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检索条件"任意字段=2021 International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2021"

共 691 条记录，以下是51-60 订阅

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Automatic Segmentation of Thyroid Nodule from Ultrasound images using Spatial-Channel Attentive U-Net 2

Automatic Segmentation of Thyroid Nodule from Ultrasound Ima...

引用

2nd international conference on advanced algorithms and signal image processing, aasip 2022

作者： Song, Shuang Liu, Linlin Yu, Ming-An Xiao, Ruoxiu School of Computer Science and Engineering Beihang University Beijing100091 China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Department of Interventional Medicine China-Japan Friendship Hospital Beijing100029 China Shunde Graduate School University of Science and Technology Foshan Beijing100024 China

ISBN: (纸本)9781510660571

The ultrasound image is a commonly used imaging modality to treat thyroid nodules due to its rapid imaging speed and the ability for multiple anatomical and soft-tissue visualization. However, considering the variability of the position, shape, size and intensity of nodules, the accurate segmentation of thyroid nodules is challenging. In this paper, we proposed an automatic segmentation network of thyroid nodules by combining fast-RCNN and spatial-channel attentive U-Net. In our model, fast-RCNN is firstly utilized to identify the rough position of nodules. The predicted boxes are then utilized to crop the image patches containing only nodules and then build a new database. Next, a spatial-channel attentive U-Net is designed and trained to realize the nodule segmentation. By comparing the proposed network with other state-of-the-art models, we can gain superior results in discarding similar non-nodule structures and preserving the boundary information of nodules. © 2022 SPIE.

关键词： image segmentation

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A feature extraction with pose-auxiliary method for Person Reidentification 2

A feature extraction with pose-auxiliary method for Person R...

引用

2nd international conference on advanced algorithms and signal image processing, aasip 2022

作者： Ma, ZePing Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu610054 China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China

ISBN: (纸本)9781510660571

Person re-identification is widely considered as a sub problem of image retrieval. It is a technology that uses computer vision technology to determine whether there is a specific pedestrian in an image or video, given a monitored pedestrian image, the pedestrian image under the cross device is retrieved. Many researches have been made to study how to match one people from two picture, but how to locating proper body parts has become a problem which could lead to misalign and wrongly divided sample. To solve this problem, we introduce a pose-auxiliary network to extract the position feature of pedestrian. We use a post estimation network to estimate the person's body location, then we decide whether it's a body part or a static obstacle by using a separated trained classifier. Then we compare these features from two picture and calculate their loss. Meanwhile, we use a graph-matching strategy to compare two pedestrians pose structure, which is also used into jugging the match problem. By using a multi-verification strategy, our method has some improvement than some previous study using Market-1501 and DukeMTMC-Reid datasets, but still remains room for improvement. © 2022 SPIE.

关键词： image retrieval

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Visible light polarization image fusion based on dense connection generative adversarial network

Visible light polarization image fusion based on dense conne...

引用

2021 international conference on advanced algorithms and signal image processing, aasip 2021

作者： Wang, Tao Cheng, Jianghua Liu, Tong Zhao, Kangcheng Cheng, Bang Liu, Zilong College of Electronic Science National University of Defense Technology Hunan Changsha410005 China 32065 Troops Liaoning Shenyang110162 China

Polarized images have rich high-frequency information. The texture, contours and edges of objects in the image are very obvious, while the intensity images contains the main energy of the image and the background field is rich in information. Therefore, the polarization image is combined with Light intensity image fusion is of great significance. This article uses deep learning methods, based on the training method of generating adversarial networks, using densely connected generator networks, using SSIM loss and gradient loss functions, and experiments have shown that the ideal fusion effect can be achieved. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： image fusion

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Adaptive image enhancement technology based on bad weather 4

Adaptive image enhancement technology based on bad weather

引用

4th international conference on advanced algorithms and signal image processing, aasip 2024

作者： Chang, Jiaxiu Hou, Wenshuai Chen, Wenjing Yan, Jun Cui, Kaige College of Transportation Shandong University of Science and Technology Qingdao266590 China

ISBN: (数字)9781510682818

ISBN: (纸本)9781510682801

To tackle the formidable challenges that adverse weather conditions pose for image object detection, this paper presents an innovative approach grounded in the image Adaptive YOLO (IA-YOLO) framework. The framework has introduced a series of advanced strategies to address the challenges to the accuracy and reliability of object recognition under extreme weather conditions. In environments where visibility is reduced due to factors like rain, fog, or low light, traditional object detection methods often struggle to achieve satisfactory results. However, IA-YOLO aims to overcome these limitations by incorporating adaptive image enhancement techniques that can effectively improve the quality of captured images. By embedding a unique image refinement mechanism within an efficient convolutional neural network, IA-YOLO empowers the system to autonomously acquire superior parameters for image refinement through a minimally supervised learning approach. This approach ensures that the images are enhanced in a way that is specifically tailored to improve object detection performance. To encapsulate, the article presents IA-YOLO as an influential instrument for tackling the obstacles posed by inclement weather in the realm of image object detection. By leveraging adaptive image enhancement techniques, IA-YOLO aims to provide more accurate and reliable detections, even in the most challenging weather scenarios. © 2024 SPIE.

关键词： Convolutional neural networks

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A text-driven image style transfer model based on CLIP and SCBAM 3

A text-driven image style transfer model based on CLIP and S...

引用

3rd international conference on advanced algorithms and signal image processing, aasip 2023

作者： Wu, Haodong Geng, Guohua Zhao, Yanting Wang, Xiaolei Li, Qihang School of Information Science and Technology Northwest University Xi'an710127 China

ISBN: (纸本)9781510668522

To address the issues of low image quality and inadequate detail features encountered in current zero-shot style transfer algorithms, we propose a new text-driven image style transfer model. The model first uses CLIP (Contrastive Languageimage Pre-Training) model to convey the semantic information of text conditions. In addition, We designed a lightweight network, which can quickly express texture information according to text conditions, minimize the similarity cosine distance between the transferred image and the text conditions by CLIP model, and finally obtain the style transfer image. Furthermore, we introduce dual attention mechanism, identity consistency loss, content and style feature loss to make the translated image more vivid and realistic. Extensive experimental results demonstrate that our approach enables the transfer of multiple styles based on text conditions, achieving a broader, more realistic, and faster style transfer compared to existing methods. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Semantics

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Underwater mixed spatial attention network

Underwater mixed spatial attention network

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2021 international conference on advanced algorithms and signal image processing, aasip 2021

作者： Pan, Qiang Yang, Yueting Yang, Chuansheng Wang, Chao Tan, Anhui School of Information Engineering Zhejiang Ocean University Zhejiang Zhoushan316022 China School of Mathematics and Statistics Beihua University Jilin Jilin132013 China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhejiang Zhoushan316022 China

High-quality underwater images play an important role in obtaining and understanding underwater information. However, raw underwater images have many problems, such as low contrast, chromatic aberration, blur and low light, which seriously restrict the progress of other underwater tasks. In this paper, we propose an Underwater Mixed Spatial Attention Network (UMSAN) for underwater image enhancement. The evaluation on the EUVP dataset and some other real underwater images demonstrates that our network is sufficient against several of the advanced models. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： image enhancement

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The grab tracking method for 3D reconstruction of a waste pool 3

The grab tracking method for 3D reconstruction of a waste po...

引用

3rd international conference on advanced algorithms and signal image processing, aasip 2023

作者： Ding, Xiaosong Feng, Chong Li, Liqiang Chen, Xi International Business School Beijing Foreign Studies University Beijing100089 China

ISBN: (纸本)9781510668522

With 3D reconstruction method, this paper conducts experiments based on the data of a waste incineration plant in eastern China, and analyzes and compares the visual 3D monitoring, laser sensor 3D monitoring, and grabbing bucket tracking. By comparison, the grab tracking method is very appropriate for 3D reconstruction in terms of cost, accuracy, and implementation. With the proposed method, the volume monitoring not only provides practical suggestions for the construction of the optimal waste dump volume monitoring decision-making system, but also helps waste incineration enterprises achieve the goal of ''carbon peaking and carbon neutrality''. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Computer vision

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Adaptive feature normalization network for single image dehazing

Adaptive feature normalization network for single image deha...

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2021 international conference on advanced algorithms and signal image processing, aasip 2021

作者： Hu, Guangrui Yang, Yueting Yang, Chuansheng Wang, Chao Tan, Anhui School of Information Engineering Zhejiang Ocean University Zhoushan316022 China School of Mathematics and Statistics Beihua University Jilin Jilin132013 China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan316022 China

In this paper, we design an end-to-end adaptive feature normalization network (AFN-Net) for single image dehazing. In order to fit the function that can recover haze-free images from haze images more effectively, an Adaptive Normalization module (AN) is designed, which can unify the input into the same feature space. A large number of experimental evaluations show that the proposed method is superior to the state-of-the-art (SOTA) method on the benchmark datasets. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Large dataset

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Transplantation and optimization calculation of power inspection image matching application based on the ARM platform 3

Transplantation and optimization calculation of power inspec...

引用

3rd international conference on advanced algorithms and signal image processing, aasip 2023

作者： Wei, Wei Rundong, Gan Xun, Li Haibin, Su Zhu, Zhan Guizhou Power Grid Co. Ltd. Information Center Guiyang China Fangxin Technology Co. Ltd. Changsha China Hunan Electric Power Computing Power Integration Engineering Technology Research Center Changsha China

ISBN: (纸本)9781510668522

image matching is an important step in the process of power inspection image stitching and 3D reconstruction, which directly affects the effect of power line image stitching and 3D reconstruction, and is widely used in power line inspection. Traditional power inspection image matching applications are mostly implemented on X86 architecture, but ARM architecture has gradually entered the server field in recent years, showing high efficiency and energy-saving advantages, so it is necessary to transplant power inspection image matching applications to the server platform of ARM architecture. Since the application of image matching is very computation-based, in the process of porting the algorithm to the ARM platform, this paper adopts the Gaussian pyramid algorithm to accelerate the application of image matching through the search method from coarse to fine. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image matching

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Multi-TransPathoNet: A transfer learning-based neural network for pathology image classification with multi-Task learning 3

Multi-TransPathoNet: A transfer learning-based neural networ...

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3rd international conference on advanced algorithms and signal image processing, aasip 2023

作者： Deng, Xin Li, Min Ren, Xiaojun Zheng, Xiuyan Liang, Fei He, Liang Zhu, Min School of Software Xinjiang University China School of Information Science and Engineering Xinjiang University China Xinjiang Key Laboratory of Clinical Genetic Testing and Biomedical Information China Xinjiang Clinical Research Center for Precision Medicine of Digestive System Tumor China Department of Pathology Karamay Central Hospital China Xinjiang Key Laboratory of Signal Detection and Processing China Dept. of Electron. Eng. and Beijing National Research Center for Information Science and Technology Tsinghua University China

ISBN: (纸本)9781510668522

Histopathological images are one of the common diagnostic tools used in the medical field. However, the complex cellular and tissue structures inside colorectal cancer histopathological images make it difficult for physicians to perform efficient cancer diagnosis and quantitative analysis. Therefore, this study proposed a multi-Task learning-based pathology image classification method Multi-TransPathoNet (A transfer learning-based neural network for pathology image classification with multi-Task learning, Multi-TransPathoNet) for detection and visualization output of colorectal cancer histopathology images, thus helping to improve medical efficiency. To verify the practical application value of this method, this study used the paracancerous, highly differentiated, and high-moderately differentiated histopathological images which are more difficult to distinguish in colorectal cancer as the dataset for classification test, and our method obtained 92% accuracy. Besides, we compared with the single-Task learning models Resnet50 and Vgg19 respectively, which also have good performance in image classification tasks, to comprehensively evaluate the proposed model's performance. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Computer aided diagnosis

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