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检索条件"任意字段=2023 International Conference on Image Processing and Computer Vision, IPCV 2023"

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Review on Ultrasound-image Analysis of Fetus Using Deep Learning 8

Review on Ultrasound-Image Analysis of Fetus Using Deep Lear...

引用

8th IEEE international conference for Convergence in Technology, I2CT 2023

作者： Gautam, Lavendra Kaur, Gull Delhi Technological University Department of Computer Science and Engg Delhi India Delhi Technological University Department of Computer Science Delhi India

ISBN: (纸本)9798350334012

Deep learning Techniques are rapidly becoming the industry level for analyzing fetal ultrasound pictures. Despite the significant number of survey studies that have previously been published on this subject, the majority focus on image analysis-related Medical Science or do not include all fetal Ultrasound deep learning applications. We have collected 30 research papers released after 2016;this study examines the most recent work on the subject. Each study is evaluated and commented on from both a technique and an application standpoint. The studies were considered for the analysis of anatomical fetus structure. The primary limits and open concerns for each category are mentioned. Summary tables are supplied to make it easier to compare the various techniques. Also presented are publicly accessible data sets and key performance metrics indicators widely used to evaluate algorithmic Behaviour regarding deep learning techniques used. Here studies wrap up with a condemning overview of the present date in Deep learning algorithms for fetal Ultrasound image processing and a conclusion of nowadays problems that researchers working in the field must address to transform research techniques into practical clinical practice. © 2023 IEEE.

关键词： Learning algorithms

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Research on Method of User Preference Analysis Based on Entity Similarity and Semantic Assessment 8

Research on Method of User Preference Analysis Based on Enti...

引用

8th international conference on Signal and image processing, ICSIP 2023

作者： Mou, Chengjie Dai, Weinan Ye, Xuesong Wu, Jun Information Studies Trine University Phoenix United States Computer Science Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9798350397932

The rapidly evolving landscape of Internet technology has led to an increasing interest in smart terminal devices, particularly smartphones, among users. As users interact with these devices, they encounter a range of applications (APPs) with diverse functionalities and consume services according to their individual needs. The service consumption process elicits different emotional experiences from users, which serve as an instinctive reflection of their satisfaction with the service consumption experience. Thus, there is an urgent need to address the challenge of accurately identifying and analyzing users' emotional experiences, assessing their service experience during service consumption, and exploring the factors that influence their service experience. This research addresses the challenge of accurately identifying and analyzing user emotions during service consumption on smart terminal devices. In the ontology alignment work, we integrate existing methods for edit distance similarity, WordNet similarity, and shared nearest neighbor similarity calculation. We achieve entity alignment in the requirement ontology based on both element and structure. Furthermore, we propose an improved method for calculating WordNet similarity. © 2023 IEEE.

关键词： Semantics

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DANet: Density-Adaptive Network for Geometry-based Point Cloud Compression Artifacts Removal

DANet: Density-Adaptive Network for Geometry-based Point Clo...

引用

2023 IEEE international conference on Visual Communications and image processing, VCIP 2023

作者： Yang, Zetao Gao, Wei Lu, Xijing Peking University Shenzhen Graduate School School of Electronic and Computer Engineering Shenzhen China

ISBN: (纸本)9798350359855

To remove compression artifacts in voxelized point clouds compressed by Geometry-based Point Cloud Compression (G-PCC), we propose the Density-Adaptive Network (DANet). The DANet considers the density characteristics of decompressed point clouds and density variation within network processing, which facilities the removal of artifacts. Regarding density characteristics, we design the corresponding modules, including Sparse-convolution Point Aggregation (SPA), Dense Convolution Block (DCB) and Dilated Hyper-Cross Block (DHCB). The proposed SPA combines point-based feature extraction and sparse convolution, enabling efficient processing of both moderately and intensively compressed point clouds. The proposed DCB and DHCB effectively cater to the density variation during network processing, expanding receptive fields while maintaining an affordable computational cost. Experimental results on representative datasets demonstrate that our proposed method can bring a 91.6% D1 BD-Rate gain over G-PCC. Compared with advanced deep learning-based methods, DANet also achieves 4.6% D1 BD-Rate improvement with a smaller model size. © 2023 IEEE.

关键词： Convolution

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Extraction of Soybean Pod Features Based on computer vision 5th

Extraction of Soybean Pod Features Based on Computer Vision

引用

5th EAI international conference on 6G for Future Wireless Networks, 6GN 2022

作者： Ning, Shan Zhao, Qiuduo Zhang, Xudong Heilongjiang University of Science and Technology Harbin150022 China

ISBN: (纸本)9783031360138

Getting agronomic traits of soybean is a labor-intensive and time-consuming operation. The technology of object detection based on image processing is the fundamental and significant link in the research of automatic accurate detection of crop phenotype. In this study, a solution based on computer vision was proposed, which could automatically and accurately detect the phenotype of crops. A fixed distance digital camera and two flash lighting were used for pod identification and automatic measurement of pod width and pod length. A novel proof of method for extraction of soybean pod characters was proposed for segmented soybean plant images. In the extraction of soybean pod characters, it was identified pod types and extracted length and width of pods. From the confusion matrix heat map, the mean of precision was 95.12%, the mean of recall was 97.06%, and the mean of F1-score value was 96.09%. Meanwhile, compared with the deep learning methods, the advantage of the method was that it did not need a large number of training samples and image annotation. The image preprocessing time of IM-Watered was 143.44 min, 21.36 min, and 17.01 min less than that of Vgg16, Alexnet, and GoogleNet. Mosreover, the agronomic characters of soybean could be obtained by one program. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Extraction

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Research and application of 3D simulation of truck formation based on Unreal Engine 3

Research and application of 3D simulation of truck formation...

引用

3rd international conference on image processing and Intelligent Control, IPIC 2023

作者： Wang, Zhenzhou Wu, Fang Zhang, Jiangnan Wu, Jianguang School of Computer and Artificial Intelligence Zhengzhou University China The School of Geo-Science & Technology Zhengzhou University China

ISBN: (纸本)9781510668140

In order to show the transport conditions of goods on different roads and provide more real and three-dimensional transport information for situation inference users, this paper proposes a simple and PID controlled three-dimensional simulation method for truck formation based on Unreal Engine. Firstly, based on the basic theory of automatic control[1], the longitudinal lollipop controller and the transverse PID controller are designed respectively based on the lollipop control and PID control ideas, and the perception-decision framework is combined to realize the automatic driving of the truck along the spline line on the road. On this basis, a truck controller is designed to realize the truck formation driving with high recovery degree based on the leader-follower strategy. The results show that the truck based on PID control can accurately drive along the road line. With the cooperation of truck formation controller, the whole process of formation, maintenance and driving of truck formation can be basically restored. © 2023 SPIE.

关键词： Controllers

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Humanoid Robots that Move on Two Feet and Are Led by vision Systems 3

Humanoid Robots that Move on Two Feet and Are Led by Vision ...

引用

3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kumar, Santosh Yadav, Satya Prakash School of Computing Graphic Era Hill University Dehradun India Gla University Department of Computer Engineering & Applications UP Mathura India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Jaipuria Institute of Management Department of Management Jaipur302033 India Glbitm Department of Computer Science and Engineering UP Greater Noida India

ISBN: (纸本)9798350342338

At the convergence of robotics and computer vision lies a crucial research field that focuses on the development of vision-guided walking for humanoid robots. The ability of humanoid robots to walk independently while simultaneously seeing and comprehending their surroundings via the use of visual inputs offers tremendous potential. Humanoid robots are developed to reproduce human-like qualities and skills. This abstract discusses current developments in the area of vision-guided walking for humanoid robots as well as the approaches that have been used. Through the use of computer vision methods in conjunction with walking algorithms, humanoid robots are given the capacity to traverse situations that are both complex and dynamic while exhibiting increased flexibility. For this, real-time sensing of the environment, object identification and avoidance of obstacles, as well as analysis of the terrain, are required in order to arrive at intelligent judgements while moving. vision-guided walking not only makes robotic locomotion more secure and effective, but it also paves the way for a wide range of potential applications, including exploration, human-robot interaction, search and rescue, and many more. Visual sensing systems, sensor fusion procedures, gait generation methods, motion planning, and control strategies. These topics are discussed in relation to vision-guided walking. Different types of sensors, such as cameras, depth sensors, and LiDAR, are being investigated to see how they can best provide a holistic understanding of the surrounding environment. In addition, the abstract draws attention to the difficulties caused by sensor noise, occlusions, fluctuations in illumination, and the need for real-time processing. In places an emphasis on the role that methods of machine learning play in the improvement of vision-guided walking. The use of deep learning algorithms gives robots the ability to learn from data, hence enhancing their capacity to understand and r

关键词： Head movement in robot Humanoid robots Planning and control Proprioceptive sensors Sensors vision guided

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Reversible Data Hiding in Encrypted image based on Pixel Prediction and Group Marking

Reversible Data Hiding in Encrypted Image based on Pixel Pre...

引用

2022 international conference on Signal processing, computer Networks, and Communications, SPCNC 2022

作者： Liu, Ruihua Zhou, Quan Xi'an Institute of Space Radio Technology Xi'an710100 China

ISBN: (纸本)9781510664616

In recent years, as people pay more and more attention to the protection of cloud data, reversible data hiding in encrypted image (RDHEI) has become the focus of research. This paper proposes a hiding scheme based on pixel prediction and group marking. First, the cover image is pre-processed, including block, grouping, pixel prediction, etc. Then, the embedding method and position are selected according to the parameters, and the marked map is generated. Finally, we encrypt the image and embed data in the encrypted pixels according to the marker map to obtain an image for transmission. For the receiver, separable data recovery is performed based on the key. The correct rate of secret data extraction is 100%, and the cover image can be recovered without distortion. This method does not require auxiliary information during decryption, but the decryption quality is good. At the same time, the method provides two embedding methods to provide a high embedding rate. Therefore, this method has advantages in image decryption quality, embedding rate, and separability. © 2023 SPIE.

关键词： Forecasting

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Reconfigurable Rounding based Approximate Multiplier for Floating Point Numbers 4

Reconfigurable Rounding based Approximate Multiplier for Flo...

引用

4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Mahima, R. Maheswari, M. Ramkumar, P. Kaushik, N. Mugilan Karthik, N. Nishanth, S. K. Ramakrishnan College of Engineering Department of Ece Trichy India

ISBN: (纸本)9798350300093

Most of all computations carried out by these DSP cores are contributed by the arithmetic units, particularly multipliers. For the creation of energy-effectual digital signal processing cores, a high economical and low power design is also a crucial necessity. The DSP processors in portable devices run multimedia programs and produces image and video outputs for human consumption. Because human vision is limited, an approximate architecture can be employed to achieve excellent energy consumption with minimal performance loss. The theme is to design a reconfigurable ROBA multiplier by using floating numbers as an input. The architectural (circuit and logic levels) and algorithmic of ROBA multiplier can used to approximate values within arithmetic units. As a result, researchers in the subject of approximation computing have focused particularly on developing approximate arithmetic units, ROBA multiplier offers a superior trade-off between power, performance, and computational error. The design is stimulated by Xilinix Vivado software. © 2023 IEEE.

关键词： Digital signal processing

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computer vision Aided Machine Learning Framework for Detection and Analysis of Arm Flapping Stereotypic Behavior Exhibited by the Autistic Child 1

引用

Proceedings of the 6th IFIP TC 12 international conference on Computational Intelligence in Data Science, ICCIDS 2023

作者： Dundi, Umamaheswara Reddy Kanaparthi, V. Phani Kumar Bandaru, Ramakrishna Umaiorubagam, Ganapathy Sankar Diagnostics Therapeutics and Assistive Devices Laboratory Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India SRM College of Occupational Therapy SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India

ISBN: (数字)9783031382963

ISBN: (纸本)9783031382956

Stereotypic movements such as arm flapping, spinning, and body rocking are found to be unique characteristics in the majority of autistic children. Detection and analysis of the above said stereotypic movements at an early stage could potentially help in treating autistic children before they develop severe and socially unacceptable behaviors. Taking advantage of computer vision and media pipe machine learning framework, a threshold-based data peak filtering algorithm for detection of one of the stereotypic behavior, i.e., arm flapping is proposed in this study. Flapping behavior of autistic children, such as Arm flapping intensity and its frequency, is extracted to assess the severity of autism. This crucial information helps the therapist to analyse and estimate the variations in a child’s stereotypic behavioral pattern for Autism Spectrum Disorder (ASD) therapy. © 2023, IFIP international Federation for Information processing.

关键词： computer vision

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Anomaly Detection and Classification in Agricultural Produce Using image processing and CNN Assisted by a Robotic Arm

Anomaly Detection and Classification in Agricultural Produce...

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international conference on New Technologies, Development and Application

作者： Viswanathan, Varsha Murali, Supraajha Veeraraghavan, Venkatakrishnan Easwari Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9783031310652;9783031310669

TheGlobal Hunger Crisis has long been one of the most pressing problems of the modern world. Surveys have shown that globally, around 14 percent of food produced is wasted between harvest and retail. This project aims to develop a mechanism that uses image processing and deep learning to classify agricultural produce and perform anomaly detection. The system performs two kinds of evaluations;a mass-evaluation and a singular evaluation. The mass evaluation of produce is done by angling a camera at an angle theta (.), that is pre calculated through an optimal angle calculation algorithm. In addition, the system provides controls to a supervisor to specifically evaluate individual items based on the factor of "intuitive inquiry". In this process, a robotic arm picks the target item and takes it to the camera physically for end-to-end coverage. The data obtained from both mass analysis and individual analysis is fed into a program containing metrics for evaluation. Based on the degree of adherence/divergence from standards, the system also recommends a further progression by classifying the item into sets-i.e., if the item is anomaly-free, if it is fully defective and must be discarded, if it can be corrected through further processing, or if it has been under processed. With each iteration of item evaluation, the system intelligently learns from its decisions for improved accuracy and speed.

关键词： computer vision image processing Convolutional Neural Networks Deep Learning

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