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检索条件"任意字段=IEEE Symposium on Computational Intelligence for Image Processing"

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Morphological image Analysis and Feature Extraction for Reasoning with AI-Based Defect Detection and Classification Models

Morphological Image Analysis and Feature Extraction for Reas...

引用

ieee symposium Series on computational intelligence (SSCI)

作者： Jiajun Zhang Georgina Cosma Sarah Bugby Axel Finke Jason Watkins Dept of Computer Science School of Science Loughborough University UK Dept of Physics School of Science Loughborough University UK Dept of Mathematical Sciences School of Science Loughborough University UK Railston & Co. Ltd. Nottingham UK

As the use of artificial intelligence (AI) models becomes more prevalent in industries such as engineering and manufacturing, it is essential that these models provide trans-parent reasoning behind their predictions. This paper proposes the AI-Reasoner, which extracts the morphological characteristics of defects (DefChars) from images and utilises decision trees to reason with the DefChar values. Thereafter, the AI-Reasoner exports visualisations (i.e. charts) and textual explanations to provide insights into outputs made by masked-based defect detection and classification models. It also provides effective mitigation strategies to enhance data pre-processing and overall model performance. The AI-Reasoner was tested on explaining the outputs of an IE Mask R-CNN model using a set of 366 images containing defects. The results demonstrated its effectiveness in explaining the IE Mask R-CNN model's predictions. Overall, the proposed AI-Reasoner provides a solution for improving the performance of AI models in industrial applications that require defect analysis.

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Wildfire smoke detection using computational intelligence techniques

Wildfire smoke detection using computational intelligence te...

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ieee International Conference on computational intelligence for Measurement Systems and Applications (CIMSA)

作者： Angelo Genovese Ruggero Donida Labati Vincenzo Piuri Fabio Scotti Department of Information Technology Università degli Studi di Milano Milan Italy

In this paper, we propose an image processing system for the detection of wildfire smoke based on computational intelligence techniques and capable of adapting to different applicative environments. The proposed system is designed for processing with limited computational complexity. The detection process focuses on the extraction of specific features of wildfire smoke. A computational intelligence classifier is adopted to identify the presence of smoke. In order to test its effectiveness, the proposed system has been tested with low quality frame sequences, providing the capability to deal also with low cost cameras. The results indicate that the proposed approach is accurate and can be effectively applied in different environmental conditions.

关键词： Feature extraction image color analysis Delta modulation Artificial neural networks Shape Machine vision image edge detection

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Super-Resolution using Regularized Orthogonal Matching Pursuit based on compressed sensing theory in the wavelet domain

Super-Resolution using Regularized Orthogonal Matching Pursu...

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ieee International symposium on computational intelligence in Robotics and Automation (CIRA)

作者： Tingting Li College of Mathematics and Physics Chongqing University Chongqing China

ISBN: (纸本)9781424448081

We proposed a compressed sensing Super Resolution algorithm based on wavelet. The proposed algorithm performs well with a smaller quantity of training image patches and outputs images with satisfactory subjective quality. It is tested on classical images commonly adopted by Super Resolution researchers with both generic and specialized training sets for comparison with other popular commercial software and state-of-the-art methods. Experiments demonstrate that, the proposed algorithm is competitive among contemporary Super Resolution methods.

关键词： Matching pursuit algorithms Compressed sensing Wavelet domain Strontium image resolution Pixel Degradation Wavelet transforms Layout Microscopy

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nnMobileNet: Rethinking CNN for Retinopathy Research

nnMobileNet: Rethinking CNN for Retinopathy Research

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ieee/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhu, Wenhui Qiu, Peijie Chen, Xiwen Li, Xin Lepore, Natasha Dumitrascu, Oana M. Wang, Yalin Arizona State Univ Sch Comp & Augmented Intelligence Tempe AZ 85287 USA Washington Univ St Louis McKeley Sch Engn St Louis MO USA Clemson Univ Sch Comp Clemson SC USA Childrens Hosp Los Angeles Dept Radiol CIBORG Lab Los Angeles CA USA Mayo Clin Dept Neurol Rochester NY USA

ISBN: (纸本)9798350365474

Over the past few decades, convolutional neural networks (CNNs) have been at the forefront of the detection and tracking of various retinal diseases (RD). Despite their success, the emergence of vision transformers (ViT) in the 2020s has shifted the trajectory of RD model development. The leading-edge performance of ViT-based models in RD can be largely credited to their scalability-their ability to improve as more parameters are added. As a result, ViT-based models tend to outshine traditional CNNs in RD applications, albeit at the cost of increased data and computational demands. ViTs also differ from CNNs in their approach to processing images, working with patches rather than local regions, which can complicate the precise localization of small, variably presented lesions in RD. In our study, we revisited and updated the architecture of a CNN model, specifically MobileNet, to enhance its utility in RD diagnostics. We found that an optimized MobileNet, through selective modifications, can surpass ViTbased models in various RD benchmarks, including diabetic retinopathy grading, detection of multiple fundus diseases, and classification of diabetic macular edema. The code is available at https:// ***/ Retinal-Research/NN-MOBILENET

关键词： Classification CNN Diabetic Retinopathy grading Fundus image Medical image MobileNet Retinal Diseases

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An Enhanced SqueezeNet Based Network for Real-Time Road-Object Segmentation

An Enhanced SqueezeNet Based Network for Real-Time Road-Obje...

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ieee symposium Series on computational intelligence (SSCI)

作者： Chao Li Xian Wei Hui Yu Jielong Guo Xuan Tang Yuxuan Zhang Chinese Academy of Sciences Quanzhou Institute of Equipment Manufacturing Haixi Institutes Quanzhou China Chinese Academy of Science Fujian Institute of Research on the Structure of Matter Qaunzhou China Shanghai Jiao Tong University School of Aeronautics and Astronautics Shanghai China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Point cloud image segmentation plays an important role in self-driving. SqueezeSeg network has good performance in terms of accuracy and calculation speed on point cloud segmentation. However, potential details might be lost during the computational processing of SqueezeSeg and other similar kinds of networks. In this work, we try to retain the detailed information of the image by combining PointSeg network and the conditional random field in order to capture more data information and improve the recall rate. These two processes can complement and fully play their respective advantages. The proposed method has been tested on KITTI dataset. Simulation results demonstrate that our method can overcome the shortcomings of the SqueezeSeg network and similar kinds of networks on the extraction of detailed information.

关键词： Three-dimensional displays image segmentation Kernel Semantics Convolution Sensors Fires

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A novel artificial fish swarm algorithm for pattern recognition with convex optimization

A novel artificial fish swarm algorithm for pattern recognit...

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International Conference on Communication and Electronics Systems (ICCES)

作者： Shi, Lei Guo, Rui Ma, Yuchen Peking Univ Beijing Peoples R China Macquarie Univ N Ryde NSW Australia Hong Kong Baptist Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781509010660

image pattern recognition is an important area in digital image processing. An efficient pattern recognition algorithm should be able to provide correct recognition at a reduced computational time. Off late amongst the machine learning pattern recognition algorithms, Artificial fish swarm algorithm is one of the swarm intelligence optimization algorithms that works based on population and stochastic search. In order to achieve acceptable result, there are many parameters needs to be adjusted in AFSA. Among these parameters, visual and step are very significant in view of the fact that artificial fish basically move based on these parameters. In standard AFSA, these two parameters remain constant until the algorithm termination. Large values of these parameters increase the capability of algorithm in global search, while small values improve the local search ability of the algorithm. In this paper, we empirically study the performance of the AFSA and different approaches to balance between local and global exploration have been tested based on the adaptive modification of visual and step during algorithm execution. The proposed approaches have been evaluated based on the four well-known benchmark functions. Experimental results show considerable positive impact on the performance of AFSA. A Convex optimization has been integrated into the proposed work to have an ideal segmentation of the input image which is a MR brain image.

关键词： image segmentation fish school algorithms convex optimization recognition rates

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Cost Optimization for In-Network Computing Services Deployment in Edge-Cloud Networks 7

Cost Optimization for In-Network Computing Services Deployme...

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7th World Conference on Computing and Communication Technologies, WCCCT 2024

作者： Liu, Xiaorong Meng, Sa Zheng, Danyang Southwest Jiaotong University School of Computing and Artificial Intelligence China

ISBN: (纸本)9798350372328

In edge-cloud computing networks, clients can conveniently offload their computation tasks to the servers along the traffic flow, the process of which is known as in-network computing (INC). Notably, within the landscape of cutting-edge artificial intelligence, big data, and virtualization techniques, the computational demands of an INC service can range from relatively modest demands, for instance, image processing, to highly demanding tasks, such as generative pre-training. Since the edge-cloud computing networks' pricing structure is associated with computational resources availability and the service location, a compelling quandary emerges concerning the deployment of INC services in edge-cloud networks. In this work, our focal point is the optimization of the cost-effectiveness pertaining to the deployment of INC services in edge-cloud computing networks. To begin, we mathematically formalize the In-Network Computing services Deployment (INCD) problem with the objective of cost optimization. In addressing the INCD problem, we propose an efficient heuristic algorithm known as Optimal In-Network Computing Services Deployment (Opt-INCD), which is proved to be optimal when deploying an INC service. Extensive simulation results show that the proposed Opt-INCD algorithm outperforms the greedy-based benchmarks by an average of 35.42% and 43.46%, respectively. © 2024 ieee.

关键词： Cost effectiveness

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Identification of F117 fighter with image processing by using Labview

Identification of F117 fighter with image processing by usin...

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ieee International Conference on computational intelligence and Computing Research

作者： H. Ghayoumi Zadeh H. Goodarzi Dehrizi J. Haddadnia Engineering Faculty Sabzevar Tarbiat Moallem University Sabzevar Razavi Khorasan Iran

In this paper, a method for tracking (identifying) the fighter F117 is introduced. Because of its individual design and also the material that is used to build the fighter body, it cannot be identified and tracked with the conventional radars. In this work, an operational method based on image processing and by using LabView software is presented. By extraction the special geometrical properties of this fighter, an accurate and high speed tracking system is introduced.

关键词： Entropy Airplanes Pixel Cameras image processing Radar tracking Uncertainty

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On decomposing 3D packing problems in wooden furniture industry

On decomposing 3D packing problems in wooden furniture indus...

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ieee International symposium on computational intelligence in Robotics and Automation (CIRA)

作者： J. Hassamontr Department of Production Engineering King Mongkut's Institute of Technology North Bangkok Bangkok Thailand

This paper discusses the development of a cutting plan for three-dimensional wooden raw material pieces. Practical requirements, particularly those in wooden furniture industry, are addressed. Such considerations include the use of leftover raw material pieces and parts' color requirements. Similar parts are packed over the cross sectional area of raw material pieces first. Then the packing pattern along the length of raw material piece is considered. The material utilization criteria is used to screen out inefficient cutting patterns. In order to handle large problem sizes, the problem is decomposed into smaller lists of raw material available and parts required. Such approach not only makes the solution obtainable in reasonable time but also greatly reduces the problem size considered. A case study is used to illustrate the performance of the system developed. The program is currently used by a company that manufactures wooden picture frame. It has performed reasonably well in practice, especially in using leftover raw material pieces efficiently.

关键词： Wood industry Raw materials Manufacturing industries Costs image processing Production engineering Pulp manufacturing Manufacturing processes Assembly Mass customization

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Twin Branch Transformers-Based Re-Ranking for image Retrieval 21

Twin Branch Transformers-Based Re-Ranking for Image Retrieva...

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21st ieee International symposium on Parallel and Distributed processing with Applications, 13th ieee International Conference on Big Data and Cloud Computing, 16th ieee International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Wang, Ziqi Wang, Wei Institute of Information Engineering Chinese Academy of Sciences Beijing China Shenzhen Campus of Sun Yat-sen University School of Cyber Science and Technology Shenzhen China

ISBN: (纸本)9798350329223

image retrieval as an essential task in computer vision aims to search images to match a given query image, which could be solved with a two-stage model: retrieving a series of candidate images with a global image descriptor;re-ranking the nearest images with local descriptors. Recent study indicates that Transformer technique could reduce the number of local descriptors for the re-ranking process, and enable the model to be in parallel. However, there still exist some problems: different input orders of an image pair will produce different outputs (non-symmetric);the mean aggregation module fails to find out the difference between two images. To this end, we propose a twin branch Transformers-based re-ranking approach (TBT-RR). Specifically, we utilize a cross-attention module with shared weights and segment embeddings to make the model symmetrical. Then, we design a learnable aggregation module to adaptively take the average or difference between two images, so that local descriptors could quickly reach an agreement on whether the two images are matched. To output similarity scores between two images, we design a similarity estimation pipeline with a one-way cross-attention module. Finally, we train TBT-RR on the Google Landmark dataset and test it on the Revisited Oxford and Revisited Paris datasets. The experimental results show that the retrieval results of TBT-RR outperform previous re-ranking approaches. Besides, with less memory usage and parameter scale, TBT-RR has 50% increase in the retrieval speed and 40% reduction in the computational complexity. © 2023 ieee.

关键词： image retrieval

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