transform Lagrangian support vector regression Peak signal to noise ratio Bit error rate

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Robust Grayscale Watermarking Technique Based on Face Detection 23

Robust Grayscale Watermarking Technique Based on Face Detect...

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23nd signal processing and Communications applications Conference (SIU)

作者： Laur, Lauri Daneshmand, Morteza Agoyi, Mary Anbarjafari, Gholamreza Univ Tartu Inst Comp Sci Liivi 2 EE-50409 Tartu Estonia Univ Tartu Inst Technol iCV Grp IMS Lab EE-50411 Tartu Estonia Cyprus Int Univ Fac Engn Lefkosa KKTC Turkey

ISBN: (纸本)9781467373869

Due to increase of usage of digital media distributed over the Internet, concerns about security and piracy have emerged. The amount of digital media reproduction has brought a need for content watermarking. In this paper robust grayscale watermarking technique based on face detection is proposed. Face detection algorithm is used to find a face on host image and this part of image is transformed into frequency domain using Discrete wavelet Transform. Chirp z-transform is applied on low-frequency subband from previous step and LU decomposition is used on the outcome. Diagonal matrix from LU decomposition is further decomposed using Singular Value Decomposition and watermark is embedded into singular values. Numerous experiments are run on that algorithm and results are compared with novel and state-of-the-art techniques. The results show that proposed method has good imperceptibility and robustness characteristics.

关键词： Chirp Z Transform Discrete wavelet Transform face detection LU decomposition Singular Value Decomposition watermarking

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image Compressive Sensing Based on Blended Basis Functions 3rd

Image Compressive Sensing Based on Blended Basis Functions

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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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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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Moving object segmentation in Daubechies complex wavelet domain

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signal image AND VIDEO processing 2015年第3期9卷 635-650页

作者： Khare, Manish Srivastava, Rajneesh Kumar Khare, Ashish Univ Allahabad Dept Elect & Commun Allahabad 211002 Uttar Pradesh India

Motion segmentation is a crucial step in video analysis and is associated with a number of computer vision applications. This paper introduces a new method for segmentation of moving object which is based on double change detection technique applied on Daubechies complex wavelet coefficients of three consecutive frames. Daubechies complex wavelet transform for segmentation of moving object has been chosen as it is approximate shift invariant and has a better directional selectivity as compared to real valued wavelet transform. Double change detection technique is used to obtain video object plane by inter-frame difference of three consecutive frames. Double change detection technique also provides automatic detection of appearance of new objects. The proposed method does not require any other parameter except Daubechies complex wavelet coefficients. Results of the proposed method for segmentation of moving objects are compared with results of other state-of-the-art methods in terms of visual performance and a number of quantitative performance metrics viz. Misclassification Penalty, Relative Foreground Area Measure, Pixel Classification Based Measure, Normalized Absolute Error, and Percentage of Correct Classification. The proposed method is found to have high degree of segmentation accuracy than the other state-of-the-art methods.

关键词： Double change detection Daubechies complex wavelet transform Moving object segmentation

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