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Fish-eye image effective region segmentation algorithm based on circle fitting 3

Fish-eye image effective region segmentation algorithm based...

引用

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

作者： Jiang, Taiping Yu, Nong Li, Xue School of Computer Science and Technology Anhui University of Technology Ma'anshan Anhui China

ISBN: (纸本)9781510668522

When synthesizing panoramic images from fish-eye images, dynamically segmenting the fish-eye image's valid region is crucial. This paper addresses the challenge of segmenting the valid region in fish-eye images caused by lens glare by proposing a segmentation algorithm that combines adaptive threshold edge detection with improved circle fitting. The process begins by performing gradient and direction calculations on the original fish-eye image to extract edge contour information. Subsequently, the edge contours undergo refinement using the non-maximum suppression method. An adaptive dual threshold is generated using the maximum between-class variance method to enable accurate edge detection. The algorithm further initializes and fits the edge points using the least squares method while incorporating a loss function to enhance the circle-fitting algorithm. Finally, an iterative gradient descent method is employed to iteratively solve for the optimal solution and segment the fish-eye image's valid region. Experimental results demonstrate the algorithm's ability to accurately detect contours and effectively segment the valid region, successfully addressing the challenge of extracting the valid region caused by lens glare. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Edge detection

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UAV image object detection network based on Yolo 3

UAV image object detection network based on Yolo

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

作者： Huang, Jubin Guo, Zexuan Hong, Xuebin Wu, Haohai Lin, Zhe College of Electronic Information Shantou Polytechnic Donghu Haojiang district Shantou Guangdong China

ISBN: (纸本)9781510668522

This paper proposes an object detection network based on YOLOv7 for UAV(unmanned aerial vehicle) aerial images which have a large field of view, complex backgrounds, small target sizes and uneven position distribution. The proposed network integrates Dyhead (a dynamic target detection head) which is based on attention mechanism into the network, causing the model to focus more on target-rich regions and alleviating noise interference caused by complex backgrounds, thereby improving detection accuracy. Additionally, the network proposes a new loss function which combines CIOU(Complete IOU) and WISE-IOU. The improved loss function balances the IOU and geometric metrics (such as center point distance and aspect ratio) in the position regression loss, increasing the model's tolerance for low-quality data samples and improving its overall generalization capability and detection accuracy. Finally, experiment on the Visdrone UAV image dataset show that the proposed network has a 2.1% and 1.3% increase in mAP@0.5(mean average precision(IOU=0.5)) and mAP@0.5:0.95(mean average precision(IOU=[.5:.95])) evaluation index compared to the base network. Increasement of accuracy are also observed in all ten subdivision categories of the dataset, validating the effectiveness of the proposed network for UAV image object detection tasks. © COPYRIGHT SPIE.

关键词： Object detection

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Research on lightweight detection algorithm of Infrared image based on YOLOv5 4

Research on lightweight detection algorithm of Infrared Imag...

引用

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

作者： Zhang, Wenyi Mo, Hongwei Wu, Songling Li, Xiaomin Harbin Engineering University School of Intelligent Science and Engineering Harbin150000 China Beijing Aerospace Automatic Control Institute Beijing100854 China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing100854 China

ISBN: (数字)9781510682818

ISBN: (纸本)9781510682801

At present, infrared image can show good detection effect in complex environment because of its unique imaging characteristics, which brings a lot of convenience to people's production and life. However, because the infrared image contains a great lot of unnecessary information, it has great requirements for computing power and memory capacity, so it is challenging to satisfy the demands of real-time detection on resource-constrained portable mobile devices. To solve this problem, this paper proposes a refined lightweight algorithm built upon the YOLOv5, which changes the backbone network of the model to GhostNet network, introduces CA attention mechanism into the model to improve the detection effect of small targets, and changes the loss function of the model to Focal-EIoU loss function to improve the location effect of the boundary box and quicken the model's rate of convergence. Then replace the conventional convolution used in the sampling process of the model with the space-to-depth convolution module, in order to enhance the model's detection performance on low-resolution images and enable the extraction of more feature information. According to the experimental results, the enhanced detection algorithm is just 1% less than the initial YOLOv5 algorithm, and the model volume and floating point calculation are reduced by 31.9% and 36% respectively. This is better suited for deployments that are lightweight. © 2024 SPIE.

关键词： Convolution

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Review of MRI brain tumor segmentation and MGMT promoter classification methods on BraTs dataset based on Deep learning 7

Review of MRI brain tumor segmentation and MGMT promoter cla...

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IEEE 7th international conference on advanced Technologies, signal and image processing (ATSIP)

作者： Amor, Feriel Mzoughi, Hiba Njeh, Ines Ben Slima, Mohamed Adv Technol Image & Signal Proc ATISP Lab Sfax Tunisia Sfax Univ Natl Engn Sch Sfax ATMS LAB Sfax Tunisia Gabes Univ Higher Inst Comp Sci & Multimedia Gabes Gabes Tunisia Sfax Univ Natl Sch Elect & Telecommun Sfax Sfax Tunisia

ISBN: (纸本)9798350351491;9798350351484

During clinical decision-making, the segmentation process Is considered an important task because it provides valuable information about the location, size, and characterization of the brain tumor. However, manual human segmentation iserror-prone, time-consuming, and requires skilled neuroradiologists. Magnetic resonance imaging (MRI) could give extremely detailed images for the investigation and diagnosis of glioblastoma brain cancers. We compared evaluated approaches on the BRATS 2021 and BRATS 2022 datasets and found that they outperformed and could compete with state-of-the-art algorithms in comparable settings. In our research, we focused on two crucial tasks: segmentation and MGMT classification. This study also addresses asn objective evaluation through performance evaluation of cutting-edge DL-based techniques for MR image analysis (Brats 2021-Brats 2022). Based on the findings of the contrasted methods, we can confirm that using a combination of DL techniques will produce more accurate segmentation results than depending on a single, unique methodology. For the second task, five distinct deep learning-based methods were evaluated to predict the methylation state of the MGMT promoter.

关键词： Deep Learning (DL) Resonance Imaging (MRI) Segmentation Gliomas MGMT BraTS2021 BraTS 2022

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image classification of brain tumor based on channel attention mechanism

Image classification of brain tumor based on channel attenti...

引用

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

作者： Liu, Mengxin Yang, Jun School of Information Engineering Ningxia University Yinchuan Ningxia750021 China

The main method of traditional medicine to diagnose brain tumors is that expert doctors read the pictures with naked eyes. Meanwhile, the annotation of this type of medical image data set requires to be done manually by experienced experts, which makes the production cost of labeled medical image data set higher. In view of the above situation, this study firstly used unsupervised learning method Deep Convolution Generative Adversarial Networks (DCGAN) to generate data set expansion on the four types of MRI brain tumor images including glioma, meningioma, pituitary tumor and no tumor. In addition, an improved DenseNet-201 brain tumor classification network model is proposed, which uses the channel attention mechanism SENet as a bypass connecting the Convolutional layer and the Transition layer (or the Transition layer and the Transition layer), and interweaves the classification model on this basis. Optimized the problem of weak resolution of the original model to extract features, thereby accurately capturing the position, shape and texture information of the brain tissue in the MRI image. The model improves the accuracy of the brain MRI image classification, and enhances the generalization ability of the model. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Tumors

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SEL-Net: A multimodal image segmentation method based on spatial attention mechanism 3

SEL-Net: A multimodal image segmentation method based on spa...

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

作者： Xu, Ke Li, Min Wang, Yunling Ma, Hongbing School of Software Xinjiang University China School of Information Science and Engineering Xinjiang University China Department of Radiology The First Affiliated Hospital of Xinjiang Medical University China Department of Electronic Engineering Tsinghua University China

ISBN: (纸本)9781510668522

Brain segmentation, a crucial research area in medical image processing, plays a vital role in assisting doctors with diagnosing and treating various neurological disorders. The brain consists of multiple tissues and structures, resulting in differential representation across different imaging modalities. This makes it challenging to achieve effective segmentation of image information within a single modality. Therefore, we propose the multimodal brain segmentation method Selective Modality Segmentation Network (SEL-Net), which combines information from multiple image modalities and adopts a method based on a spatial attention mechanism that can integrate multi-modal image information, adaptively adjusts the weights of each modality using a spatial attention mechanism, and optimizes it through different loss function ratios. Different loss function ratios can balance the influence of different modalities, thereby improving the performance and robustness of the model. In the BraTs2019 dataset, SEL-Net proves its effectiveness and superiority, and SEL-Net achieves better results in the metrics of Dice coefficient, sensitivity and specificity. This indicates that SEL-Net has great potential in multimodal brain segmentation, which can provide more accurate and reliable results for medical diagnosis and treatment. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Magnetic resonance imaging

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Multi-Stage Enhanced Rain image Restoration Network

Multi-Stage Enhanced Rain Image Restoration Network

引用

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

作者： Wang, Quanqing Cheng, Yuanshun Yang, Yueting Wang, Chao Yang, Chuansheng School of Information Engineering Zhejiang Ocean University Zhejiang Zhoushan316022 China Tianfu College of SWUFE Sichuan Mianyang621000 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

The restoration task of rain images is beneficial to the further development of computer vision tasks. However, recent rain removal methods have difficulty coping with dense rain streaks. In this paper, we propose a Multi-stage Enhanced Rain image Restoration Network (MERNet) for the task of rain removal. Our main idea is to design a three-stage network architecture, which makes full use of the enhanced information of the previous stage and complements the information of the next stage through transmission step by step. We give consideration to information enhancement at the same time of information transmission, fully improve the information utilization rate and the overall network effect. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Rain

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Two-Stage Fusion Model for Heavy Rain Removal on Single image

Two-Stage Fusion Model for Heavy Rain Removal on Single Imag...

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

作者： Pan, Xiaomiao 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

Current single image derain methods cannot solve the heavy rain situation well. In this paper, based on the physical model of a rainy image, we build a two-stage network, TSF-Net, which combines model-driven and data-driven methods. The first stage gets the rain streaks, atmospheric light, and transmission map to obtain the coarse rain-free image by the physical model. The second stage is a fully convolutional neural network with the structure of U-Net. The proposed Multi-Scale Projection Fusion Block (MSPFB) module, which can perceive the spatial information across the scale of images, is employed to remove the residual rain and fuzzy parts in the first stage and obtain a refined clean image. Extensive experiments show that our TSF-Net achieves better accuracy and visual improvements against state-of-the-art methods. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Rain

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BLIND image QUALITY EVALUATOR WITH SCALE ROBUSTNESS

BLIND IMAGE QUALITY EVALUATOR WITH SCALE ROBUSTNESS

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IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Wang, Ci Li, Mei East China Normal Univ Sch Commun & Elect Engn Shanghai Peoples R China East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9781728176055

image quality assessment (IQA) algorithms usually need define some visual-related features and build their relationship with image visual quality. Regardless of whether the features are extracted in spatial or frequency domain, these features are often sensitive to image resolution, resulting in that existing no-reference (NR) IQA algorithms are only efficient for images with similar resolution. In this paper, we propose a more generalized blind image quality evaluator with scale robustness (BIQESR) to assess image quality by locating the robust feature points in a multi-scale space. Compared with the state-of-the-art NR IQA methods, BIQESR has higher performance and stability for images with significantly different resolutions.

关键词： Different resolutions image quality assessment multi-scale space no-reference

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A study based on weed image recognition of a corn-weed control robot 3

A study based on weed image recognition of a corn-weed contr...

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

作者： Li, Shengbo Zhao, Dejin Department of Mechanical Engineering Yanbian University Yanji Jilin133002 China

ISBN: (纸本)9781510668522

Based on the ability of YOLOv5 algorithm for maize weed image recognition, an improved model is proposed to promote the precision of the model for weed identification by adding the attention mechanism ACmix. The backbone network module has been enhanced by incorporating the ACmix module to substitute the 3×3 convolution kernel, while the detection head part has been upgraded through replacement of the 3×3 convolution with the CBAM attention mechanism. The detection accuracy of the model is improved by learning the feature map information at multiple target scales simultaneously. To explore the performance of the model, we collected large number of corn and weed images, built our own dataset and labeled it using the Labelimg tool. Regarding the preprocessing of data, techniques including data augmentation were utilized to improve the model's ability to generalize. We also performed experimental validation using the dataset, and the results show that our improved model achieves significant improvements in both detection and classification tasks of corn weed images compared to the original YOLOv5 algorithm. Finally, we conducted comparative experiments on the ACmix attention mechanism and compared it with other commonly used attention mechanisms. Through training experiments on the established dataset, it is found that the improved model achieves 94.3%precision and 82.5 %recall. Compared with the original YOLOv5 model, the precision improved by 3.0% and recall improved by 1.5%;compared with YOLOv4, the precision improved by 4.6% and recall improved by 1.8%. The results show that the ACmix attention mechanism has advantages in improving model performance. In summary, this paper proposes an improved method based on YOLOv5 deep learning neural network to improve the accuracy of maize weed image recognition by adding the attention mechanism ACmix, which has practical application value. A corn seedling weed dataset was established to provide a data base for subsequent resea

关键词： image recognition

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