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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems IV"

共 13657 条记录，以下是691-700 订阅

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A Comparison on Dental X-ray Segmentation based on Fuzzy Clustering for ADIS System

A Comparison on Dental X-ray Segmentation based on Fuzzy Clu...

引用

2022 International conference on Computing, Communication, Security and Intelligent systems, IC3SIS 2022

作者： Joy, Manoj T. Shan, B. Priestly Shaji, Liyan Grace Sathyabama Institute of Science Technology Computer Science Engineering Chennai India Chandigarh University Mohali Dean Academic Affairs Punjab India Amal Jyothi College of Engineering Kanjirappally India

ISBN: (数字)9781665468831

ISBN: (纸本)9781665468831

image segmentation in image processing is the method by which images are divided into different segments. Many methods are available and one of the commonly used method are fuzzy-based systems. This paper ignites light into different fuzzy clustering methods, used for the segmentation of dental radiographs. Fuzzy C-means (FCM), Semi-Supervised fuzzy clustering with spatial constraints (SSFC-SC) and Fuzzy Level Set Algorithm (SFC-LM) are discussed in this paper for the automated dental radiograph segmentation. The three algorithms give different performance rate based on time and accuracy. These algorithms are evaluated against a real dental radiograph dataset available, and this set contains panoramic and periapical radiographs. The proposed study help in identifying the strengths and weakness of the algorithms. From the comparisons, it is inferred that the SSFC-SC based systems fair better than its counterparts. © 2022 IEEE.

关键词： image segmentation

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Score Level Fusion Based on T-Norms Algorithm

Score Level Fusion Based on T-Norms Algorithm

引用

International conference on image and Signal processing and their Applications (ISPA)

作者： Hellal Leila Naceur-Eddine Boukezzoula Mohamed Cheniti LTS Laboratory University Ferhat Abbas Setif Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Multimodal biometric systems unite data from multiple biometric sources to surmount the limitations of individual systems. This paper presents an efficient algorithm for extracting details based on the image representation of fingerprints. Additionally, we conduct an in-depth investigation of T-Norm algorithms and offer a comprehensive score-level merging analysis. This analysis combines matching outcomes derived from the left and right fingerprints using the Hammacher, Schweizer-Sklar, Dombi, and Yager algorithms. The latter approaches are compared based on the receiver operating characteristic curve. The experimental evaluation conducted on the NIST fingerprint database validates the efficacy of fusion at the score level. Furthermore, it demonstrates the effectiveness of the proposed algorithm when compared to other T-Norms.

关键词： Databases Merging Signal processing algorithms Receivers Fingerprint recognition Signal processing NIST

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A comparative analysis of techniques for extracting features from the object in image processing 5

A comparative analysis of techniques for extracting features...

引用

5th IEEE International conference on Advanced systems and Emergent Technologies (IC_ASET)

作者： Hafsia, Touka Tlijani, Hatem Nouri, Khaled Polytechn Sch Tunisia Adv Syst Lab BP 743 La Marsa Tunisia Natl Engn Sch Tunis Automat Res Lab BP 37 El Manar campus Tunis Tunisia

ISBN: (纸本)9781665427357

Visual informations is very rich to control and pursue mobile robots, like wheeled mobile robots, underwater robots and aerospace mobile robots, etc. These are considered as mobile robots that move in different spaces, their main problem in this case is navigation especially in unknown environments. In effect, this navigation is possible only by the localisation and orientation of the robot by using different embedded sensors. We have the camera which is a necessary sensor in this work, it is an embedded instrument that gives very rich visual information as a sensor complementing the other sensors. In this context, the recognition of objects from visual informations is a main function among the functions very useful in image processing tasks due to its varied applications in the field of robotics. Based on the analysis of this informations and the determination of image features like color or shape or object primitives (points, lines, edges, etc.) or some other features. What interest us in this paper, various feature extraction techniques and classification of point and edge detection are discussed which are required for object recognition showing advantages and disadvantages of the selected algorithms. So, points and edge detection refers to the process of identifying and detecting sharp discontinuities in an image. In this work, we try to develop a novel algorithm using the work on the existing to create a novel detector.

关键词： image image processing Features Point Edge Detector

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Simulation Research and Development of Agricultural Products processing Process Based on Machine Learning Algorithm

Simulation Research and Development of Agricultural Products...

引用

2022 International conference on Artificial Intelligence and Autonomous Robot systems, AIARS 2022

作者： Peng, Luo Shihezi University School of Food Science and Technology Xinjiang Shihezi832003 China

ISBN: (数字)9781665454575

ISBN: (纸本)9781665454575

In recent years, with the development of science and technology and its application in agricultural production, China's agricultural science and technology have made great progress, the concept of "agricultural processing"has been mentioned, and the research on agricultural processing has also achieved fruitful results. The combination of intelligent and automated machine learning algorithms with traditional industries can promote productivity improvement on the one hand, and realize industrial upgrading and transformation on the other hand. However, in practical production applications, machine learning algorithms are restricted by factors such as high cost, and the research and application of machine learning algorithms are greatly limited. With the development of virtual simulation technology in the field of machine learning algorithm research, it provides a new way for machine learning algorithm technology to be applied to agricultural product processing. Therefore, the research on machine learning algorithms has become a trend. The development of machine learning algorithms will drive the development of modern agriculture. It is very necessary for the research of machine learning algorithms to learn algorithms. The image is converted into a data matrix, and a computer used to replace the human brain is used to analyze the image, while completing a vision related task. China's agricultural development is facing severe challenges such as rising costs, continuous deterioration of the ecological environment and high tension of resource conditions. With the deepening of machine learning algorithm research and the rapid development of machine learning algorithm technology, machine learning algorithm simulation technology, as a safe and economic experimental tool in the application of machine learning algorithm technology, plays a more and more important role. In order to make full use of the latest research results abroad and narrow the gap with the advanced level ab

关键词： Deterioration

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Contour Extraction of Surgical Stoma Surfaces Using 2.5D images from Smartphone 3D Scanning 26

Contour Extraction of Surgical Stoma Surfaces Using 2.5D Ima...

引用

26th IEEE Signal processing: algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Monroy, Francisco Luquin Hussein, Rania University of Washington Electrical and Computer Engineering Department Seattle United States

ISBN: (纸本)9798350304985

In this paper, we present a technique for extracting stoma outlines from 2.5D images acquired through smartphone-based 3D scanning. Accurate stoma outlining plays a crucial role in tailoring ostomy wafers, thereby minimizing stoma damage, preserving the health of surrounding skin, and ultimately enhancing the health-related quality of life for stoma patients. Our proposed method leverages a widely used contour algorithm and incorporates error analysis to assess its effectiveness and precision. Through a series of experiments, we investigate the potential and limitations of smartphone 3D scanning in achieving precise and personalized stoma contouring. Additionally, we introduce an error metric to evaluate the performance of the technique quantitatively. Our findings shed light on the possibilities for advancements in 3D scanning technology and image processing techniques, marking a significant step towards harnessing smartphone technology for improved ostomy management and patient care. © 2023 Division of Signal processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： image segmentation

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Research on Computer image Recognition Technology Based on Artificial Intelligence

Research on Computer Image Recognition Technology Based on A...

引用

International conference on systems and Informatics (ICSAI)

作者： Jing Li Yuxi Liu Yahong Ma Feng Liu School of Electronic Information Xijing University Xian China

ISBN: (数字)9798331510138

ISBN: (纸本)9798331510145

In order to solve the problem of fast processing for ultra-high-resolution images in embedded systems, this paper proposes a multi-mode tracking and recognition method based on embedded processing architecture, through the construction of a multi-GPU hardware processing system, the large-scale image parallel processing algorithm is studied, and a variety of image processing modes such as full-frame, full-window tracking and tracking scanning are designed, and experimental verification is carried out on ultra-high-resolution images. The multi-GPU architecture and large-scale image parallel processing algorithm described in this paper have certain advantages over traditional processing methods.

关键词： image recognition Software design image resolution Software algorithms Computer architecture Switches Parallel processing Hardware Artificial intelligence Informatics

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Hole repair of 3D terrain mesh in satellite stereo image reconstruction

Hole repair of 3D terrain mesh in satellite stereo image rec...

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2024 International conference on Remote Sensing, Mapping, and Geographic Information systems, RSMG 2024

作者： Bao, Qiwei Liu, Jun Liu, Xining Zhao, Limin Yuan, Zhanliang Chen, Xingfeng Li, Jiaguo Li, Huafu Zhang, Haixiang School of Surveying and Land Information Engineering Henan Polytechnic University Jiaozuo454003 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Sanya Institute of South China Sea Geology Guangzhou Marine Geological Survey China Geological Survey Sanya572024 China

ISBN: (纸本)9781510685826

When constructing a 3D surface grid using point cloud data closely matched with high-resolution satellite stereo images, holes often appear due to matching failures or limitations of triangulation algorithms, affecting the grid's integrity and the accuracy of subsequent applications. To solve this problem, a boundary contraction algorithm is studied for repairing mesh holes. The research focuses on different terrain conditions such as mountains, plains, and cities, and evaluates the effectiveness of boundary contraction algorithms in repairing holes. Using visual inspection methods, the quality of the repaired mesh is checked to ensure its integrity, smoothness, continuity, and topological correctness. The experimental results of GF-7 satellite images show that the boundary contraction algorithm can effectively fill holes in various terrain environments, significantly improving the geometric quality and overall integrity of the mesh, verifying its feasibility as a reliable mesh repair method, and providing strong support and verification for the application of the boundary contraction algorithm in actual terrain data processing. © 2024 SPIE.

关键词： Mesh generation

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A Machine Learning Framework for Skin Cancer Detection using Transfer Learning

A Machine Learning Framework for Skin Cancer Detection using...

引用

International image processing, Applications and systems conference (IPAS)

作者： Mary Linda Panakal Augustine Sotirios Spanogianopoulos Mhd Saeed Sharif Department of Computer Science and Digital Technologies School of Architecture Computing and Engineering University of East London London UK School of Electrical and Mechanical Engineering University of Portsmouth Portsmouth UK

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

This paper uses deep learning algorithms including InceptionV2, InceptionV3, DenseNet, MobileNet, and VGG19 to improve skin cancer detection. This research aims to improve skin cancer diagnosis. This work aims to determine the most effective evolutionary metrics-based technique to recognizing skin cancer, which is comparable to other diseases. Ultimately, our paper aims to create a realistic skin cancer detection system that uses the best deep learning algorithm. This discovery might improve medical diagnostics, leading to earlier diagnosis and improved healthcare outcomes.

关键词： Deep learning Machine learning algorithms image processing Transfer learning Medical diagnosis Reliability Skin cancer image classification

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GLNet: Low-light image enhancement via grayscale priors 13

GLNet: Low-light image enhancement via grayscale priors

引用

13th International conference on Signal processing systems, ICSPS 2021

作者： Guo, Li Xie, Junwei Xue, Yuyang Li, Ru Zheng, Weixin Tong, Tong Gao, Qinquan College of Physics and Information Engineering Fuzhou University Tsukuba Japan Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology Fuzhou University Tsukuba Japan Imperial Vision Technology Fujian China Department of Computer Science University of Tsukuba Tsukuba Japan

ISBN: (数字)9781510653184

ISBN: (纸本)9781510653177

Low-light images are generally produced by shooting in a low light environment or a tricky shooting angle, which not only affect people's perception, but also leads to the bad performance of some artificial intelligence algorithms, such as object detection, super-resolution, and so on. There are two difficulties in the low-light enhancement algorithm: in the first place, applying image processing algorithms independently to each low-light image often leads to the color distortion;the second is the need to restore the texture of the extremely low-light area. To address these issues, we present two novel and general approaches: firstly, we propose a new loss function to constrain the ratio between the corresponding RGB pixel values on the low-light image and the high-light image;secondly, we propose a new framework named GLNet, which uses the dense residual connection block to obtain the deep features of the low-light images, and design a gray scale channel network branch to guide the texture restoration on the RGB channels by enhancing the grayscale image. The ablation experiments have demonstrated the effectiveness of the proposed module in this paper. Extensive quantitative and perceptual experiments show that our approach obtains state-of-the-art performance on the public dataset. © COPYRIGHT SPIE.

关键词： image enhancement

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S2P: State-conditioned image Synthesis for Data Augmentation in Offline Reinforcement Learning 36

S2P: State-conditioned Image Synthesis for Data Augmentation...

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36th conference on Neural Information processing systems (NeurIPS)

作者： Cho, Daesol Shim, Dongseok Kim, H. Jin Seoul Natl Univ Automat & Syst Res Inst ASRI Seoul South Korea Seoul Natl Univ Interdisciplinary Program AI Seoul South Korea Seoul Natl Univ Artificial Intelligence Inst Seoul Natl Univ AIIS Seoul South Korea

ISBN: (纸本)9781713871088

Offline reinforcement learning (Offline RL) suffers from the innate distributional shift as it cannot interact with the physical environment during training. To alleviate such limitation, state-based offline RL leverages a learned dynamics model from the logged experience and augments the predicted state transition to extend the data distribution. For exploiting such benefit also on the image-based RL, we firstly propose a generative model, S2P (State2Pixel), which synthesizes the raw pixel of the agent from its corresponding state. It enables bridging the gap between the state and the image domain in RL algorithms, and virtually exploring unseen image distribution via model-based transition in the state space. Through experiments, we confirm that our S2P-based image synthesis not only improves the image-based offline RL performance but also shows powerful generalization capability on unseen tasks.

关键词： Reinforcement learning

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