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检索条件"任意字段=Conference on Image Extraction, Segmentation, and Recognition"

共 5366 条记录，以下是1-10 订阅

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SBC-UNet: A Network Based on Improved Hourglass Attention Mechanism and U-Net for Medical image segmentation 7th

SBC-UNet: A Network Based on Improved Hourglass Attention Me...

引用

7th Chinese conference on Pattern recognition and Computer Vision

作者： Wane, Yiyi Su, Jia Gan, Guoxi Zhang, Shenmeng Capital Normal Univ Informat Engn Coll Beijing 100048 Peoples R China

ISBN: (纸本)9789819784981;9789819784998

Accurate image segmentation plays a crucial role in the development of computer-aided diagnosis. The U-Net architecture based on Convolutional Neural Network (CNN) is widely adopted, but the locality of convolutional operations limits its ability to process global information, and Transformer can generate feature representations with more contextual relevance. To further explore the impact of the fusion of CNN and Transformer on medical image segmentation, we propose a network called SBC-UNet, which integrates the SBCFormer into the U-Net architecture. Specifically, we use SBCFormer as the core of the encoder, and the improved hourglass attention in SBCFormer reduces the impact of local images detail loss caused by low resolution, which is conducive to learning complete contextual information. We introduce Convolutional Block Attention Module (CBAM) and Multi-Path Feature extraction Module (MFEM) on the basis of the primordial decoder, enhancing the comprehensiveness of features. Extensive experiments on Synapse multiorgan segmentation dataset and ISIC2018 Challenge dataset show the effectiveness of our model compared to the classical methods.

关键词： SBC-UNet center dot Convolutional Neural Network center dot Transformer center dot Medical image segmentation

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VATBoost-Net: Integrating Enhanced Feature Perturbation and Detail Enhancement for Medical image segmentation 7th

VATBoost-Net: Integrating Enhanced Feature Perturbation and ...

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7th Chinese conference on Pattern recognition and Computer Vision

作者： Liu, Baichen Cai, Jiaxin Zhu, Shunzhi Xiamen Univ Technol Xiamen Peoples R China

ISBN: (纸本)9789819784950;9789819784967

Medical image segmentation is of great help to clinical practice. However, due to the high annotation costs and substantial time requirements for medical images, fully supervised medical image segmentation models often suffer from insufficient data volume,leading to poor performance. To tackle the annotation challenge of medical image segmentation, we introduce a semi-supervised learning algorithm called vatMatch, which relies on strong-weak consistency perturbation. It leverages partially annotated data to guide the training on unlabeled data and can achieve results comparable to fully supervised learning. Additionally, due to the uneven distribution of feature information across various channels in medical images, the existing mainstream methods based on Transformers are not effective in extracting feature information from individual image channels, and typically incur significant computational resource usage during the feature extraction process. To extract more feature information across different channels using minimal computational resources, we propose a Pixel Fusion Module (PFA) and an Adaptive Large Kernel Convolution (ALK) to enhance the ability of feature information extraction. By integrating PFA, ALK, and vatMatch, we propose a detail-enhanced attention network called VATBoost-Net, which not only addresses the annotation challenge but also enhances the capability of extracting image information features. Experimental results demonstrate the effectiveness of VATBoost-Net. Especially, our method outperforms the current state-of-the-art (SOTA) semi-supervised learning segmentation method UniMatch on the ACDC dataset.

关键词： Semi-supervised learning Medical image segmentation Feature perburbation

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recognition of Arc Magnetic Blow Angle of FFP-TIG Based on Semantic segmentation 30th

Recognition of Arc Magnetic Blow Angle of FFP-TIG Based on S...

引用

30th International conference on Computational and Experimental Engineering and Sciences

作者： Zhou, Danhuan Dong, Ying Li, Liuyi Zuo, Baojun Zhang, Qin Wang, Zhenmin South China Univ Technol Sch Mech & Automot Engn Guangzhou Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9783031816758;9783031816734;9783031816727

In the manufacturing process of bathyscaphe, the uneven magnetic field around the arc during welding of high-strength low-alloy steel is easy to induce magnetic bias, which damages the welding quality and affects the pressure resistance and service life of bathyscaphe. Therefore, the accurate identification of arc magnetic blow is the key to ensure the welding quality, and it is the premise of implementing effective control measures. However, the traditional image processing technology needs to adjust the parameters when processing images under different conditions, which limits its applicability in changeable environments. To solve this problem, this paper proposes a deep learning-based arc blow angle extraction algorithm to improve the accuracy and robustness of arc magnetic blow identification. First of all, a welding arc monitoring platform is built, and the arc image is collected by this platform, and the welding arc blowing image data set is constructed. Secondly, the semantic segmentation model of welding arc based on MobileNetv3 improved UNet network is established, and an algorithm of blow angle extraction based on semantic segmentation output is proposed. Finally, interpretable analysis is carried out to verify the validity of the model decision. The experimental results show that the proposed method can effectively identify the welding arc deflection Angle, achieving 6.24FPS and 94.7%mIoU. The proposed method lays a foundation for the on-line correction of arc magnetic blow, and has important practical significance for ensuring the manufacturing quality of bathyscaphe.

关键词： Arc image recognition Arc magnetic blow Semantic segmentation UNet FFP-TIG

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FPSNet: Focus-Perceptual-Semantic Full Flow Visual Redundancy Predicting for Camera image 7th

FPSNet: Focus-Perceptual-Semantic Full Flow Visual Redundanc...

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7th Chinese conference on Pattern recognition and Computer Vision

作者： Xiao, Xiongwei Hubei Wuhan China

ISBN: (纸本)9789819786916;9789819786923

With the rapid popularization of electronic devices, the large amount of data generated by camera imaging poses a huge challenge to the limited storage capacity and communication bandwidth. Achieving higher compression ratios without sacrificing visual quality remains a fundamental challenge for image compression. In this paper, we propose a novel full flow bidirectional visual threshold estimation method for camera imaging perceptual compression. Specifically, we study the features from camera imaging to visual perception to semantic understanding, and characterize them with focus identification, perceptual distribution, and semantic segmentation respectively. We also carefully design feature extraction networks suitable for each feature type. In addition, we draw inspiration from the bidirectional perceptual mechanism of the human visual system and propose a feature extraction framework that adopts top-down and bottom-up methods. We further enhance our model by regulating and fusing bidirectional perceptual features through a gated decoding structure. Extensive experimental validation on benchmark datasets confirms that our FPSNet significantly improves the accuracy of visual redundancy prediction.

关键词： Camera image Visual redundancy image compression

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Transformer image Quality Assessment Based on Multi-directional Feature extraction 7th

Transformer Image Quality Assessment Based on Multi-directio...

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7th Chinese conference on Pattern recognition and Computer Vision

作者： Zhao, Wenqing Lil, Mengwei Ma, Yumei North China Elect Power Univ Dept Comp Baoding Peoples R China North China Elect Power Univ Hebei Key Lab Knowledge Comp Energy & Power Baoding Peoples R China North China Elect Power Univ Minist Educ Engn Res Ctr Intelligent Comp Complex Energy Syst Baoding Peoples R China

ISBN: (纸本)9789819785049;9789819785056

Transformer image quality is influenced by various factors, including acquisition equipment and external environmental conditions. There is an urgent need for a quality evaluation method to assess transformer image quality, particularly to enhance the accuracy of transformer oil leakage identification tasks. Addressing this issue, this paper introduces image quality evaluation to the field of transformers for the first time and proposes a novel model called Multi-Directional Feature extraction Transformer image Quality Assessment (MFE-TIQA) for automatic evaluation of large-scale transformer images. MFE-TIQA comprises a main branch and a sub-branch. The main branch utilizes convolutional neural networks to extract multi-scale features from transformer images and employs a multi-directional feature extraction module to capture fine details. Meanwhile, the sub-branch employs a super-pixel segmentation model to generate local visual information from transformer images. Subsequently, a multi-branch information fusion module is constructed to comprehensively integrate the information from both branches, enhancing the model's focus on critical details. Furthermore, comparative experiments are conducted on a self-constructed transformer image dataset and a publicly available dataset to validate the effectiveness of the proposed model. This research provides foundational evidence for subsequent transformer oil leakage identification and detection efforts.

关键词： No-Reference image Quality Assessment Deep Learning Transformer Oil Leakage Multi-directional Feature extraction

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ODC-SA Net: Orthogonal Direction Enhancement and Scale Aware Network for Polyp segmentation 7th

ODC-SA Net: Orthogonal Direction Enhancement and Scale Aware...

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7th Chinese conference on Pattern recognition and Computer Vision

作者： Xu, Chenhao Zhang, Yudian Xu, Kaiye Zhu, Haijiang Beijing Univ Chem Technol CIST Beijing 100029 Peoples R China

ISBN: (纸本)9789819784950;9789819784967

Accurate polyp segmentation is crucial for the early detection of colorectal cancer. However, existing polyp detection methods sometimes ignore multi-directional features and the drastic scale changes of concealed targets. To address these challenges, we design an Orthogonal Direction Enhancement and Scale Aware Network (ODC-SA Net) for polyp segmentation. The Orthogonal Direction Convolutional (ODC) block can extract multi-directional features using transposed rectangular convolution kernels through forming sets of orthogonal feature vector basis, which solves the issue of random feature direction changes. Additionally, the Multi-scale Fusion Attention (MSFA) mechanism is proposed to emphasize scale changes in both spatial and channel dimensions, enhancing the segmentation accuracy for polyps of varying sizes. extraction with Re-attention (ERA) module is used to re-combine effective features, and Shallow Reverse Attention (SRA) mechanism is used to enhance polyp edge with low level information. A large number of experiments conducted on public datasets have demonstrated that the performance of this model is superior to state-of-the-art methods.

关键词： Polyp segmentation Colonoscopy Medical image processing Computer vision

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Bridge the Gap of Semantic Context: A Boundary-Guided Context Fusion UNet for Medical image segmentation 7th

Bridge the Gap of Semantic Context: A Boundary-Guided Contex...

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7th Chinese conference on Pattern recognition and Computer Vision

作者： Chen, Yu Wu, Jiahua Wang, Da-Han Zhang, Xinxin Zhu, Shunzhi Xiamen Univ Technol Sch Comp & Informat Engn Xiamen 361024 Peoples R China Fujian Key Lab Pattern Recognit & Image Understan Xiamen 361024 Peoples R China

ISBN: (纸本)9789819784981;9789819784998

Accurate medical image segmentation of lesion areas is crucial in assisting diagnostics and treatment planning of diseases. In this paper, we propose a boundary-guided context fusion U-Net(BCF-UNet) for medical image segmentation to address a significant problem with existing approaches: ignoring the relationships between the boundary information and semantic context. In the BCF-UNet, we propose a novel feature fusion module, named Semantic Context-based Adaptive Fusion Module (SCAFM), to incorporate the different levels of encoder outputs and transfer them to the decoder. The SCAFM allows our network to fuse the deepest semantic information with the shallow edge information to greatly bridge the gap between different scale features. Then, in each layer of the encoder sub-network, we propose a newly pyramid edge enhancement module with edge-related prior knowledge to obtain the edge multi-granularity information, which enhances the accuracy of the segmentation boundary. In the BCF-UNet, edge information is peculiarly utilized to enhance the capability of semantic context extraction, which helps the model better understand and recognize the relationship between different tissue structures. The result of extensive experiments on two datasets shows that the proposed BCF-UNet outperforms stateof-art techniques.

关键词： Medical image segmentation center dot Adaptive Feature Fusion center dot Edge Enhancement center dot U-Net

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InjectionNet: Realizing Information Injection for Medical image segmentation with Layer Relationships 27th

InjectionNet: Realizing Information Injection for Medical Im...

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27th International conference on Pattern recognition, ICPR 2024

作者： Zhu, Xinyu Li, JiaFeng Wen, Yin East China Normal University Shanghai China

ISBN: (纸本)9783031783111

In current medical image segmentation tasks, the combined transformer and convolutional architectures excel in capturing global cues and local details, but still pose two main concerns from a layer-level perspective: (1) intra-layer issue: the existing methods inefficiently obtain and fuse global-local information, potentially resulting in incomplete feature extraction;(2) inter-layer issue: the most of methods follow the classical U-shape structure, which inevitably leads to information weakening in the encoder-decoder. In light of these, we propose InjectionNet from the perspective of layers, mainly comprising the Intra-layer Global-Local Injection (GLI) module and Inter-layer Weight Injection (WI) modules. GLI employs multi-scale convolution for local information extraction and flexibly uses a multi-head self-attention mechanism for efficiently capturing global information and fusing them effectively. WI enhances information transfer by injecting generated feature weights, with different variants to suit various network stages. Extensive experiments on three medical imaging public datasets demonstrate the superior performance of InjectionNet compared to previous works. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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Face Mask Detection in Real Time using Machine Learning Algorithms 3

Face Mask Detection in Real Time using Machine Learning Algo...

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3rd International conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kavitha, R. Sultania, Tanishq Padhmashri, V. Shreya, A. Kumaraguru College of Technology Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798331509675

Coronavirus pandemic caused by a deadly virus that rapidly spread worldwide, necessitated the usage of face mask to minimize the airborne transmission of the virus. An automated face mask recognition system has made it easier to identify if an individual is wearing a face mask or not. HAAR-CASCADE and ResNet10 algorithms are applied for initial face recognition and then Mobilenet V2 and VGG16 algorithm are employed for recognizing face masks in gatherings. Amongst the stages involved in accomplishing the AI training are the pre-processing augmenting data, training, testing, and segmenting images. During each step, the CNN algorithms are utilized to generate an image segmentation of the input dataset highlighting those wearing masks and those without mask. The model is then trained with dataset, Mobilenet V2 yields an accuracy of 96.6% and VGG16 yield an accuracy of 99.42% in detecting individuals with and without wearing masks. VGG16's deep architecture, feature extraction and transfer learning abilities enable it to be highly precise and effective. However, VGG16 is highly resource intensive and needs extensive computation time. © 2025 IEEE.

关键词： image segmentation

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GPU-Accelerated Feature extraction for Real-Time Vision AI and LLM Systems Efficiency: Autonomous image segmentation, Unsupervised Clustering, and Smart Pattern recognition for Scalable AI Processing with 6.6× Faster Performance, 2.5× Higher Accuracy, and UX-Centric UI Boosting Human-in-the-Loop Productivity

GPU-Accelerated Feature Extraction for Real-Time Vision AI a...

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IEEE/SEMI conference and Workshop on Advanced Semiconductor Manufacturing

作者： Kiarash Ahi Stewart Wu Satya Sriram Germain Fenger Siemens EDA USA Siemens EDA Belgium Siemens EDA India

ISBN: (数字)9798331531850

ISBN: (纸本)9798331531867

The high computational cost of digital image processing, requiring high-performance hardware and extensive resources, severely limits real-time applications. While advancements in algorithm design and GPU acceleration have significantly improved efficiency, modern AI-driven applications such as large language models (LLMs), Generative AI (GenAI), medical imaging, autonomous vehicle perception, photography, advanced nano-scale semiconductor metrology, satellite image analysis, high-precision manufacturing, robotics, and real-time anomaly detection, still demand further optimization to reduce computational overhead and improve *** this paper, we introduce GPU-Accelerated Feature extraction to enhance runtime and efficiency in edge-based simulations. Our approach leverages AI-driven clustering, grouping images with similar visual and pattern characteristics to enable adaptive tuning on a small subset before generalizing across the full dataset. This method achieves a 3.78× reduction in ***, rather than processing an entire image, we recognize and extract a single representative pattern or region of interest (ROI) per image, removing redundant data and background noise. This refinement results in an additional 1.74× runtime improvement, culminating in an overall 6.6× speed boost, enhancing Scalable Real-Time AI Processing. We also demonstrated that with a similar runtime, the accuracy achieved is 2.5× *** solution, integrated into Calibre SEMSuite™, supports multicloud and real-time deployment for enhanced scalability, usability, and performance, providing users with a powerful tool for fully automated, AI-driven image classification, making high-throughput image review feasible even at the scale required for cutting-edge *** performance gains, this approach introduces autonomous data cleaning, anomaly detection and defect identification mechanism, allowing failed patterns and defective images to be identified without h

关键词： Accuracy Runtime image edge detection Clustering algorithms Feature extraction Real-time systems Computational efficiency Artificial intelligence Anomaly detection Tuning

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