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

共 5377 条记录，以下是11-20 订阅

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image Foreground segmentation Based on Small Data Set for Visual Servo Applications

Image Foreground Segmentation Based on Small Data Set for Vi...

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IEEE/ASME International conference on Advanced Intelligent Mechatronics (AIM)

作者： Luo, Yan Chen, Gaoming Liu, Chao Xiong, Zhenhua Shanghai Jiao Tong Univ Sch Mech Engn Shanghai Peoples R China

ISBN: (纸本)9781665476331

extraction of features is a key process in image-based visual servo. However, existing image processing methods are difficult to segment the target foreground and cannot overcome distracting factors, such as background and illumination, resulting in reduced accuracy of feature extraction. Therefore, target foreground segmentation is a critical problem in image-based visual servo tasks. In this paper, a method for image foreground segmentation and visual servo control based on small data training is proposed. Semantic segmentation is achieved by training a small number of images. Focusing on the target artefact region and blurring the background are also achieved to avoid its influence on feature recognition, especially for industry parts. It is shown that recognition and segmentation under different lighting conditions can be obtained, reducing the interference of lighting on visual servo. Experimental results show that the proposed method is effective in visual servo control applications.

关键词： Feature extraction foreground segmentation small data set visual servo

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End-to-End Unsupervised Style and Resolution Transfer Adaptation segmentation Model for Remote Sensing images 6th

End-to-End Unsupervised Style and Resolution Transfer Adapta...

引用

6th Chinese conference on Pattern recognition and Computer Vision (PRCV)

作者： Li, Zhengwei Wang, Xili Shaanxi Normal Univ Sch Comp Sci Xian Peoples R China

ISBN: (纸本)9789819984619;9789819984626

For remote sensing image unsupervised domain adaptation, there are differences in resolution except for feature differences between source and target domains. An end-to-end unsupervised domain adaptation segmentation model for remote sensing images is proposed to reduce the image style and resolution differences between the source and target domains. First, a generative adversarial-based style transfer network with residual connection, scale consistency module, and perceptual loss with class balance weights is proposed. It reduces the image style and resolution differences between the two domains and maintains the original structural information while transferring. Second, the visual attention network (VAN) that considers both spatial and channel attention is used as the feature extraction backbone network to improve the feature extraction capability. Finally, the style transfer and segmentation tasks are unified in an end-to-end network. Experimental results show that the proposed model effectively alleviates the performance degradation caused by different features and resolutions. The segmentation performance is significantly improved compared to advanced domain adaptation segmentation methods.

关键词： image segmentation Remote sensing image Unsupervised domain adaptation Feature and resolution differences

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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 ...

引用

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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Tongue body positioning and segmentation based on improved TransUNet

Tongue body positioning and segmentation based on improved T...

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2024 International conference on image, Signal Processing, and Pattern recognition, ISPP 2024

作者： Wang, Haowei Liang, Tiantian Shao, Yinping School of Automation and Electrical Engineering Dalian Jiaotong University Dalian China

ISBN: (纸本)9781510680425

This paper proposes an improved TransUNet medical image segmentation method for tongue positioning and segmentation to solve the premise of tongue image diagnosis task and the segmentation performance of deep learning network. By introducing efficient multi-scale attention module in the jump connection layer of the network, the image conducts deeper feature extraction and compensate for the spatial information lost by image dimension reduction. At the same time, an improved empty space convolution pooling pyramid is added to the convolutional neural layer of the network to enhance the network perception of the input image while reducing the network training cost. Facing the complex background conditions and a variety of interference problems, the network has excellent performance. © 2024 SPIE.

关键词： image segmentation

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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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Research on Salient Object Detection Algorithm Based on image Depth Information 5

Research on Salient Object Detection Algorithm Based on Imag...

引用

5th IEEE International conference on Pattern recognition and Machine Learning, PRML 2024

作者： Zeng, Peijie Zhou, Wei School of Artificial Intelligence Guangxi Minzu University Nanning China

ISBN: (纸本)9798350355925

This paper proposes a novel RGBD segmentation network structure named AsymFusion-FPDNet, aiming to improve the accuracy of RGBD segmentation by fully utilizing the rich semantic information of RGB images. Although significant progress has been made in existing RGBD segmentation methods, most of them primarily focus on the utilization of depth image information and the fusion of RGB and depth information, while overlooking the semantic information in RGB images. Therefore, based on the asymmetric multimodal structural network AsymFormer, we introduce dilated convolution blocks after the final downsampling operation of the RGB image feature extraction network. By gradually expanding the receptive field through dilated convolutions with different dilation factors, we extract more extensive feature information to enhance the representation capability of the network. Additionally, we incorporate a feature pyramid module and a CSPLayer_2Conv structure into the RGB image feature extraction network structure to improve the model's feature extraction ability in complex scenes, particularly by fusing spatial details and semantic information to enhance the model's comprehensive understanding and capture of targets. Experimental results show that AsymFusion-FPDNet achieves excellent performance on the NYUv2 dataset, with a MIoU of 56.91% and a PA of 79.78%, representing an improvement of 2.81% in MIoU and 1.29% in PA compared to the advanced AsymFormer model. This validates the effectiveness and advancement of the proposed model for RGBD segmentation tasks. © 2024 IEEE.

关键词： Semantic segmentation

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Research on Propeller Blades Sanding Degree recognition Algorithm Based on Improved DeepLabV3+Network 7

Research on Propeller Blades Sanding Degree Recognition Algo...

引用

7th International conference on Mechatronics, Robotics and Automation

作者： Cao, Siying Yu, Baocheng Xu, Wenxia Wuhan Inst Technol Coll Artificial Intelligence Coll Comp Sci & Engn Wuhan Peoples R China

ISBN: (纸本)9798350352481;9798350352474

In the overhaul process of airplane propeller blades, the foam filling on the shoulder of the blades needs to be ground off. To achieve automated recognition of the sanding degree of propeller blades, an image semantic segmentation algorithm based on the improved DeepLabV3+ network is proposed. The algorithm adopts a lightweight network, MobileNetV2, to replace the original backbone network of DeepLabv3+, reducing computational complexity. The atrous spatial pyramid pooling (ASPP) feature extraction module is improved by replacing the atrous convolution with depthwise separable convolution and introducing the Convolutional Block Attention Module(CBAM), enhancing the overall training efficiency and segmentation accuracy of the model. Group normalization (GN) is used instead of batch normalization (BN) to further improve model performance. The experimental results on the dataset collected from the propeller blades sanding site show that the proposed algorithm achieves a mean intersection over union (MioU) of 91.40%, a mean pixel accuracy (MPA) of 95.63%, and an inference speed (FPS) of 33.88. Compared to the current mainstream segmentation algorithms such as DeepLabV3+, PSPNet, and HRNet, the improved algorithm demonstrates enhanced segmentation accuracy and inference speed.

关键词： Airplane Propeller blades sanding image Semantic segmentation DeepLabV3+ Lightweight

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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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MIST: Medical image segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder

MIST: Medical Image Segmentation Transformer with Convolutio...

引用

IEEE/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Rahman, Md Motiur Shokouhmand, Shiva Bhatt, Smriti Faezipour, Miad Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9798350318920;9798350318937

One of the common and promising deep learning approaches used for medical image segmentation is transformers, as they can capture long-range dependencies among the pixels by utilizing self-attention. Despite being successful in medical image segmentation, transformers face limitations in capturing local contexts of pixels in multimodal dimensions. We propose a Medical image segmentation Transformer (MIST) incorporating a novel Convolutional Attention Mixing (CAM) decoder to address this issue. MIST has two parts- a pre-trained multi-axis vision transformer (MaxViT) is used as an encoder, and the encoded feature representation is passed through the CAM decoder for segmenting the images. In the CAM decoder, an attention-mixer combining multi-head self-attention, spatial attention, and squeeze and excitation attention modules is introduced to capture long-range dependencies in all spatial dimensions. Moreover, to enhance spatial information gain, deep and shallow convolutions are used for feature extraction and receptive field expansion, respectively. The integration of low-level and high-level features from different network stages is enabled by skip connection, allowing MIST to suppress unnecessary information. The experiments show that our MIST transformer with CAM decoder outperforms the state-of-the-art models specifically designed for medical image segmentation on the ACDC and Synapse datasets. Our results also demonstrate that adding the CAM decoder with a hierarchical transformer improves the segmentation performance significantly. Our model with data and code is publicly available on GitHub(1).

关键词： Algorithms Applications Biomedical / healthcare / medicine image recognition and understanding

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Rail surface defect detection using a U-Net convolutional neural network 4

Rail surface defect detection using a U-Net convolutional ne...

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4th International conference on Signal image Processing and Communication, ICSIPC 2024

作者： Sun, Qin Xiao, Shuhao He, Wei Zuo, Xueqian Yu, Mingyou Wuchang Shouyi University Wuhan430064 China

ISBN: (纸本)9781510682467

To address the challenges of high labor intensity and low efficiency in traditional manual inspection, the use of traditional pixel segmentation methods has issues such as algorithm incompatibility, stringent lighting condition consistency requirements, and a lack of robustness against interference. This paper introduces a U-Net convolutional neural network-based rail surface defect detection algorithm. Initially, multiple contraction blocks are employed to learn complex structures for feature extraction. Subsequently, two 3×3 and one 2×2 convolutional layers are utilized to form bottleneck sections and generate feature vectors. Subsequently, two 3×3 convolutional layers and a 2×2 upsampling layer form an expansion section to convert features into images. Finally, the required number of segmentation features are output through a 3x3 convolutional layer to produce a single-channel map. Experiments demonstrate that compared to traditional image segmentation methods, MIoU, FWIoU, PA, MPA, and BCE have improved by 26.7%, 8.7%, 8.4%, 11.7%, and 93.8% respectively. The introduction of skip connections in this method effectively automates the segmentation of surface image defects on steel rails, offering faster recognition speed, improved accuracy, and enhanced robustness. This approach meets the automation needs of lossless detection and holds promising applications in engineering practices. © 2024 SPIE.

关键词： image segmentation

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