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Autism spectrum disorder analysis by using a 3D-ResNet-based approach 基于3d - resnet的自闭症谱系障碍分析方法
International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644315
Heqian Zhang, Zhaohui Wang
{"title":"Autism spectrum disorder analysis by using a 3D-ResNet-based approach","authors":"Heqian Zhang, Zhaohui Wang","doi":"10.1117/12.2644315","DOIUrl":"https://doi.org/10.1117/12.2644315","url":null,"abstract":"Autism spectrum disorder is a heterogeneous neurological disorder. The early diagnosis of autism is critical to apply effective treatment. Presently, most diagnoses are based on behavioral observations of symptoms. There has been an increasing number of approaches using magnetic resonance imaging with the development of deep learning in recent years. However, the interfering elements and insignificant differentiation between positive and negative samples have seriously affected the classification performance. In this paper, a multi-scale information fusion mechanism is proposed to combine with attention sub-nets to establish an end-to-end classification model, which selects appropriate fusion strategies for the outputs of different layers of the convolutional neural network to make comprehensive use of the information at different levels of the image. Experiments are conducted by using the dataset of Autistic Brain Imaging Data Exchange. The results show that the proposal achieves better performance than the models in comparison.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127088194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Rapid identification of mature Xanthoceras sorbifolium bunge 成熟文冠果的快速鉴定
International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644452
Xia Geng, Yufei Zhang, Baoxin Wu, Wenwen Zhu, Han Li
{"title":"Rapid identification of mature Xanthoceras sorbifolium bunge","authors":"Xia Geng, Yufei Zhang, Baoxin Wu, Wenwen Zhu, Han Li","doi":"10.1117/12.2644452","DOIUrl":"https://doi.org/10.1117/12.2644452","url":null,"abstract":"Xanthoceras sorbifolium bunge is a kind of edible oil tree in China, which has very high economic value, but the timely picking of mature fruits is a problem that has troubled farmers for a long time. To rapidly, automatically and accurately identify mature Xanthoceras sorbifolium bunge in the field, a mobile data acquisition and transmission system was firstly designed based on the architecture of the Internet of Things, which provides image acquisition and positioning tools for timely and accurate picking of Xanthoceras sorbifolium bunge. Secondly, a mature Xanthoceras sorbifolium bunge identification network model was constructed based on the lightweight efficient model YOLOv3 by using convolutional neural network (CNN) and flip residual network. The established optimal identification model was evaluated, the results of which indicate that the constructed optimal model can serve as a tool to identify the maturity of Xanthoceras sorbifolium bunge with the mAP of 97.04%.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134369361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
LDAM: line descriptors augmented by attention mechanism LDAM:由注意力机制增强的行描述符
International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644245
Xulong Cao, Yao Huang, Yongdong Huang, Yuanzhan Li, Shen Cai
{"title":"LDAM: line descriptors augmented by attention mechanism","authors":"Xulong Cao, Yao Huang, Yongdong Huang, Yuanzhan Li, Shen Cai","doi":"10.1117/12.2644245","DOIUrl":"https://doi.org/10.1117/12.2644245","url":null,"abstract":"Compared with point features, line features can provide more geometric information in vision tasks. Although traditional line descriptor methods have been proposed for a long time, learning-based line descriptor methods still need to be strengthened. Inspired by the message passing mechanism of graph neural networks, we propose a new neural network architecture named LDAM that alternately uses two attention mechanisms to augment line descriptors and extract more line correspondences. Compared with previous methods, our method learns the geometric properties and prior knowledge of images through the mutual aggregation of features between a pair of images. The experiments on real data verify the good performance of LDAM in terms of matching accuracy. Furthermore, LDAM is also robust to viewpoint change or occlusion.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124139149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Modelling and simulation of lateral jet infrared radiation based on the light of sight method 基于视光法的侧向射流红外辐射建模与仿真
International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2643850
Yang He, Cheng Mu, Shipeng Li, Huan Liu, Nan Wu, Taiye Liu
{"title":"Modelling and simulation of lateral jet infrared radiation based on the light of sight method","authors":"Yang He, Cheng Mu, Shipeng Li, Huan Liu, Nan Wu, Taiye Liu","doi":"10.1117/12.2643850","DOIUrl":"https://doi.org/10.1117/12.2643850","url":null,"abstract":"In order to study the effect of flight altitude on the radiation characteristics of engine lateral jet, based on the simulation results of three-dimensional flow field, radiation transfer equation and molecular spectral line parameter database, we applied the apparent light line of sight method to solve the lateral jet radiation transfer equation and established a procedure to calculate the infrared radiation characteristics of the lateral jet of the attitude control engine; Correct the spectral line intensity of gases at high temperature and pressure. Using the spectral band model to calculate the spectrum absorption coefficients. The infrared radiation characteristics of the lateral jet of the attitude control engine at different flight altitudes are studied, and the distribution of the infrared radiation brightness of the lateral jet in different bands is obtained. The lateral jet spectral irradiance of the attitude control engine decreases with the increase of flight altitude in the low altitude environment, and increases with the increase of flight altitude in the high altitude environment. The results show that the program can simulate the infrared radiation characteristics of the lateral jet of the attitude control engine well and is widely applicable; Different flight altitudes affect the infrared radiation characteristics of the lateral jet of the engine to a certain extent and the flight altitudes at low and high altitudes have different effects on the radiation characteristics of the lateral jet of the attitude control engine.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"1075 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122890399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
ECA-based generative adversarial network for multi-focus colour image fusion 基于eca的生成对抗网络多焦点彩色图像融合
International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2643626
Xiaojie Luo, Zhen-tai Lu
{"title":"ECA-based generative adversarial network for multi-focus colour image fusion","authors":"Xiaojie Luo, Zhen-tai Lu","doi":"10.1117/12.2643626","DOIUrl":"https://doi.org/10.1117/12.2643626","url":null,"abstract":"In this paper, the idea of regression model is adopted to complete the fusion of multi-focus images through an end-to-end generative adversarial network (GAN). In the generator part, image features are extracted through multi-branch connection and dense connection technology. In the process of extracting high-dimensional image features, the ECA module is embedded to improve the capability of network. In the discriminator part, the idea of relative GAN is used to predict the relative authenticity between images. Due to the idea and reasonable network construction, the method proposed in this paper can obtain good results of image fusion. And the experimental results demonstrate that the one can also obtain fine results in objective evaluation, which is better than the compared algorithms.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122085178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Ship target detection based on multitask learning 基于多任务学习的舰船目标检测
International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644430
Ju He, Ting Zhang, Zhaoying Liu, Yujian Li
{"title":"Ship target detection based on multitask learning","authors":"Ju He, Ting Zhang, Zhaoying Liu, Yujian Li","doi":"10.1117/12.2644430","DOIUrl":"https://doi.org/10.1117/12.2644430","url":null,"abstract":"Ship target detection is of great significance in marine surveillance, rescue and so on. In this paper, in order to improve the performance of ship target detection, we proposed a ship target detection method based on multi task learning. There are mainly two contributions. Firstly, we designed a multi-task learning model by integrating segmentation module to the faster RCNN model. Through the strategies of feature sharing and joint learning, it is helpful to improve the accuracy of target detection with the assistance of segmentation; Secondly, in order to deal with the impact of initial anchor frame scale on target detection accuracy, we introduced an adaptive anchor width height ratio setting method based on improved K-means algorithm, by adaptively select initial anchor size suitable for the characteristics of ship targets, it is beneficial to further improve the detection accuracy. Moreover, we constructed an extended version of ship image data set including 14614 images belonging to 13 categories. Experimental results demonstrated that the proposed model can effectively improve the accuracy of ship target detection; and the comparison and the ablation experiments further validated the strategies of multi-task joint learning and adaptive anchor size setting is helpful for improving the performance of ship target detection.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"38 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123274951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Identifying recaptured images using deep hybrid correlation network 利用深度混合相关网络识别再现图像
International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2643013
Nan Zhu, Zhiqin Liu, Xiaolu Guo
{"title":"Identifying recaptured images using deep hybrid correlation network","authors":"Nan Zhu, Zhiqin Liu, Xiaolu Guo","doi":"10.1117/12.2643013","DOIUrl":"https://doi.org/10.1117/12.2643013","url":null,"abstract":"With the explosive advancements in image display technology, recapturing high-quality images from high-fidelity LCD screens becomes more and more easy. Such recaptured images can not only be used for deceiving intelligent recognition systems but also for hiding tampering traces. In order to prevent such a security loophole, we propose a recaptured image detection approach based on deep hybrid correlation network. Specifically, we first design a deep hybrid correlation module to extract the correlations in different color channels and neighboring pixels. This module has three different branches, in which a 1×1 convolution layer is used to learn the correlations between color channels while two consecutive convolution sub-modules are used to extract the correlations between neighboring pixels. Then we feed the output of this module into consecutive convolution modules to further learn the hierarchical representation for make decision. Ablation experiments verify the effectiveness of our proposed deep hybrid correlation module, while single database experiments demonstrate that our proposed method can achieve average accuracy with about 99% on three public databases. Specifically, our method not only performs very close to the state-of-the-art methods on the most difficult-to-detect ICL-COMMSP database and the relative low-quality NTU-ROSE database, but also improves the performance on the most diverse Dartmouth database obviously, which verifies the effectiveness of the proposed deep architecture. Besides, mixed database experiments verify the superiority of the generalization ability of our proposed method.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"38 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127983404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Super-resolution for semantic segmentation 语义分割的超分辨率
International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2643026
Xuan‐Zhi Zhang, Guoping Xu, Wentao Liao, Xing Wu
{"title":"Super-resolution for semantic segmentation","authors":"Xuan‐Zhi Zhang, Guoping Xu, Wentao Liao, Xing Wu","doi":"10.1117/12.2643026","DOIUrl":"https://doi.org/10.1117/12.2643026","url":null,"abstract":"Image segmentation is a classical problem in the field of computer vision. With the extensive development of deep learning, it has achieved much progress in semantic segmentation. However, the mainstream networks used in deep learning such as Fast-SCNN, U-Net, which still face challenges in image segmentation. A common problem is that linear interpolation is used in the up-sampling stage of these networks to obtain high-resolution images. Due to the lack of sufficient feature information, the contours of the objects in the image are blurred and grided. For this reason, we propose a new super-resolution (SR) method to replace the up-sampling with linear interpolation in the network model. Five representative networks integrated with our proposed SR module are used for verification on the CamVid data set. The experimental results show that our method has a 2%~4% improvement in mIoU (the mean value of Intersection over Union) and a 2%~3% improvement in pixel accuracy, which demonstrates its generalization and effectiveness of our method.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130500646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Dock detection method in remote sensing images based on improved YOLOv4 基于改进YOLOv4的遥感图像码头检测方法
International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2643035
Haitao Guo, Hui Gao, Chaohui Guo, Jun Lu, Yuzhun Lin
{"title":"Dock detection method in remote sensing images based on improved YOLOv4","authors":"Haitao Guo, Hui Gao, Chaohui Guo, Jun Lu, Yuzhun Lin","doi":"10.1117/12.2643035","DOIUrl":"https://doi.org/10.1117/12.2643035","url":null,"abstract":"The dock target in remote sensing images has the characteristics of slender structure and direction arbitrarily. The general target detection algorithm based on the convolutional neural network cannot effectively obtain the direction information of the target, which cannot meet the actual demand of dock detection. This study designed a deep convolutional neural network architecture in any direction based on the YOLOv4 algorithm aimed at resolving the above problems. First, the multidimensional coordinate method was used to calibrate the dock target so that the network could contain the direction information of the target. Second, the loss function of the algorithm was optimized to make it suitable for directional target detection. Finally, an attention mechanism was introduced to enhance the extraction ability of the algorithm and further improve its detection accuracy. Two datasets of dock target detection from remote sensing images were selected for experiments, and the results showed that the improved YOLOv4 network was better than the other networks in the dock target detection task.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132435252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
An improved method of image enhancement based on fuzzy theory 一种基于模糊理论的图像增强改进方法
International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644407
Weidong Qu, Ming Shao, Xiang-zheng Cheng, Yunfeng Zhang, Wei Liu
{"title":"An improved method of image enhancement based on fuzzy theory","authors":"Weidong Qu, Ming Shao, Xiang-zheng Cheng, Yunfeng Zhang, Wei Liu","doi":"10.1117/12.2644407","DOIUrl":"https://doi.org/10.1117/12.2644407","url":null,"abstract":"Artificial intelligence (AI) and its application are developed explosively not only in control field but also in signal and information processing field. Fuzzy theory is an important branch of AI. In fuzzy enhancement theory of image processing, Pal function is often employed as the membership function. Although this function possesses good filtering effect, the fuzzy factors of the function are often empirical values, which results to different image enhancement effects when the input images are different, and details of the enhancement image are not clear, then bad enhancement effect always appears. In this paper, the fuzzy factors are considered as variables. At the same time, an evaluation function is constructed to evaluate the enhancement performance, and a suitable optimization algorithm is used to obtain the most optimum values of the fuzzy factors automatically. Simulation results show good performance of the improved method.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124174342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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