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Geolocated visual Summarization of Social Media Data 28

Geolocated Visual Summarization of Social Media Data

引用

28th Signal processing and communications Applications conference (SIU)

作者： Sanhalp, Elif Bulbul, M. Abdullah Ankara Yildirim Beyazit Univ Bilgisayar Muhendisligi Ankara Turkey

ISBN: (纸本)9781728172064

The usage of social media is increasing day by day. People use social media platforms to communicate with their friends or other users and to demonstrate what they are interested in by sharing different kinds of media such as photos, texts, and videos. A portion of the posted content also include location information. Such posts having location information are called geo-tagged posts in social networks. According to the analysis of geo-tagged posts, popular locations or activities can be identified. This study proposes a method to identify the most representative subset of the visual content shared in a region through social media. Our approach aims to detect the popular places and events and utilizes Scale-Invariant Feature Transform (SIFT) features. Identified representative visuals are used to generate a web based tourist map. In this study, Flickr is used as the source of geo-tagged visual content.

关键词： image processing social media analysis geo-tagged images Flickr location information SIFT feature extraction

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Involution Based Speech Autoencoder: Investigating the Advanced Vision Operator Performance on Speech Feature Extraction 10

Involution Based Speech Autoencoder: Investigating the Advan...

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10th IEEE Global conference on Consumer Electronics, GCCE 2021

作者： Zhong, Tianle Velazquez, Israel Mendoza Haneda, Yoichi University of Electronic Science And Technology of China School of Computer Science And Engineering Chengdu China University of Electro-Communications Graduate School of Informatics Tokyo Japan

ISBN: (纸本)9781665436762

Feature extraction is an important aspect of deep learning. In recent studies, various methods from image processing have been applied to speech feature extraction. Although the involution operator has achieved great success in vision recognition, the use of the involution operator in speech tasks has not been investigated. To determine whether the performance improvement of this operator on visual tasks is reflected directly in the speech processing domain, this study investigated autoencoder networks operating on Mel-spectrograms utilizing involution and convolution. It was determined that convolution continues to outperform involution as a direct replacement. The reasons for discrepancies in the input feature dimensions of different fields are discussed. Based on its channel-agnostic property, involution in combination with convolution requires additional research. © 2021 IEEE.

关键词： Feature extraction

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Encoded Deep Features for visual Place Recognition 28

Encoded Deep Features for Visual Place Recognition

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28th Signal processing and communications Applications conference (SIU)

作者： Hafez, A. H. Abdul Alqaraleh, Saed Tello, Ammar Hasan Kalyoncu Univ Comp Engn Dept Gaziantep Turkey

ISBN: (纸本)9781728172064

In this work, a new VPR approach that uses the features extracted from a Convolutional Neural Network (CNN) architecture that will be encoded by the Fisher Vector (FV) is introduced. As the main aim of this work is to develop a robust approach that can meet real-life challenges, the deep features are encoded with FV, which as shown in the experiments section, can lead to getting more robust features. Our approach was evaluated using two classifiers, Dynamic Time Warping (DTW) and Support Vector Machine (SVM) in particular. Using both classifiers, the FV-based encoded features have outperformed the non-encoded features.

关键词： Dynamic time warping Deep features Fisher Vector CNN image sequence matching visual place recognition

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TSAN et: Two-subnet Attention Network for Single image Dehazing

TSAN et: Two-subnet Attention Network for Single Image Dehaz...

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International conference on Wireless communications and Signal processing (WCSP)

作者： Chenran Jiang Fei Yan Tao Deng Lin Sun Jun Li School of Information Science and Technology Southwest Jiaotong University Chengdu China China Electronic Product Reliability and Environmental Testing Research Institute Guangzhou China

ISBN: (纸本)9781665450867

Haze restricts visual quality and degrades the qual-ity of captured images. The aim of single image dehazing is to recover a haze-free image from a hazy one. However, most present image dehazing methods treat different feature information in channels and pixels evenly, which may influence the dehazing result because of the uneven distribution of haze. To address hazy uneven distribution, we propose an end-to-end two-sub net attention network (TSANet), which consists of attention-recurrent (AR) and asymmetric u-shaped dehazing refinement (AUDR). In addition, a feature residual attention (FRA) block is designed to focus on thick-hazy regions and high-frequency regions of a hazy image when dehazing. In the model, the input image is first fed into the AR sub-network to extract feature information like thick hazy regions and high-frequency regions. For further feature refinement, we propose the AUDR sub-network to further process feature information from the AR sub-network. The AUDR sub-network adopts an encoder-decoder module containing FRA and transformer blocks to further process feature information of high-frequency regions and filter hazy feature information, and uses skip connections to enhance the representation of our TSANet. The extensive experimental results demonstrate the effectiveness of our method and outperform other dehazing methods on synthetic and real-world hazy datasets.

关键词： Wireless communication visualization Computer vision Signal processing Feature extraction Information filters Transformers

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Research on image Defogging Algorithm for Power Monitoring

Research on Image Defogging Algorithm for Power Monitoring

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International conference on Communication Technology (ICCT)

作者： Li Jiang Chaoxing Zhao School of Electrical and Information Engineerin Zhengzhou University Zhengzhou China

With the continuous expansion of power grid scale, smart grid has become the mainstream of power monitoring. Bad weather conditions such as smog will lead to the deterioration of the quality of captured images, which will further restrict the development of smart grid. In this paper, an image defogging algorithm is proposed according to the characteristics of power monitoring images. According to the characteristics of image regions, the image is divided into sky regions and non sky regions. For non sky regions, an improved dark channel prior algorithm is used to solve the problem of poor detail processing. For sky regions, a large number of experiments have observed that there is a linear relationship between the minimum channels of fog free images and fog images. It improves the poor segmentation effect of the conventional algorithm in the sky area, and applies the synthetic projection rate and atmospheric light to the defogging of the power monitoring image. Compared with other defogging methods, its objective indicators are improved, and the overall visual effect of the defogging image is good.

关键词： image segmentation Force visual effects communications technology Smart grids Complexity theory Monitoring

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An algorithm for parallel processing of traffic signs video on a graphics processor

An algorithm for parallel processing of traffic signs video ...

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International conference on Information Science and communications Technologies (ICISCT)

作者： Mukhriddin Umarov Jamshid Elov Sirojiddin Khalilov Inomjon Narzullayev Marat Karimov dept. Department of Software Engineering Samarkand branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Samarkand Uzbekistan dept. Department of Hardware and Software of Management Systems in Telecommunication Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan dept. Department of Information Technologies Nurafshan branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan

In the lighting conditions such as snowing, hazing, raining, and weak lighting condition, the accuracy of traffic sign recognition is not very high. It is important to develop an algorithms for real-time fast detection of road signs from video images and use them in driver assistance systems. Firstly, we image preprocessing by using dark channel prior based image dehazing for remove noises from the input image. In this article, we develop a transposed image filtering method combined with other fast parallel filtering algorithms. We propose a high-speed and high-accuracy method by improving image dehazing algorithms and analyzing GPU architecture. In this, we quickly removing fog from HD video images using DCP with image quality and visual effects.

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Human Attention Based Movie Summarization: Dataset and Baseline Model

Human Attention Based Movie Summarization: Dataset and Basel...

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2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Zhao, Defang Zhu, Dandan Min, Xiongkuo Yue, Jiaomin Zhang, Kaiwei Zhou, Qiangqiang Zhai, Guangtao Yang, Xiaokang CloudWalk Technology Shanghai China School of Computer Science and Technology Donghua University Shanghai China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China School of Software Jiangxi Normal University Nanchang China Ai Institute Shanghai Jiao Tong University MOE Key Lab of Artificial Intelligence Shanghai China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

The movie summarization model can automatically edit a condensed and succinct version of the movie by selecting the keyframes. Previous works mainly resort to hand-crafted heuristics and most of them are unsupervised. Supervised movie summarization is a new research field and, there is currently no publicly suitable dataset available. Moreover, existing works only focus on the movies themselves while neglecting the audiences, who have the most say in which part of the movie is more attractive. To deal with the aforementioned limitations, we establish a human attention based movie summarization dataset Movie50. Specifically, we explore the human attention variations when watching videos and have the following findings: (1) The attention of humans is concentrated when watching keyframes. (2) The attention of humans is distracted when watching non-keyframes. Inspired by these findings, we collect the eye fixations of 20 participants when watching 50 movies and propose a novel human attention based annotation pipeline. In addition, we introduce A/V-MSNet, an audiovisual neural network that takes advantage of spatio-temporal visual and auditory information to better model human attention as well as exploit more plentiful information. Extensive experiments demonstrate the superiority of the proposed method. © 2022 IEEE.

关键词： Motion pictures

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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...

引用

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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