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Deep learning-based Object Detection in Underwater communications System

Deep learning-based Object Detection in Underwater Communica...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International conference on

作者： M. Siva Sangari K. Thangaraj U. Vanitha N. Srikanth J. Sathyamoorthy K. Renu Department of CSE KPR Institute of Engineering and Technology Coimbatore India Department of Information Technology Sona College of Technology Salem India Department of ECE Sri Krishna College of Engineering and Technology Coimbatore India Department of Electronics and Communication Technology SASI Institute of Technology and Engineering Tadepalligudem Andhra Pradesh India Department of CSE Bannari Amman Institute of Technology Erode India Department of EECE GST GITAM Deemed to be University Visakhapatnam -45 A.P India

Being at the nexus of robotics and ocean engineering, underwater robots have been a developing research area. They can be used for deep sea infrastructure inspections, oceanographic mapping, and environmental monitoring. Autonomous navigation skills are essential for doing these activities successfully, especially given the poor communication conditions in underwater locations. Autonomous navigation technologies, such as path planning and tracking, have been one of the fascinating but difficult issues in the field of study due to the extremely dynamic and three-dimensional settings. Due to their short detection ranges and poor visibility, cameras have not received much attention as an underwater sensor. However, using visual data from cameras is still a popular technique for underwater sensing, and it works particularly well for close-range detections. In this study, the enhancement of underwater vision is achieved by combining the max-RGB and shades of grey methods. Then, to solve the problem of poorly illuminated underwater images, a technique known as RCNN (Region-based Convolutional Neural Network) is proposed. This procedure tells the mapping relationship how to create the illumination map. Following image processing, an RCNN strategy for underwater detection and classification is recommended. Two improved strategies are then used to change the RCNN structure in accordance with the properties of underwater vision. In order to deal with the challenges of object tracking and detection in underwater communication, a correlation filter tracking algorithm (CFTA) method was created. The properties of the invariant moment and area were looked at after the object's region had been extracted using a threshold segment and morphological technique. The findings show that the suggested method is effective for underwater target tracking based on RCNN-CFTA in the aquatic environment. Simulated evaluation of these methods' performance demonstrates the potency of the suggested

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A Modified Algorithm of visual Quality Providing in Lossy Compression of Remote Sensing images

A Modified Algorithm of Visual Quality Providing in Lossy Co...

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International conference on Radio Electronics & Info communications (UkrMiCo)

作者： Fangfang Li Sergey Krivenko Vladimir Lukin National Aerospace University- KhAI Kharkiv Ukraine

The development of remote sensing technology leads to a huge number of acquired images needed to be stored and/or transferred. Lossy compression is an essential mean to reduce the image size and, thus, to improve the efficiency of storing and transferring. Meanwhile, a certain quality (including visual quality) must be provided in lossy compression since this quality determines the efficiency of further image processing and/or analysis. However, the task to provide a desired quality in lossy image compression quickly and accurately is not trivial. In this paper, we propose a modified lossy compression scheme able to guarantee a desired quality with high accuracy in two steps. The quality is evaluated in terms of both a conventional metric PSNR and a human visual system (HVS)-based metric PSNR-HVS-M. The experiments are conducted for discrete cosine transform (DCT)-based AGU coder for which we reach the max variance of errors equal to 0.124 dB 2 , and the biases less than 0.27 dB. This result proves that the average rate-distortion curve drawn from the remote sensing category better fits remote sensing data. The combination scheme of derivative and tangent gives better parameter predictions and helps reduce the final error.

关键词： Measurement visualization Technological innovation image coding Rate-distortion visual systems Prediction algorithms

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A Study on 4D Light Field Compression Using Multi-focus images and Reference Views

A Study on 4D Light Field Compression Using Multi-focus Imag...

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IEEE visual communications and image processing (VCIP)

作者： Shuho Umebayashi Kazuya Kodama Takayuki Hamamoto Graduate School of Engineering Tokyo University of Science Tokyo Japan National Institute of Informatics Research Organization of Information and Systems Tokyo Japan

ISBN: (纸本)9781728173221

We propose a novel method of light field compression using multi-focus images and reference views. Light fields enable us to observe scenes from various viewpoints. However, it generally consists of 4D enormous data, that are not suitable for storing or transmitting without effective compression at relatively low bit-rates. On the other hand, 4D light fields are essentially redundant because it includes just 3D scene information. While robust 3D scene estimation such as depth recovery from light fields is not so easy, a method of reconstructing light fields directly from 3D information composed of multi-focus images without any scene estimation is successfully derived. Based on the method, we previously proposed light field compression via multi-focus images as effective representation of 3D scenes. Actually, its high performance can be seen only at very low bit-rates, because there exists some degradation of low frequency components and occluded regions on light fields predicted from multi-focus images. In this paper, we study higher quality light field compression by using reference views to improve quality of the prediction from multi-focus images. Our contribution is twofold: first, our improved method can keep good performance of 4D light field compression at a wider range of low bit-rates than the previous one working effectively only for very low bit-rates; second, we clarify how the proposed method can improve its performance continuously by introducing recent video codec such as HEVC and VVC into our compression framework, that does not depend on 3D-SPIHT previously adopted for the corresponding component. We show experimental results by using synthetic and real images, where quality of reconstructed light fields is evaluated by PSNR and SSIM for analyzing characteristics of our novel method well. We notice that it is much superior to light field compression using HEVC directly at low bit-rates regardless of its light field scan order.

关键词： image coding Three-dimensional displays visual communication Time series analysis Estimation Video compression Light fields

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3rd International conference on Data Stream and Mining and processing, DSMP 2020

3rd International Conference on Data Stream and Mining and P...

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3rd International conference on Data Stream and Mining and processing, DSMP 2020

ISBN: (纸本)9783030616557

The proceedings contain 36 papers. The special focus in this conference is on Data Stream and Mining and processing. The topics include: Comparison analysis of clustering quality criteria using inductive methods of objective clustering;assessing the investment risk of virtual it company based on machine learning;methodological support for the management of maintaining financial flows of external tourism in global risky conditions;technology for determining the residual life of metal structures under conditions of combined loading according to acoustic emission measurements;expansion of the capabilities of chromatography-mass spectrometry due to the numerical decomposition of the signal with the mutual superposition of mass spectra;statistical methods for analyzing and processing data components when recognizing visual objects in the space of key point descriptors;sewer pipe defects classification based on deep convolutional network with information-extreme error-correction decision rules;critical modes of photography: Light sensitivity and resolution;multiclass image classification explanation with the complement perturbation images;image enhancement in automatic mode by recursive mean-separate contrast stretching;the principles of organizing the search for an object in an image, tracking an object and the selection of informative features based on the visual perception of a person;method of speech signal structuring and transforming for biometric personality identification;method of improving instance segmentation for very high resolution remote sensing imagery using deep learning;computer vision system for recognizing the coordinates location and ripeness of strawberries;novel nonparametric test for homogeneity and change-point detection in data stream;software for shelter’s fire safety and comfort levels evaluation.

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An Improved Fuzzy Algorithmic Approach Applying on Medical image to Improve the Contrast 16

An Improved Fuzzy Algorithmic Approach Applying on Medical I...

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16th IEEE International Wireless communications and Mobile Computing conference, IWCMC 2020

作者： Wu, Min Mathematics and Computer Institute Guangdong Ocean University Zhanjiang China

ISBN: (纸本)9781728131290

Noise led from nodes of medical imaging system and underexposure or overexposure will make the image be in a low contrast. In this paper we try to enhance the image contrast with a method of digital image processing. We present an improved fuzzy algorithmic approach on the basis of the conventional Pas. S. K algorithm. Processed by this improved algorithm, the imperceptible information can be avoided to lose when the planar space of original image is transformed to the fuzzy space. And special experiment has been carried out to demonstrate the effect of this approach applying on the X-ray roentgenoscopy image of animal carotid, which is obtained by a homemade instrument and convert to digital signal with an image-collecting card. We find that both images processed with the conventional algorithm and the improved algorithm can provide more details than the original one. While percent of the zero gray pixels of the image processed with the conventional algorithm is much more (to reach 12.3%) than that with the improved algorithm (6.2%) by analyzing with visual C++ image process program, indicating a more loss of information caused by the forcible process of making some image elements equal to zero. Thus it can be concluded that the improved algorithm seems to be more distinct than the conventional one. © 2020 IEEE.

关键词： image enhancement

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High Accurate and Efficient image Retrieval Method Using Semantics for visual Indoor Positioning 1

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8th International conference on communications, Signal processing, and Systems, CSPS 2019

作者： Dai, Jin Ma, Lin Qin, Danyang Tan, XueZhi School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China Electronic Engineering College Heilongjiang University Harbin150080 China

ISBN: (数字)9789811394096

ISBN: (纸本)9789811394089

visual indoor positioning has a wide application because of its good positioning performance without additional hardware requirement. However, as the indoor scenes and complexity increase, the offline database will inevitably become large and the online retrieval time will also become long, which make visual indoor positioning unpractical. To solve this problem, we propose a Semantic and Content-Based image Retrieval (SCBIR) method. By dividing the offline database into semantic databases with different semantic types, the retrieval scope of the image is reduced, and the retrieval time is reduced. First, we use the semantic segmentation method to detect the semantics. Then we divide different semantic scenes in terms of the image order and basic pattern of the semantics in the scene. Finally we use the images belonging to each different semantic scene to build a semantic database, so as to achieve online accurate and fast image retrieval. The experiment results indicate that the proposed method is suitable for large scale retrieval database, and it can reduce the retrieval time in the online stage on the premise of ensuring the accuracy of image retrieval that is critical for visual indoor positioning. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Semantics

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A Multispectral image Edge Detection Algorithm Based on Improved Canny Operator 8th

A Multispectral Image Edge Detection Algorithm Based on Impr...

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8th International conference on communications, Signal processing, and Systems, CSPS 2019

作者： Zhang, Baoju Wang, FengJuan Li, Gang Zhang, CuiPing Zhang, ChengCheng Tianjin Normal University Tianjin300387 China

ISBN: (纸本)9789811394089

The traditional canny operator performs edge detection, which needs to artificially intervene in the variance of Gaussian filtering, and the choice of variance will affect the edge retention and denoising effects. When filtering out the noise, many edge details are lost. Aiming at the shortcomings of the traditional canny algorithm, this paper proposes an improved canny algorithm for edge detection. After multispectral image Gaussian filtering, the mixed and enhanced operation is made on multispectral image. This operation filters out the noise and retains the important edge detail information. In addition, when the gradient amplitude image is obtained, more edge information are obtained by changing the size of the sobel operator. The edge details of the multispectral image processed by this operation are more abundant and the detection is more accurate. The multispectral image is then subjected to non-maximum suppression and double threshold processing. Experiments show that compared with the traditional canny edge detection effect, the algorithm proposed in this paper has greatly improved the effect of edge connection and pseudo edge removal, and the objective evaluation and visual effect have been greatly improved than before. © 2020, Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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Tackling multiple visual artifacts: Blind image restoration using conditional adversarial networks 4th

Tackling multiple visual artifacts: Blind image restoration ...

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4th International conference on Computer Vision and image processing, CVIP 2019

作者： Anand, M. Natraj, A. Ashwin Jose, V. Jeya Maria Subramanian, K. Bhardwaj, Priyanka Pandeeswari, R. Deivalakshmi, S. Department of Electronics and Communication Engineering National Institute of Technology Tiruchirappalli Tiruchirappalli India Department of Instrumentation and Control Engineering National Institute of Technology Tiruchirappalli Tiruchirappalli India

ISBN: (纸本)9789811540172

Restoring images that are degraded by visual artifacts like noise, blurness and other environmental visual artifacts like shadow, snow, rain, and haze is a challenging task. From literature, it can be seen that there are many model-based as well as blind restoration methods that have been proposed to restore an image degraded by a single artifact. In most practical cases, the image is degraded by more than one artifact. Complexity arises while trying to estimate degradation function using conventional techniques where images are degraded by multiple visual artifacts. To the best of our knowledge, there has not been any generalized method proposed to tackle this problem. In this paper, we propose a methodology using conditional adversarial networks for blind image restoration of images that are degraded by multiple artifacts. To analyze the performance, ISTD dataset (meant originally for shadow removal) is used by augmenting it with different types of noises and blurness. The network has been trained on this data and has been analyzed how it behaves during the addition of each artifact. Various image quality metrics like Peak signal-to-noise ratio (PSNR), Mean squared error (MSE), Structural similarity index (SSIM), Blind/reference-less image spatial quality evaluator (BRISQUE) and Naturalness image quality evaluator (NIQE) have been evaluated to validate the performance of the proposed method. © Springer Nature Singapore Pte Ltd 2020.

关键词： image quality

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Secure image Restoration and Contrast Enhancement Using Wavelet Transform and Thresholding Technique

Secure Image Restoration and Contrast Enhancement Using Wave...

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International conference on VLSI, communications, and Signal processing (VCAS)

作者： Singh, Kumari Suniti Mishra, Yogesh Kumar Singh, Harsh Vikram KNIT Sultanpur Dept Elect Engn Sultanpur India

ISBN: (纸本)9789813297753;9789813297746

The paper represents a methodology which involves the use of Satellite images taken from Satellite Imaging Corporation and Google Earth. This technique proposes a satellite image enhancement system in two steps which consists of restoration and contrast enhancement. The work represents a methodology for enhancement of Satellite images using wavelet transform such as Discrete wavelet transform (DWT) and Stationary wavelet transform (SWT), thresholding and contrast enhancement using equalization based on absolute difference. The result of the evaluation indicates that the technique obtains good PSNR value of satellite images. The results also strengthen that the technique fares well in preserving the information content of the satellite images and also improve the visual quality of images.

关键词： Satellite images DWT SWT DWT with SWT Thresholding and equalization

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Medical image Fusion Based on Weighted Least Square Optimization and Deep Learning Algorithm

Medical Image Fusion Based on Weighted Least Square Optimiza...

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Japan-Egypt International conference on Electronics, communications and Computers (JEC-ECC)

作者： C. Ghandour Walid El-Shafai S. El-Rabaie Faculty of Electronic Engineering Menoufia University Menouf Egypt Security Engineering Lab Prince Sultan University Riyadh KSA

ISBN: (纸本)9781665482936

Recently, medical image processing has become a hot area of research, especially with the rapid development in technology and instrumentation, and that’s because of its effective role in the health sector. So that it becomes a very active research tool. This research introduces an image fusion algorithm that utilizes a deep learning model to produce only one medical fused image that includes all the traits from the medical source images. Firstly, the source images are separated into detailed content and base parts using the Gaussian and rolling guidance filters (RGF). Secondly, by the weighted averaging strategy, the base parts are fused. For the detail content, to quote traits of multi-layer which employ weighted average strategy to produce the fused detail content several candidates, the deep learning network is utilized. The max selection technique is employed to gain the last detailed content as soon as these candidates are gotten. Eventually, by uniting the fused detail and base layers, the fused image will be recreated. The experimental outcomes show that this algorithm can accomplish better results by comparing the other fusion methods in both thematic assessment and visual quality.

关键词： Deep learning visualization Instruments Filtering algorithms Information filters Task analysis image fusion

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