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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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Non-consistent cell-average multiresolution operators with application to image processing

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APPLIED MATHEMATICS AND COMPUTATION 2016年 272卷 208-222页

作者： Arandiga, Francesc Yanez, Dionisio F. Univ Valencia Dept Matemat Aplicada E-46003 Valencia Spain Univ Catolica Valencia Valencia Spain Univ Catolica Valencia Dept Matemat CC NN CC SS Aplicadas Educ Valencia Spain

In recent years different techniques to process signal and image have been designed and developed. In particular, multiresolution representations of data have been studied and used successfully for several applications such as compression, denoising or inpainting. A general framework about multiresolution representation has been presented by Harten (1996) [20]. Harten's schemes are based on two operators: decimation, P. and prediction, P. that satisfy the consistency property DP _ I, where I is the identity operator. Recently, some new classes of multiresolution operators have been designed using learning statistical tools and weighted local polynomial regression methods obtaining filters that do not satisfy this condition. We show some proposals to solve the consistency problem and analyze its properties. Moreover, some numerical experiments comparing our methods with the classical methods are presented. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Generalized wavelets Consistency Subdivision schemes Statistical multiresolution image compression

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OUHANDS database for hand detection and pose recognition 6

OUHANDS database for hand detection and pose recognition

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6th International conference on image processing Theory, Tools and applications (IPTA)

作者： Matilainen, Matti Sangi, Pekka Holappa, Jukka Silven, Olli Ctr Machine Vis & Signal Anal CMVS Oulu Oulu Finland

In this paper we propose a publicly available static hand pose database called OUHANDS and protocols for training and evaluating hand pose classification and hand detection methods. A comparison between the OUHANDS da... 详细信息

ISBN: (纸本)9781467389105

关键词： Database Computer vision Hand pose estimation Hand detection Pattern recognition

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High Speed, Low-Complexity image Coding for IP-Transport with JPEG xS 39

High Speed, Low-Complexity Image Coding for IP-Transport wit...

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conference on applications of Digital image processing xxxIx

作者： Richter, Thomas Foessel, Siegfried Keinert, Joachim Descampe, Antonin Univ Stuttgart IZUS TIK Comp Ctr D-70550 Stuttgart Germany Fraunhofer Inst Integrated Circuits IIS D-91058 Erlangen Germany IntoPIX B-1435 Mont St Guibert Belgium

ISBN: (数字)9781510603349

ISBN: (纸本)9781510603332;9781510603349

The JPEG committee (formally, ISO/IEC SC 29 WG 01) is currently investigating a new work item on near lossless low complexity coding for IP streaming of moving images. This article discusses the requirements and use cases of this work item, gives some insight into the anchors that are used for the purpose of standardization, and provides a short update on the current proposals that reached the committee.

关键词： wavelets Video compression Video Computer programming image compression Standards development Quantization Field programmable gate arrays Video processing Visualization

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A novel structured dictionary for fast processing of 3D medical images, with application to computed tomography restoration and denoising

A novel structured dictionary for fast processing of 3D medi...

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conference on Medical Imaging - image processing

作者： Karimi, Davood Ward, Rabab K. Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada

ISBN: (纸本)9781510600195

Sparse representation of signals in learned overcomplete dictionaries has proven to be a powerful tool with applications in denoising, restoration, compression, reconstruction, and more. Recent research has shown that learned overcomplete dictionaries can lead to better results than analytical dictionaries such as wavelets in almost all image processing applications. However, a major disadvantage of these dictionaries is that their learning and usage is very computationally intensive. In particular, finding the sparse representation of a signal in these dictionaries requires solving an optimization problem that leads to very long computational times, especially in 3D image processing. Moreover, the sparse representation found by greedy algorithms is usually sub-optimal. In this paper, we propose a novel two-level dictionary structure that improves the performance and the speed of standard greedy sparse coding methods. The first (i.e., the top) level in our dictionary is a fixed orthonormal basis, whereas the second level includes the atoms that are learned from the training data. We explain how such a dictionary can be learned from the training data and how the sparse representation of a new signal in this dictionary can be computed. As an application, we use the proposed dictionary structure for removing the noise and artifacts in 3D computed tomography (CT) images. Our experiments with real CT images show that the proposed method achieves results that are comparable with standard dictionary-based methods while substantially reducing the computational time.

关键词： dictionary learning computed tomography CT denoising restoration structured dictionary sparsity

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Prototype-based Class-Specific Nonlinear Subspace Learning for Large-Scale Face Verification 6

Prototype-based Class-Specific Nonlinear Subspace Learning f...

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6th International conference on image processing Theory, Tools and applications (IPTA)

作者： Iosifidis, Alexandros Gabbouj, Moncef Tampere Univ Technol Dept Signal Proc Tampere Finland

ISBN: (纸本)9781467389105

In this paper, we describe a face verification method which is based on non-linear class-specific discriminant subspace learning. We follow the Kernel Spectral Regression approach to this end and employ a prototype-based approximate kernel regression scheme in order to scale the method for large-scale nonlinear discriminant learning. Experiments on two publicly available facial image databases show the effectiveness of the proposed approach, since it scales well with the data size and outperforms related approaches.

关键词： Class-Specific Discriminant Analysis Prototype-based Approximation Nonlinear Subspace Learning

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Artifact Removal for Physiological signals via wavelets 8

Artifact Removal for Physiological Signals via Wavelets

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8th International conference on Digital image processing (ICDIP)

作者： Lin, En-Bing Abayomi, Oluremi Dahal, Keshab Davis, Patrick Mdziniso, Nonhle Channon Cent Michigan Univ Dept Math Mt Pleasant MI 48859 USA

ISBN: (纸本)9781510605039;9781510605046

In order to analyze brain activity signals, it is important to remove any artifact of the obtained data so that we can further provide diagnosis of possible symptoms. There are many different ways to do denoising of the given signals. In this paper, we test several biosignals and obtain an optimal ways to denoise the data and perform time frequency analysis of an EEG signal.

关键词： artifact removal physiological signal EEG fNIRS wavelet transform multiresolution analysis

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Graphical stochastic models for tracking applications with variational message passing inference 6

Graphical stochastic models for tracking applications with v...

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6th International conference on image processing Theory, Tools and applications (IPTA)

作者： Trusheim, Felix Condurache, Alexandru Mertins, Alfred Robert Bosch GmbH Stuttgart Germany Univ Lubeck ISIP Lubeck Germany

ISBN: (纸本)9781467389105

In this paper we present a novel, highly-adoptable, state-estimation filter based on the framework of graphical stochastical models and variational message passing inference. We evaluate our method on both real and simulated data for tracking applications. Our experimental results show that the proposed approach offers qualitative and computational advantages over established filter methods in practical situations, where the noise within a process is not simply a Gaussian noise, but rather described by a more complex distribution.

关键词： Object tracking image-based object tracking Non-Gaussian noise Variational inference Particle filter KITTI

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A Vector Quantization Based k-NN Approach for Large-Scale image Classification 6

A Vector Quantization Based k-NN Approach for Large-Scale Im...

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6th International conference on image processing Theory, Tools and applications (IPTA)

作者： Ozan, Ezgi Can Riabchenko, Ekaterina Kiranyaz, Serkan Gabbouj, Moncef Tampere Univ Technol Dept Signal Proc Tampere Finland Qatar Univ Coll Engn Dept Elect Engn Doha Qatar

ISBN: (纸本)9781467389105

The k-nearest-neighbour classifiers (k-NN) have been one of the simplest yet most effective approaches to instance based learning problem for image classification. However, with the growth of the size of image datasets and the number of dimensions of image descriptors, popularity of k-NNs has decreased due to their significant storage requirements and computational costs. In this paper we propose a vector quantization (VQ) based k-NN classifier, which has improved efficiency for both storage requirements and computational costs. We test the proposed method on publicly available large scale image datasets and show that the proposed method performs comparable to traditional k-NN with significantly better complexity and storage requirements.

关键词： k-NN Classifier Vector Quantization Large-Scale image Classification

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Median Based Pixel Selection for Partial image Encryption 6

Median Based Pixel Selection for Partial Image Encryption

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6th International conference on image processing Theory, Tools and applications (IPTA)

作者： Goel, Anish Chaudhari, Kaustubh Shri Ramdeobaba Coll Engn & Management Elect & Commun Engn Dept Nagpur Maharashtra India

ISBN: (纸本)9781467389105

Communication between devices and data through networks have been increasing drastically in the past few decades. Encryption of data provides high level of security. This paper presents a median based technique for selective encryption where partial data of image is encrypted based on pixel values. An encrypted mask of the image is also appended with the image that specifies the pixels that are encrypted and the ones that are not. The technique results in saving the encryption data blocks to the Advanced Encryption Standard (AES) used for encrypting the data. The proposed technique shows significant reduction in the amount of data encrypted with a little overhead of the mask. Performance with different values of percentage deviation of median and block sizes are presented along with results for Entropy, Mean Square Error and Peak signal to Noise Ratio that depict substantial level of security in partially encrypted image.

关键词： Pixel Selective Encryption Median Deviation

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