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Comparison of Neural Network Classifiers for Automatic Target Recognition 28

Comparison of Neural Network Classifiers for Automatic Targe...

引用

conference on signal processing, Sensor/Information Fusion, and Target Recognition xxVIII

作者： Carlotto, Mark Nebrich, Mark Ramirez, David Gen Dynam Mission Syst Stokesdale NC 27357 USA

ISBN: (纸本)9781510627024

We consider a challenge problem involving the automatic detection of large commercial vehicles such as trucks, buses, and tractor-trailers in Quickbird EO pan imagery. Three target classifiers are evaluated: a "bagged" perceptron algorithm (BPA) that uses an ensemble method known as bootstrap aggregation to increase classification performance, a convolutional neural network (CNN) implemented using the MobileNet architecture in TensorFlow, and a memory-based classifier (MBC), which also uses bagging to increase performance. As expected, the CNN significantly outperformed the BPA. Surprisingly, the performance of the MBC was only slightly below that of the CNN. We discuss these results and their implications for this and other similar applications.

关键词： Evolutionary algorithms image segmentation 3D modeling Buildings Object recognition

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Super Resolution of DS-2 Satellite imagery using Deep Convolutional Neural Network 25

Super Resolution of DS-2 Satellite Imagery using Deep Convol...

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conference on image and signal processing for Remote Sensing xxV

作者： Aburaed, Nour Panthakkan, Alavi Almansoori, Saeed Al-Ahmad, Hussain Univ Dubai Dubai U Arab Emirates Mohammed bin Rashid Space Ctr Dubai U Arab Emirates

ISBN: (数字)9781510630147

ISBN: (纸本)9781510630147

Nowadays, Satellite images are used for various analysis, including building detection and road extraction, which are directly beneficial to governmental applications, such as urbanization and monitoring the environment. Spatial resolution is an element of crucial impact on the usage of remote sensing imagery. High spatial resolution means satellite images provide more detailed information. To improve the spatial resolution at the sensor level, many factors are ought to be taken into consideration, such as the manufacturing process. Moreover, once the satellite is launched, no further action can be taken from the perspective of hardware. Therefore, a more practical solution to improve the resolution of a satellite image is to use Single image Super Resolution (SISR) techniques. This research proposal deals with the re-design, implementation, and evaluation of SISR technique using Deep Convolutional Neural Network with Skip Connections and Network in Network (DCSCN) for enlarging multispectral remote sensing images captured by DubaiSat-2 (DS-2) and estimating the missing high frequency details. The goal is to achieve high performance in terms of quality, and to test whether training the network using luminance channel only, which is extracted from YCbCr domain, can achieve high quality results. For this purpose, DCSCN is trained, evaluated, and tested using a dataset collected from DS-2. A single low resolution DS-2 image is used to construct its high resolution version by training the model from scratch and fine-tuning its hyper-parameters to produce optimal results. The performance is evaluated using various quality indices, such as Structural Similarity Index Measurement (SSIM), Peak signal-to-Noise Ratio (PSNR), and Wavelet domain signal-to-Noise Ratio (WSNR). The performance is compared to other state-of-the-art methods, such as Bil-inear, Bi-cubic, and Lanczos interpolation.

关键词： image Reconstruction Super Resolution Deep Learning Multispectral Peak signal to Noise Ratio (PSNR) Wavelet domain signal to Noise Ratio (WSNR) Structure Similarity Index Measurement (SSIM)

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Multidimensional images processing in electrocardiography using Bragg diffraction technologies 9

Multidimensional images processing in electrocardiography us...

引用

conference on Medical Laser applications and Laser-Tissue Interactions Ix

作者： Zaichenko, K. V. Gurevich, B. S. RAS Inst Analyt Instrumentat 26 Rizhsky Pr St Petersburg 190103 Russia State Univ Aerosp Instrumentat Bolshaya Morskaya Str 67 St Petersburg 90000 Russia St Petersburg Electrotech Univ 5 Prof Popova St Petersburg 197376 Russia

ISBN: (数字)9781510628526

ISBN: (纸本)9781510628526

Multidimensional images are often used for the most convenient representation of the processes carrying out in the studied object. However, sometimes, especially in such areas as medical diagnostics or related ones, it is required to estimate quickly simultaneously several aspects of the observed process, and certain part of multidimensional image must be observed in details without influence of several excessive signal distribution along some dimensions. The acousto-optic method based on Bragg diffraction has been proposed for this problem solution which provides fast and automatic (controlled by software) way of multidimensional switching to its selective part or several parts. Especially this method is convenient in electrocardiography where signal processing produces such pictures as 3D mapping images or signal wavelet transform images. Information losses which appear due to this method application, have been estimated, and the ways of the method optimization from the point of view of maximum signal-to-noise ratio providing, have been proposed.

关键词： Bragg diffraction acousto-optic device electrocardiography 3D mapping information losses bioelectric signals wavelet processing signal-to-noise ratio

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image Segmentation Based Privacy-Preserving Human Action Recognition for Anomaly Detection

Image Segmentation Based Privacy-Preserving Human Action Rec...

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IEEE International conference on Acoustics, Speech and signal processing

作者： Jiawei Yan Federico Angelini Syed Mohsen Naqvi Intelligent Sensing and Communications Research Group Newcastle University UK

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Human Action Recognition and Anomaly Detection significantly improved automatic video analysis, assisted living, and video-based surveillance. The focus of this work is on those applications where privacy protection is required, such as surveillance and assisted living. RGB video data is the most common source for human action recognition. However, RGB data also contains privacy-related data, such as the identity of the target. In this paper, we prove that human action recognition accuracy mostly depends on contextual data, rather than on privacy-related data. Therefore, human target data can be occluded by using an image segmentation mask. The proposed method achieves almost similar accuracy in comparison with the privacy case and provides the platform for privacy-preserving anomaly detection. Simulations are performed on the two popular datasets for human action recognition, i.e. UCF101 and HMDB51.

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ADRN: Attention-Based Deep Residual Network for Hyperspectral image Denoising

ADRN: Attention-Based Deep Residual Network for Hyperspectra...

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IEEE International conference on Acoustics, Speech and signal processing

作者： Yongsen Zhao Deming Zhai Junjun Jiang xianming Liu School of Computer Science and Technology Harbin Institute of Technology Harbin China

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Hyperspectral image (HSI) denoising is of crucial importance for many subsequent applications, such as HSI classification and interpretation. In this paper, we propose an attention-based deep residual network to directly learn a mapping from noisy HSI to the clean one. To jointly utilize the spatial-spectral information, the current band and its K adjacent bands are simultaneously exploited as the input. Then, we adopt convolution layer with different filter sizes to fuse the multi-scale feature, and use shortcut connection to incorporate the multi-level information for better noise removal. In addition, the channel attention mechanism is employed to make the network concentrate on the most relevant auxiliary information and features that are beneficial to the de-noising process best. To ease the training procedure, we reconstruct the output through a residual mode rather than a straightforward prediction. Experimental results demonstrate that our proposed ADRN scheme outperforms the state-of-the-art methods both in quantitative and visual evaluations.

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Lossy DCT-based compression of remote sensing images with providing a desired visual quality 25

Lossy DCT-based compression of remote sensing images with pr...

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conference on image and signal processing for Remote Sensing xxV

作者： Krivenko, Sergey S. Abramov, Sergey K. Lukin, Vladimir V. Vozel, Benoit Chehdi, Kacem Natl Aerosp Univ UA-61070 Kharkov Ukraine Univ Rennes CNRS IETR UMR 6164 F-22305 Lannion France

ISBN: (数字)9781510630147

ISBN: (纸本)9781510630147

Modern remote sensing (RS) systems produce a huge amount of data that should be passed to potential users from sensors or saved. Then, compression is an operation that is extremely useful where lossy compression has found many applications. A requirement to it is not to loose useful information contained in RS data and to provide a rather high compression ratio (CR). This has to be done in automatic manner and quickly enough. One possible approach to ensure minimal or appropriate loss of useful information is to provide a desired visual quality of compressed images where introduced distortions are invisible. In this paper, we show how this can be done for coders based on discrete cosine transform (DCT) that employ either uniform or non-uniform quantization of DCT coefficients. For multichannel images that contain sub-band images with different dynamic range, it is also proposed to carry out preliminary normalization. Additionally, compression performance can be improved if sub-band images are compressed in groups. Then, either introduced distortions are smaller for a given CR or a larger CR is provided for a given level of compressed data quality. Examples for real-life data are presented.

关键词： image compression Visualization Remote sensing Chromium Visual compression image quality Quantization image classification Data compression Sensors

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Cra: A Generic Compression Ratio Adapter for End-To-End Data-Driven image Compressive Sensing Reconstruction Frameworks

Cra: A Generic Compression Ratio Adapter for End-To-End Data...

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IEEE International conference on Acoustics, Speech and signal processing

作者： Zhikang Zhang Kai xu Fengbo Ren Parallel Systems and Computing Laboratory Arizona State University

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

End-to-end data-driven image compressive sensing reconstruction (EDCSR) frameworks achieve state-of-the-art reconstruction performance in terms of reconstruction speed and accuracy. However, due to their end-to-end nature, existing EDCSR frameworks can not adapt to a variable compression ratio (CR). For applications that desire a variable CR, existing EDCSR frameworks must be trained from scratch at each CR, which is computationally costly and time-consuming. This paper presents a generic compression ratio adapter (CRA) framework that addresses the variable compression ratio (CR) problem for existing EDCSR frameworks with no modification to given reconstruction models nor enormous rounds of training needed. CRA exploits an initial reconstruction network to generate an initial estimate of reconstruction results based on a small portion of the acquired measurements. Subsequently, CRA approximates full measurements for the main reconstruction network by complementing the sensed measurements with resensed initial estimate. Our experiments based on two public image datasets (CIFAR10 and Set5) show that CRA provides an average of 13.02 dB and 5.38 dB PSNR improvement across the CRs from 5 to 30 over a naive zero-padding approach and the AdaptiveNN approach(a prior work), respectively. CRA addresses the fixed-CR limitation of existing EDCSR frameworks and makes them suitable for resource-constrained compressive sensing applications.

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Discrete fourier analysis and wavelets : applications to signal and image processing / Second edi...

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2018年

作者： Broughton S. Allen

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An Evaluation of a CNN-Based Parking Detection System with Webcams

An Evaluation of a CNN-Based Parking Detection System with W...

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Asia-Pacific signal and Information processing Association Annual Summit and conference (APSIPA)

作者： Takuto Fukusaki Hiroshi Tsutsui Takeo Ohgane Graduate School of Information Science and Technology Hokkaido University

ISBN: (数字)9789881476883

ISBN: (纸本)9781728181301

In this paper, we evaluate an image processing based parking detection system utilizing convolutional neural networks (CNNs). At present, usage surveys on outdoor parking lots are often performed manually, which may cost a lot. By using commodity webcams and image processing, it may be possible to deploy a parking detection system at a quite low cost. Some parking detection methods utilize HOG and SIFT feature values, and temporal changes of RGB and HSV values. However, these approaches have difficulties due to the influence of ambient light. To tackle this issue, we propose a parking detection method utilizing CNNs, which have high potential in classification and object recognition applications. By training CNNs with different ambient light and lighting conditions, it is expected that the proposed approach can overcome the issue related to the ambient light changes. We evaluate the accuracy of the proposed parking detection system comparing with a method without machine learning, that is, a color-based approach. Experimental results show that the proposed approach can achieve 99 % accuracy for parking and vacancy detection, resulting in an F value of 0.996.

关键词： Webcams image color analysis Automobiles Interpolation image resolution Training Testing

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Classification of breast abnormalities in digital mammography using phase-based features 42

Classification of breast abnormalities in digital mammograph...

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conference on applications of Digital image processing xLII

作者： Diaz-Escobar, Julia Kober, Vitaly CICESE Dept Comp Sci Ensenada 22860 BC Mexico Chelyabinsk State Univ Dept Math Chelyabinsk Russia

ISBN: (数字)9781510629684

ISBN: (纸本)9781510629684

Breast cancer is one of the principal causes of death for women in the world. Invasive breast cancer develops in about one in eight women in the United States during her lifetime. Digital mammography is a common technique for early detection of the breast cancer. However, only 84% of breast cancers are detected by interpreting radiologists. Computer Aided Detection (CAD) is a technology designed to help radiologists and to decrease observational errors. Actually, for every true-positive cancer detected by the CAD there are more false predictions, which have to be ignored by radiologists. In this work, a CAD method for detection and classification of breast abnormalities is proposed. The proposed method is based on the local energy and phase congruency approach and a supervised machine learning classifier. Experimental results are presented using digital mammography dataset and evaluated under different performance metrics.

关键词： CAD breast cancer phase features scale-space monogenic signal

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