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Correcting Low Illumination images Using PSO-Based Gamma Correction and image Classifying Method 5th

Correcting Low Illumination Images Using PSO-Based Gamma Cor...

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5th International conference on Computer Vision and image processing, CVIP 2020

作者： Das, Swadhin Roy, Manali Mukhopadhyay, Susanta Dhanbad India

ISBN: (纸本)9789811610912

In this work, the authors have proposed a method for improving the visual quality of 2D color images suffering from low illumination. The input image is converted to HSV (Hue, Saturation, Value) color space, and the V component is subjected to high pass Laplace filter. The filtered output is then made to undergo a two-stage classifier and a brightness correction process. Finally, the resultant image obtained is gamma-corrected using an optimum gamma value computed using a well-known meta-heuristic based optimization technique namely, particle swarm optimization (PSO). The corrected V component is combined back with the H and S components to reconstruct the final result. The authors have tested this method on a number of 2D color images of natural scenes and the result is found to be satisfactory. Also, the experimental results are compared with similar methods in terms of subjective and objective metrics. © 2021, Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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An Object Detection System for Automatic Driving: MEC-YOLO Based on "Cloud-Edge-End"

An Object Detection System for Automatic Driving: MEC-YOLO B...

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Natural Language processing (ICNLP), International conference on

作者： Siyuan Liang Xu Zhang School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

The massive machine type of communication (mMTC) of 5G is the basis for IoT (Internet of Things) development, which can promote automobile industry upgrades: intelligent and green. Automatic driving (AD) is an important trend in intelligent vehicles. With the development of computer vision (CV) technology, pure vision sensor-based solutions are dominating the AD of intelligent vehicles. Due to CV hardware platform energy and cost consumption constraints, meeting the detection speed and accuracy of vehicle AD requirements have become an obvious challenge, especially for new energy vehicles. Combining the advantages of edge computing and IoT based on mMTC, this paper proposes a system called mobile edge computing-you only look once (MEC-YOLO) to solve this problem, which is an energy-efficient and low-cost instance-level vision perception system for AD. In this system, the local hardware platform uses the improved object detection (OD) network to extract features from the locally collected visual data, processes them into a feature extraction queue, uploads them to the edge network nodes to complete the OD task, and transmits the prediction results and trains weights back to the local devices. In addition, the cloud computing center resource can be invoked to supplement edge nodes when the edge computing capacity is insufficient. Furthermore, we develop an AD platform equipped with Jetson for system validation, which improved the detection speed by 93.2% and reduced the GPU resource occupation rate by 90.8% compared to YOLOv4 on the KITTI dataset. The test results verified that the MEC-YOLO proposed in this paper has the advantages of fast detection speed, high accuracy, and low resource occupation rate compared with the traditional OD scheme.

关键词： Cloud computing Accuracy Intelligent vehicles image edge detection Graphics processing units Object detection Feature extraction Hardware Internet of Things Edge computing

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Design of a Fused Triple Convolutional Neural Network for Malware Detection: A visual Classification Approach 5th

Design of a Fused Triple Convolutional Neural Network for Ma...

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5th International conference on Advances in Computing and Data Sciences (ICACDS)

作者： Smmarwar, Santosh K. Gupta, Govind P. Kumar, Sanjay Natl Inst Technol Dept Informat Technol Raipur Madhya Pradesh India

ISBN: (纸本)9783030814625;9783030814618

Detection of malware signatures from executable files requires effective signal processing and sandboxing operations, wherein the executable file is scanned for any malignant behavior. The recent malware detection techniques are based on static approaches that use machine and deep learning for analyzing malware signatures from byte and assembly-level program data. The byte-patterns are based on outliers, and the program-data is classified as a malware. These methods are not capable of detecting new variants of malware with long patterns of codes and huge dataset to classify the benign or malicious files. The issue with pattern analysis of large byte code dataset needs effective classification performance. To overcome these drawbacks, this paper has proposed a novel fused-triple convolutional neural network (fCNN) based framework for malware detection. This framework improves the accuracy of malware classification by converting the byte and assembly information into image data. This framework obtained more than 98% accuracy on the Microsoft Malware Dataset.

关键词： Malware detection CNN VGGNet Byte Assembly image Classification

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Information-theoretic non-uniformity correction of infrared image sequences 28

Information-theoretic non-uniformity correction of infrared ...

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28th Signal processing and communications Applications conference (SIU)

作者： Nar, Fatih Oezguer, Atilla Ankara Yildirim Beyazit Univ Dept Comp Engn Ankara Turkey Jacobs Univ Math & Logist Bremen Germany

ISBN: (纸本)9781728172064

In infrared (IR) image sequences, non-uniform imaging artifacts can appear due to sensor characteristics and operating temperatures. These non-uniformity artifacts degrade the performance of scene analysis algorithms such as target detection, target recognition, and target tracking as well as human expert visual analysis. In this study, we propose a software based non-uniformity correction (NUC) approach based on optimization of a mutual information (MI) driven cost function. Proposed method, namely information-theoretic NUC (IT-NUC), has two steps. First, shutter is closed for a short period of time and non-uniform pattern is extracted using the acquired image sequence. This first step is done for once and only repeated when camera needs to be calibrated. In the second step, extracted pattern is removed from image sequences that are acquired while shutter is opened. This step is executed in an online manner via an optimization approach. Analysis show that proposed method provides satisfactory NUC capabilities using commodity hardware in real-time.

关键词： infrared sequence non-uniformity correction image enhancement mutual information

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The Method for image Noise Detection Based on the Amount of Knowledge Associated with Intuitionistic Fuzzy Sets 5th

The Method for Image Noise Detection Based on the Amount of ...

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5th Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) International Joint conference on Web and Big Data

作者： Guo, Kaihong Zhou, Yongzhi Liaoning Univ Shenyang 110036 Peoples R China

ISBN: (纸本)9789811681431;9789811681424

The existing methods of two-stage fuzzy noise detection are all based on intuitionistic fuzzy entropy (IFE) using only the aspect of hesitation margin in the context of intuitionistic fuzzy sets (IFSs). However, there are great limitations of this type of method due to the inherent shortcomings of IFE, thus leading to limited accuracy of detecting noise. To solve this problem, we introduce in this paper the intuitionistic fuzzy knowledge measure (IFKM) into this fuzzy noise detection stage, in which the IFKM plays an important role. The fuzzification of an image at each grey level is implemented first by establishing two classes of IFSs for the foreground and background of the noisy images. The maximum amount of knowledge is then calculated, with which to determine the optimal threshold using the membership function values of the foreground and background of the noisy images, respectively. The noise pixels are detected with a noise membership function constructed by the average intensity of foreground background and each pixel value of noisy images, and removed with the iterative mean filter (IMF). By experiment, we use such performance metrics as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), visual Information Fidelity (VIF) and image Enhancement Factor (IEF) to assess image quality. Furthermore, we compare denoising results of the proposed method with other state-of-the-art methods. The qualitative results show that the proposed method outperforms these algorisms. This paper applies the latest IFKM theory to the field of image noise detection for the first time, which also creates a new example for exploring the potential application areas of the theory.

关键词： Salt-and-pepper noise Noise detection image processing Intuitionistic fuzzy knowledge measure Noise removal Intuitionistic fuzzy set

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Development of a VS ultrasound diagnostic system with image evaluation functions

Development of a VS ultrasound diagnostic system with image ...

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IEEE Global conference on Consumer Electronics (GCCE)

作者： Jiayi Zhou Norihiro Koizumi Yu Nishiyama Ryosuke Tsumura Hiroyuki Tsukihara Naoki Matsumoto Department of Mechanical and Intelligent Systems The University of Electro-Communications Tokyo Japan Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan Health and Medical Research Institute National Institute of Advanced Industrial Science and Technology Tokyo Japan Graduate School of Engineering The University of Tokyo Tokyo Japan Department of Gastroenterology and Hepatology Nihon University Tokyo Japan

ISBN: (纸本)9781665492331

An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constantly adjust the probe position to obtain a cross-section of the target organ, which is constantly shifting due to patient respiratory motions. Therefore, we developed an ultrasound diagnostic robot that works in cooperation with a visual servo system based on deep learning that will help alleviate the burdens imposed on physicians. Two different image processing methods (BiSeNet V2) were used to detect the target kidney location, as well as to evaluate the appropriateness of the obtained ultrasound images (ResNet 50) in developed system.

关键词： Deep learning visualization Ultrasonic imaging image processing Medical services Probes Servomotors

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Hand Gesture image Enhancement for Improved Recognition and Subsequent Analysis 2nd

Hand Gesture Image Enhancement for Improved Recognition and ...

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2nd International conference on Communication, Networks and Computing, CNC 2020

作者： Kaur, Jatinder Mittal, Nitin Kaur, Sarabpreet Department of Electronics and Communication Engineering Chandigarh University Mohali140413 India Department of Electronics and Communication Engineering Chandigarh Engineering College Mohali140413 India

ISBN: (纸本)9789811688959

This manuscript entails the preprocessing of hand gesture images for subsequent gesture recognition and cognitive communication. From this detailed analysis, it is obvious that the preprocessing stage is important for hand gesture recognition and its enhancement. Key to achieving the system design goal is optimizing this stage’s performance. Through the techniques that are described in this stage, the quality of the image for the subsequent stages will be assured. The techniques discussed in literature are rarely successful when they seek to retain the image quality and provide a low noise result. At the segmentation stage, the image is filtered to include only the Region of Interest, which is a clear visual representation of the hand portion, separated from the rest of the information. These techniques are implemented on the images acquired from the dataset and various pre-processing techniques are compared and identified which technique yields the best possible results for optimizing the preprocessing of hand sign images. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Mean square error

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Compressed Video Quality Metric Based on Just-Noticeable-Difference and Saliency-aware Blocking Detection 7

Compressed Video Quality Metric Based on Just-Noticeable-Dif...

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7th International conference on Computer and communications, ICCC 2021

作者： Wang, Zheng Liao, Guodong Wu, Yuxuan Lin, Liqun Yang, Jing Zheng, Haifeng Fujian Key Lab For Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou China Huawei Technologies Co.Ltd. Guangdong Shenzhen China

ISBN: (纸本)9781665409506

Block-based prediction-quantization hybrid coding framework is widely used in video compression, which results in visually annoying artifacts, i.e., blocking artifacts, on encoded videos. They significantly degrade the Quality-of-Experience (QoE). To improve the QoE, it is crucial to develop Video Quality Assessment (VQA) methods that can identify and quantify various artifacts, among which the negative effect introduced by blocking artifacts is of guiding significance. To evaluate and monitor video quality effectively, a novel Blocking-based Compressed Video Quality Assessment (BCVQA) is proposed in this paper. The key idea of the BCVQA method is a blocking detection model based on Just-Noticeable-Difference (JND) mechanism, which is aimed to improve detection performance, since it only focuses on the observable blocking artifacts. Furthermore, the saliency model is utilized in quality prediction procedure after blocking detection, considering the visual attention mechanism. With the fusion of the above two visual mechanisms, quality prediction of compressed videos becomes more effective, since the useful information is refined in our framework. Experimental results show that the proposed BCVQA is consistent with perceived quality and can effectively evaluate video quality. © 2021 IEEE.

关键词： image compression

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Fusing Knowledge and Experience with Graph Convolutional Network for Cross-task Learning in visual Cognitive Development 5th

Fusing Knowledge and Experience with Graph Convolutional Ne...

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5th International conference on Cognitive Systems and Signal processing, ICCSIP 2020

作者： Zhang, Xinyue Yang, Xu Liu, Zhiyong Zhang, Lu Ren, Dongchun Fan, Mingyu State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Meituan-Dianping Group Beijing100190 China

ISBN: (纸本)9789811623356

visual cognitive ability is important for intelligent robots in unstructured and dynamic environments. The high reliance on large amounts of data prevents prior methods to handle this task. Therefore, we propose a model called knowledge-experience fusion graph (KEFG) network for novel inference. It exploits information from both knowledge and experience. With the employment of graph convolutional network (GCN), KEFG generates the predictive classifiers of the novel classes with few labeled samples. Experiments show that KEFG can decrease the training time by the fusion of the source information and also increase the classification accuracy in cross-task learning. © 2021, Springer Nature Singapore Pte Ltd.

关键词： image recognition

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Compressed sensing for SAR image reconstruction 2

Compressed sensing for SAR image reconstruction

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2nd IEEE International conference on Electronic Engineering, ICEEM 2021

作者： El-Ashkar, Alaa M. Shendy, H. El-Shafai, Walid Taha, Taha El Sayed El-Fishawy, Adel S. El-Nabi, Mohamed Abd El-Samie, Fathi E. Abd Menoufia University Faculty of Electronic Engineering Communications and Electronics Department Menouf Egypt

ISBN: (纸本)9781665418423

Synthetic Aperture Radar (SAR) is a common radar imaging technique in a wide range of applications. The SAR imaging relies on keeping eye on targets and imaging from various angles by synchronizing the movement of antenna with the target of interest. The large size of SAR images compared with storage hardware limitations or connection capacity limitations introduced a need of using compression techniques. The primary propose of this paper is to introduce an effective compression technique that can achieve high compression rates, while retaining critical information without damage or loss. Compressed Sensing (CS) represents a reliable, highly dependable and effective choice. This paper introduces a CS technique for SAR image reconstruction. The proposed technique addresses the issue of storing or transmitting large-size SAR images over restricted links, while preventing quality degradation produced by processing. Both visual and numerical results indicate the success and reliability of the presented technique. © 2021 IEEE.

关键词： Radar imaging

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