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Gun Detection with Faster R-CNN in x-Ray images 28

Gun Detection with Faster R-CNN in X-Ray Images

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28th signal processing and Communications applications conference, SIU 2020

作者： Karakaya, Ismail Safak, Ilgzn Gztilrk, Orkun Bal, Murat Esin, Yunus Emre Havelsan AS. Ankara Turkey

ISBN: (纸本)9781728172064

In this article, Faster R-CNN object detection algorithm is used for automatic weapon detection in x-Ray bag images obtained from security points. In the proposed method, Faster R-CNN model was fed by creating 2 different 3 band images. In the first one, the 3 bands of the image are equalized to the high energy values obtained from the x-Ray device. In the other one, 1st band is equalized to high values, 2nd band is equalized to low values and the 3rd band is equalized to the difference between high energy and low energy values. Performance values of the two different image generation techniques that fed the deep learning model were compared. In the study, x-Ray dataset provided by HTR Company was used. When the results are evaluated, it is seen that Faster R-CNN method can be used in weapon detection with very high performance as a deep learning model and the method of creating images that feed the model has an effect on object detection performance. © 2020 IEEE.

关键词： Object detection

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A Mosaic Method for Multichannel Sequence Starry images via Multiscale Edge-preserving Spatio-Temporal Context Filtering

A Mosaic Method for Multichannel Sequence Starry Images via ...

引用

International conference on Optical Instruments and Technology - Optoelectronic Imaging/Spectroscopy and signal processing Technology

作者： Yang, Zhijia Liu, xiaozheng Mao, Yuxuan Zhang, Tinghua Gao, Kun Beijing Inst Technol Minist Educ China Key Lab Photoelect Imaging Technol & Syst Beijing 100081 Peoples R China

ISBN: (纸本)9781510636552

Astronomical observation and spatial target surveillance applications often require mosaic processing of starry images acquired by multiple image sensors to expand the Fields of View (FOV) or improve the resolutions. Due to the low SNR (signal-to-Noise Ratio), lack of star point texture information and vulnerability of atmospheric turbulence of the starry image properties, traditional mosaic methods are prone to failures during feature point extraction. In this paper, Spatio-Temporal Context (STC) filtering is introduced as the preprocessing procedure to suppress the background interferences. We have improved the classical STC filtering and expands it into multi-scale space combining with Rolling-Guidance Filtering Algorithm (RGFA). Making full use of the fine edge-preserving feature of RGFA, the time-variant or spatial-variant interference and noise in the background, such as glimmer stars, night clouds, sensor response noise, etc, are suppressed while the profiles of the target star points are enhanced and easy to extract their centroids. Then, we produced the feature description of the star-point sets via threshold segmentation and morphological algorithms based on geometric invariant cost function for the input image pairs to be stitched. After Random Sample Consensus (RANSAC) processing, the mismatched feature point pairs in the star-point sets are excluded. The subsequent procedures of the registration parameter calculation, image fusion and parallax correction processing are adopted to complete the mosaic processing. The results of digital simulation and practical processing show that the proposed method for the multichannel sequence starry images with the low SNR and complex backgrounds can extract feature points more precisely and more robustly comparing with the traditional methods. So, it is suitable for the large FOV spatial observation or surveillance applications.

关键词： image Sequence Mosaic Star Background Spatio-Temporal Context Edge-Preserving Filter Geometric Invariant Features

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Pavement images Denoising with Cracks Detection and Classification Using 2D Discrete Wavelet Transform and Savitzky-Golay Filters

Pavement Images Denoising with Cracks Detection and Classifi...

引用

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

作者： Hamici, Zoubir Obaidat, Turki I. Al-Suleiman Al Zaytonnah Univ Jordan Elect Engn Dept Amman Jordan Al Zaytonnah Univ Jordan Civil Engn Dept Amman Jordan

ISBN: (纸本)9781728133775

image processing has gained an increased usage and impact in modern pavement networks automatic distress severity classification (DSC). DSC defines priorities and maintenance resources optimum allocation in order to achieve a cost-effective rehabilitation process. This paper presents a novel computer vision algorithm having the ability to process, isolate and evaluate the distress severity level of a pavement. A pavement color image is converted to grayscale and then processed for image denoising of the granularity and complex texture that represent and artifact in cracks edge detection. The processing is achieved by a 2D dual-tree double density wavelet transform filter banks that significantly reduces the granularity noise while preserving the pavement cracks for edge detection. The 2D wavelet FIR filters perform analysis, soft thresholding then a synthesis of the image. The second step is then an edge detection process followed by morphological filtering and labeled components size-histogram filter to isolate false edges as residuals of denoising. A final step is performed by two Savitzky-Golay filters for the detection of longitudinal and transverse alligator cracks projections. A weighted score function with multiple parameters is used for DSC.

关键词： Computer vision wavelets filter-banks Denoising Savitzky-Golay Filters Pavement Cracks

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Colour image Denoising using Curvelets and Scale Dependent Shrinkage

Colour Image Denoising using Curvelets and Scale Dependent S...

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IEEE International conference on Systems, Man, and Cybernetics (SMC)

作者： Kadri, Oussama Baarir, Zine-Eddine Schaefer, Gerald Korovin, Iakov Mohammed Khider Univ Elect Engn Dept Biskra Algeria Loughborough Univ Dept Comp Sci Loughborough Leics England Southern Fed Univ Taganrog Russia

ISBN: (纸本)9781728185262

With the widespread use of image processing and computer vision applications, effective denoising methods are highly sought after, prompting the development of a variety of algorithms under different assumptions on noise and signal properties. However, most of these techniques are developed to deal with grayscale images, and are typically extended to colour images by processing each RGB channel separately. In this paper, we extend the curvelet power shrinkage algorithm, introduced previously for grayscale images, to colour image denoising, by applying the proposed method in the luminance/opponent-colour YCbCr colour space to take into consideration image inter-channel dependencies. The performance of the proposed algorithm on colour images corrupted by additive white Gaussian noise is evaluated in terms of both objective and subjective measures, and the obtained results show our method to be competitive to other methods including curvelet domain hard thresholding and MSt-SVD.

关键词： Shrinkage

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Cover image Selection for Embedding Based on Different Criteria

Cover Image Selection for Embedding Based on Different Crite...

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conference on Mobile Multimedia/image processing, Security, and applications

作者： Rashid, Rasber Dhahir Koya Univ Dept Software Engn Fac Engn KOY-45 Koya Kurdistan Regio Iraq

ISBN: (纸本)9781510635760;9781510635753

The main goal of image steganography techniques is to maximize embedding rate while minimizing the change of the cover image after embedding. Much work has been done on how sender would embed the secret message in the cover image (i.e. embedding techniques) but there is a few works focus on how the senders choose the cover images. One advantage of image steganography is that the cover image only acts as a carrier for the message and the embedder (sender) has the freedom to choose a cover image amongst a set of cover images those results in the least detectable stego image. The way of choosing the cover image is important and since it is available to the sender both the cover and stego images, then the senders are able to measure the embedding artifacts directly. Thus, we are interested in measures which are able to quantify such artifacts. We can use the cover-stego based on measures which we have employed in our work to select best cover image among set of images. The measures used are (1) Number of Modifications to the cover image could be thought as the most intuitive. The smaller the number of changes made the less detectable the resulting stego image should be, (2) Peak signal to Noise Ratio (PSNR) which is obtained from the cover-stego image pairs where higher PSNR values are assumed to be indicative of lesser delectability, or (3) Based on the robustness to the steganalysis techniques. For the experiments we used a dataset of gray scale images with size of 512x512 resolutions as a cover image with different secret message size from 0.2 to 1.0 bits per pixel.

关键词： image Steganography Steganalysis Cover selection

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MULTI-SCALE FEATURE PYRAMIDS FOR WEAKLY SUPERVISED THORACIC DISEASE LOCALIZATION

MULTI-SCALE FEATURE PYRAMIDS FOR WEAKLY SUPERVISED THORACIC ...

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IEEE International conference on image processing (ICIP)

作者： Ma, Yang Ma, Andy J. Pan, Youngsun Chen, xi Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Beijing Peoples R China

ISBN: (纸本)9781728163956

Automatic localization of thoracic diseases has a wide range of applications which can assist radiologists for more efficient and better diagnosis. However, it is still a challenging task to locate the diseases accurately since strong location annotation may not be available and different thoracic diseases may vary in size greatly. In this paper, we propose a novel multi-scale feature pyramids model for weakly supervised disease localization on chest x-ray images. Our model leverages the multi-scale feature maps to learn location representation of lesions by fusing heatmaps generated from all these feature maps. Instead of linear combination of heatmaps, we reconfigure multi-scale feature maps with an Feature Pyramids Network (fpn) structure first. The FPN we conducted is a nonlinear combination of feature hierarchy, adding highly-nonlinear patterns in feature maps, enriching the representation space of heatmaps. Experiments on the Chestxray14 dataset show that the proposed method can significantly improve the localization performance of small-sized diseases (nodules and masses) with competitive localization performance of largesized diseases (cardiomegaly and pneumonia). Averagely, our method is superior to the state-of-the-art weakly supervised localization algorithms on the dataset, Chestxray14.

关键词： Weakly Supervised Localization Multi-Scale Feature Pyramids Chest x-ray image

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An efficient algorithm of total variation regularization in the two-dimensional case 43

An efficient algorithm of total variation regularization in ...

引用

conference on applications of Digital image processing xLIII

作者： Makovetskii, Artyom Voronin, Sergei Kober, Vitaly Voronin, Aleksei Chelyabinsk State Univ Dept Math Chelyabinsk Russia CICESE Dept Comp Sci Ensenada 22860 Baja California Mexico

ISBN: (数字)9781510638273

ISBN: (纸本)9781510638273

Denoising has numerous applications in communications, control, machine learning, and many other fields of engineering and science. Total variation (TV) regularization is a widely used technique for signal and image restoration. There are two types of TV regularization problem: anisotropic TV and isotropic TV. One of the key difficulties in the TV-based image denoising problem is the nonsmoothness of the TV norms. There are known exact solutions methods for 1D TV regularization problem. Strong and Chan derived exact solutions to TV regularization problem for one-dimensional case. They obtained the exact solutions when the original noise-free function, noise and the regularization parameter are subject to special constraints. Davies and Kovac considered the problem as non-parametric regression with emphasis on controlling the number of local extrema, and in particular consider the run and taut string methods. Condat proposed a direct fast algorithm for searching the exact solutions to the one-dimensional TV regularization problem for discrete functions. In the 2D case, some methods are used to approximate exact solutions to the TV regularization problem. In this presentation, we propose a new approximation method for 2D TV regularization problem based on the fast exact 1D TV approach. Computer simulation results of are presented to illustrate the performance of the proposed algorithm for the image restoration.

关键词： image restoration total variation denoising anisotropic total variation

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Research on high-resolution quantum medical image technology for COVID-19 28

Research on high-resolution quantum medical image technology...

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conference on Infrared Remote Sensing and Instrumentation xxVIII held at SPIE Optical Engineering + applications conference

作者： Bi, Siwen Chen, Hao Liu, Jiaying Shenzhen Jingwan Quantum Remote Sensing Sci & Te Shenzhen 518108 Guangdong Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100101 Peoples R China

ISBN: (纸本)9781510638112;9781510638105

Considering the disadvantages of low definition, low image contrast and brightness, inconspicuous details, which make image analysis more complicate, we developed quantum medical image processing system software. It includes quantum image enhancement module, quantum image denoising module and quantum image segmentation module, which respectively study the quantum enhancement algorithm, quantum denoising algorithm and quantum segmentation algorithm. Quantum denoising algorithm firstly add noise to the image and then carries out a logarithmic transfo nation and a double density dual-tree complex wavelet transfoun on the noise-added image, later a denoising of the wavelet coefficients based on Bayesian theory, and the Maxaposterior (MAP) is perfouned to estimate the variance of the double-tree complex wavelet. Finally the denoised image is obtained from the inverse transfoun of the dual-tree complex wavelet. Quantum enhancement algorithm improves image quality through quantum inspired and unsharp masking. First, a quantum enhancement operator based on quantum superposition state theory was constructed to enhance image contrast ratio, then the processed image quality was improved by unsharp masking. Quantum segmentation algorithm searches automatically the optimal threshold through improved quantum genetic algorithm, which reduces the complexity of program and improves the infounation entropy compared with traditional methods. More image infounation can be retained and more ideal segmentation effect is realized through the algorithm. Experiment show that the peak signal-to-noise ratio (PSNR) for the quantum algorithm is improved by over 2dB and the edge retention index (EPI) is 0.1 higher than that for common methods, image info nation entropy and clarity index are significantly improved.

关键词： Quantum algorithm medical image processing peak signal-to-noise ratio (PSNR) edge-preserving index (EPI) information entropy

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Exploring the MSER-based hyperspectral remote sensing image registration 26

Exploring the MSER-based hyperspectral remote sensing image ...

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conference on image and signal processing for Remote Sensing xxVI

作者： Ordonez, Alvaro Heras, Dora B. Arguello, Francisco Univ Santiago Compostela Ctr Singular Invest Tecnoloxias Intelixentes CiTI Santiago De Compostela 15782 Spain Univ Santiago Compostela Dept Elect & Comp Santiago De Compostela 15782 Spain

ISBN: (纸本)9781510638808

image registration is an essential preprocessing task in many applications of hyperspectral images capturing the Earth surface. Maximally Stable Extremal Regions (MSER) is a feature-based method for image registration which extracts regions by thresholding the image at different grey levels. Its invariance to affine transformations makes it ideal for image registration. This method is usually employed in text detection and recognition as well as in the medical domain. Hyperspectral images contain spectral information that can be used for improving the image alignment. This article presents a first approach to a hyperspectral remote sensing image registration method based on MSER that efficiently exploits the information contained in the different spectral bands. The experimental results over four hyperspectral images show that the proposed method is promising as it achieves a higher number of correct registration cases than other feature-based methods.

关键词： image registration remote sensing hyperspectral MSER

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Medical image Quality Assessment Method based on Residual Learning 5

Medical Image Quality Assessment Method based on Residual Le...

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5th IEEE International conference on signal and image processing, ICSIP 2020

作者： Cui, xiangyue Shi, Weili Zhang, Ke Miao, Yu Jiang, Zhengang Changchun University of Science and Technology School of Computer Science and Technology Changchun China

ISBN: (纸本)9781728168968

In clinical applications, the task of medical image quality assessment is done by radiologists and the existence of subjective factors leads to unstable the accuracy of the assessment. Deep learning methods bring the opportunity for objective assessment image quality. In order to avoid the overfitting problem of deep learning methods in small-sample data assessment, this paper proposes a medical image quality assessment method based on residual learning. This method adds a residual module to the traditional quality assessment network, so that the problem of overfitting can be effectively alleviated in the process of deepening the network structure. At the same time, this paper initially establishes a chest x-ray image quality assessment dataset. In the experiment, the correlation coefficients SRCC and PLCC of this method reaches 0.9692 and 0.9589, respectively, and the mean square error is 0.6697. Compared with the six assessment methods, three indicators are optimal. The method proposed in this paper can meet the clinical assessment needs of medical image quality, and the effectiveness of the method has been fully verified. © 2020 IEEE.

关键词： image quality

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