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A dermoscopic image segmentation algorithm based on U-shaped architecture 2

A dermoscopic image segmentation algorithm based on U-shaped...

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2nd International conference on Big data, Information and Computer Network, BDICN 2023

作者： Ru, Kanglei Li, Xiaoli Donghua University College of Information Science and Technology Shanghai China

ISBN: (纸本)9798350321364

The dermatological images have complex characteristics such as irregular shape, large scale variation, insufficient contrast and blurred boundary. In this paper, multiscale atrous convolutions pooling module (MSACP) is proposed to learn rich feature information of target regions from multiple perceptual fields, which can fully identify irregularly shaped regions and solve the problem of feature loss due to uneven scales. And attention refinement of contextual information (ARCI) is proposed to extract the shallow detailed feature information and pass it to the decoding part to solve the boundary blurring. Therefore, an improved u-net medical image segmentation algorithm based on MSACP and ARCI is proposed. The algorithm is compared experimentally at ISIC2018 and the results show that it outperforms other algorithms in dermatological medical image segmentation. © 2023 IEEE.

关键词： image enhancement

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Design of an Automatic Difficulty Scoring System for Aerobics Based on image Processing Technology 2

Design of an Automatic Difficulty Scoring System for Aerobic...

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2022 2nd International conference on Health Big data and Smart Sports, HBDSS 2022

作者： Wu, Qiuling Zhang, Shujian Department of Physical Education College of General Education Guangdong University of Science and Technology Guangdong Dongguan China

ISBN: (纸本)9783800760428

With the development of social economy and the increasing demand for quality of life, aerobic operation is an important part of popular sports activities, and its uniqueness and entertainment are getting more and more attention from the public. Aerobics has now become not only a fitness sport, but also it has been an important competition item. In order to improve the accuracy of aerobics scoring, this paper is based on image processing technology and action recognition algorithm, using the body sensor Kinect acquisition as a data collection module to process and analyze the images of aerobics athletes, and collect the athletes' body sensory information according to the images, and then transmit the collected image information to the data processing layer through the serial communication protocol. The data processing layer mainly uses the image processing technology and action recognition algorithm to extract the effective information from the image, and finally the segmented data is displayed through SMS/LCD and tips are given to the athlete scoring system on the upper computer interface. Experimental results show that:the algorithm can quickly and accurately perform sample training judgment as well as effective scoring, and the method can better solve the problem of subjective evaluation of athletes, which improves the accuracy and efficiency of automatic judging of aerobics difficulty. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Sports

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Action Assurance: Federated CNN Architectures Decoding Cricket Umpire Signals 2

Action Assurance: Federated CNN Architectures Decoding Crick...

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2nd IEEE World conference on Communication and Computing (WCONF)

作者： Suryavanshi, Ankita Mehta, Shiva Chaudhaiy, Preeti Thapliyal, Siddhant Chitkara Univ Inst Engn & Technol Rajpura Punjab India Graph Era Hill Univ Comp Sci & Engn Dehra Dun Uttarakhand India Graph Era Deemed Univ Comp Sci & Engn Dehra Dun Uttarakhand India

ISBN: (纸本)9798350395334;9798350395327

This article shows a federated learning framework based on CNN and will identify cricket umpire gestures. We have federated and used Federated training mechanics to train the five clients: WACA, BBL, CPL, IPL, and SMA. This way, we applied a wide range of data sources with an equal platform to maintain the anonymity of each person's data. The model accurately categorized five unique umpire signals: "Out," "Four," "Six," "No Ball," and "Wide Ball." This assessment consisted of four measures, e.g., precision, recall, F1-score, and accuracy. The performance of the five customers' C-level outcomes was between 75.63% and 87.05% regarding the overall average accuracy. Among these, datasef_5 is the most suitable for model training, showing the highest scores in rating all the metrics. The result is the accuracy (the weighted average), which changes between 75.64% and 87.07% Precision. The precision computed using the micro average ranged from 75.65% to 87.02%, and it was also found that 30.36% of sentences gained improvements in terms of pyrotechnical relevance. The results confirm the model's reliability. An overall accuracy of 92% shows a high level of accurate world application adaptation. The accuracy of the "Out" signal has shown the highest precision. Since this signal brings forward consequent outputs, successful implementation of the model will be associated with the highest efficiency in payouts from the system. The federated learning method was introduced to solve this issue, using a technique that makes it impossible to reconstruct client data. Individual client data is from the changes.

关键词： Umpire Cricket Umpire Action (CNN)_(FL) Recognition Augmentation image data privacy data security

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Application of Unmanned Aerial Vehicle image Denoising Based on FPGA in Unmanned Aerial Vehicle Tilt Photography Assisted Intelligent Construction Site Management 2

Application of Unmanned Aerial Vehicle Image Denoising Based...

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2nd International conference on 3D Immersion, Interaction and Multi-Sensory Experiences, ICDIIME 2023

作者： Wang, Yufang Xiamen Institute of Technology Xiamen Fujian361021 China

ISBN: (纸本)9798350323818

Smart construction site is a smart management platform built by means of Internet, Internet of Things, big data, BIM technology, digital technology and tilt photography technology. Managers can obtain a high-precision, high-resolution real three-dimensional real-life model of the construction site through oblique photography technology. In this paper, the application of UAV(Unmanned Aerial Vehicle) tilt photography aerial survey technology combined with FPGA in smart site system effectively integrates various resources in engineering construction, cost control, safety supervision, data analysis and decision efficiency, and realizes the monitoring and management of engineering projects. According to the actual survey data information, before planning the route, the system automatically matches the points with the same name in all the images through dense matching technology, and extracts more feature points from the images to form dense point clouds, thus more accurately expressing the details of the ground objects and effectively utilizing and expressing the underground information around the rail transit line;Finally, it provides basic conditions for spatial analysis such as personnel or large-scale equipment positioning analysis, rationality analysis of underground pipeline relocation and reconstruction, and regional management of key buildings in shield area. © 2023 IEEE.

关键词： Information management

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Brain Tumor Diagnosis using image Fusion and Deep Learning 2

Brain Tumor Diagnosis using Image Fusion and Deep Learning

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2nd International conference on Sustainable Computing and data Communication Systems, ICSCDS 2023

作者： Kumar, Vivek Joshi, Kapil Kanti, Puneet Reshi, Jagdeep Singh Rawat, Gunjan Kumar, Anil Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science Dehradun India Tula's Institute Dehradun India

ISBN: (纸本)9781665491990

Deep learning has developed into a very active and practical research tool that is applied in many domains of image processing. Pre-processing is an enhancement of the picture data that reduces unintentional distortions or boosts certain elements of the image that are crucial for subsequent processing. By extracting the supporting information from many multimodality medical images, the image fusion approach is one of the most significant and beneficial medical disease investigation tools. Combining photos from several imaging methods that is magnetic resonance imaging and computed tomography to improve the photo content is known as medical image fusion. The resulting fused images via image fusion will be more illuminating than the individual input images, making them better suited for classification issues. In this paper, to create fused pictures, brain tumor images are used with a discrete cosine transform-based fusion approach. The brain tumor is characterised by unchecked tissue development. If discovered at an early stage, it is easily curable. This study examines how image fusion can enhance the quality of the final images and hence enhance classifier performance. © 2023 IEEE.

关键词： image fusion

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The 2nd Workshop on UAVs in Multimedia: Capturing the World from a New Perspective 2

The 2nd Workshop on UAVs in Multimedia: Capturing the World ...

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2nd Workshop on UAVs in Multimedia

作者： Zheng, Zhedong Shi, Yujiao Wang, Tingyu Chen, Chen Zhu, Pengfei Hartley, Richard University of Macau China ShanghaiTech University Shanghai China Hangzhou Dianzi University Hangzhou China University of Central Florida Orlando United States Tianjin University Tianjin China Australian National University Canberra Australia

ISBN: (纸本)9798400712067

Unmanned Aerial Vehicles (UAVs), also known as drones, have become increasingly popular in recent years due to their ability to capture high-quality multimedia data from the sky. With the rise of multimedia applications, such as aerial photography, cinematography, and mapping, UAVs have emerged as a powerful tool for gathering rich and diverse multimedia content. This workshop aims to bring together researchers, practitioners, and enthusiasts interested in UAV multimedia to explore the latest advancements, challenges, and opportunities in this exciting field. The workshop will cover various topics related to UAV multimedia, including aerial image and video processing, machine learning for UAV data analysis, UAV swarm technology, and UAV-based multimedia applications. In the context of the ACM Multimedia conference, this workshop is highly relevant as multimedia data from UAVs is becoming an increasingly important source of content for many multimedia applications. The workshop will provide a platform for researchers to share their work and discuss potential collaborations, as well as an opportunity for practitioners to learn about the latest developments in UAV multimedia technology. Overall, this workshop will provide a unique opportunity to explore the exciting and rapidly evolving field of UAV multimedia and its potential impact on the wider multimedia community. © 2024 Owner/Author.

关键词： Drones

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Passive non-line-of-sight imaging for hidden target detection 2

Passive non-line-of-sight imaging for hidden target detectio...

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2nd Advanced Imaging and Information Processing conference, AIIP 2024

作者： Wang, Hailu Chen, Minsun Liu, Hao Lai, Wenchang Lei, Guozhong Han, Kai College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha410073 China Nanhu Laser Laboratory National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781510683068

The rapidly developing non-line-of-sight (NLOS) imaging technology in recent years is capable of intelligent visual perception of concealed targets, holding broad application prospects in security, emergency rescue, autonomous driving, etc. Compared to active methods, passive NLOS imaging is promising to in real-world scenarios due to its low cost. This paper uses the long-wave infrared (LWIR) to detect multiple hidden targets. In contrast to the visible band, LWIR exhibits a higher proportion of specular reflection scattering on common relay surface but cannot represent details such as texture. Furthermore, passive NLOS imaging reconstruction is an ill-posed problem, leading to sparse and blurred features, which poses significant challenges for multi-target detection tasks. To address this, the paper proposes a deep learning method for collaborative multi-task image reconstruction and detection. The detection loss is backpropagated and fused with the imaging enhancement loss to guide the NLOS target reconstruction process towards high-quality detection results. Comparative experiments are conducted in multi-person target scenarios between the latest target detection methods and our method. The results indicate that our proposed method exhibits the best performance in terms of detection accuracy, recall rate, and the F1-score. Additionally, this paper demonstrates the generalization of the proposed method at different distances ranging from 10 to 20 meters. The related results provide data and methodological support for the advancement of NLOS imaging towards practical applications. © 2024 SPIE.

关键词： image reconstruction

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Reversible data Hiding in Encrypted image Based on Block and Huffman Encoding 2nd

Reversible Data Hiding in Encrypted Image Based on Block and...

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2nd International conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Yang, Cheng-Hsing Weng, Chi-Yao Huang, Bo-Han Huang, Wei-Qing Department of Computer Science and Artificial Intelligence National Pingtung University Pingtung900 Taiwan Department of Computer Science and Information Engineering National Chiayi University Chiayi600 Taiwan

ISBN: (纸本)9789819777853

The paper presents a reversible data hiding method with image compression consisting of Huffman coding and block encrypted. In the proposed approach, the image is used to compressed for generating the specified space for hiding the secret messages. When the secret message is extracted from the image, the location of the secret message is obtained through the image information hiding structure and Huffman coding table. After message extracting and image decryption, the image could be recovered to the original image. Experimental results show that our proposed method can achieve image recovering and have better hiding payload than that of related works. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： data hiding Huffman code image encryption Reversible data hiding

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Secure Personal Identification through Fundus image-Based Transfer Learning 2

Secure Personal Identification through Fundus Image-Based Tr...

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2nd International conference on Electrical Engineering and Automatic Control (ICEEAC)

作者： Aicha, Mokhtari Eddine, Hadj Slimane Zine Univ Abou Bekr Belkaid Tlemcen Fac Technol Biomed Engn Dept Tilimsen 13000 Algeria

ISBN: (纸本)9798350349740;9798350349757

Biometric identification systems are very important in security. It is easy for an intruder to collect someone's fingerprints, facial features, iris patterns, or voice recognition and then inject it into a biometric identification system. It points out a potential vulnerability in traditional biometric methods. The purpose of this study is to use fundus images to create secure and reliable digital templates for personal identification. We use transfer learning to identify individuals based on fundus images. Transfer learning involves leveraging knowledge gained from one task to improve performance on a related. It enhance the efficiency and effectiveness of machine learning models. We use RIDB (Retinal Identification data Base), which are publicly available databases. The proposed scheme provides a higher accuracy rate of 99.998% for RIDB. This suggests that the developed model is highly effective in accurately identifying individuals based on their retinal characteristics, presenting its potential as a secure and reliable method for personal identification.

关键词： biometric identification fundus images transfer learning

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Superior Attribute Weighted Set for Object Skeleton Detection using ResNet50 with Edge based Segmentation Model 2

Superior Attribute Weighted Set for Object Skeleton Detectio...

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2nd International conference on Sustainable Computing and Smart Systems (ICSCSS)

作者： Narayana, V. Lakshman Vinayaki, K. Vaishnavi Swetha, P. Ayyar Sri, K. Divya Chaithanya, G. Vignans Nirula Inst Technol & Sci Women Dept Comp Sci & Engn Peda Palakaluru Rd Guntur 522009 Andhra Pradesh India

ISBN: (纸本)9798350391558;9798350379990

Object detection is a method used in computer vision for identifying specific items inside an image or video. Most effective object detection systems make use of machine learning or deep learning. Object detection is a method of computer vision that allows us to find specific things in pictures and videos. Labeling and counting items in a scene, as well as pinpointing their locations and following their movement, are all possible because to object detection's ability to precisely identify and localize them. For instance, it is easy to recognize circles as a distinct class because of their shared characteristic of being spherical. These unique characteristics are used for object class recognition. Facial traits like as skin tone and eye distance are employed in a manner analogous to that used for fingerprinting in order to positively identify a person by their face. The object detection task is typically made much more challenging due to the test images being sampled from a distinct data distribution. Many unsupervised domain adaptation approaches have been presented to solve the difficulties introduced by the discrepancy between the domains of the training and test data. Cross-domain object detection has many applications, including autonomous driving because to the ease with which labels can be generated for a large number of scenes in video games. Object detection methods can be categorized as either neural network-based or non-neural. This research presents a Superior Attribute Weighted Set for Object Skeleton Detection using ResNet50 (SAWS-OSD-ResNet50). The proposed model when compared with the traditional methods performs better in object detection.

关键词： image Segmentation Object Detection Computer Vision image Annotation Face detection ResNet 50

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