International Conference on Digital Image Processing最新文献

Predicting good features using a hybrid feature for visual geolocation system 基于混合特征的视觉地理定位系统特征预测

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2645302

Reem Aljuaidi, Micheal Manzke

引用次数: 0

PDNet: an advanced architecture for polyp image segmentation PDNet:用于息肉图像分割的高级架构

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2643392

Hanqing Liu, Zhipeng Zhao, Ruichun Tang, Peishun Liu, Yixin Chen, Jianjun Zhang, Jing Jia

引用次数: 0

Deformable voxel grids for shape comparisons 用于形状比较的可变形体素网格

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2645961

Raphaël Groscot, L. Cohen

引用次数: 1

Deep learning video analytics solutions for ocean surveillance systems 用于海洋监测系统的深度学习视频分析解决方案

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2643108

Harikrishnan V. S., Shivam Dixit, P. K, Jitesh Kamnani, R. S., R. Venkatesan

{"title":"Deep learning video analytics solutions for ocean surveillance systems","authors":"Harikrishnan V. S., Shivam Dixit, P. K, Jitesh Kamnani, R. S., R. Venkatesan","doi":"10.1117/12.2643108","DOIUrl":"https://doi.org/10.1117/12.2643108","url":null,"abstract":"Moored data buoys are floating platforms at sea. These buoys serve as in-situ Weather, Ocean and Tsunami observatories. These buoys transmit real-time data through 3G/GSM/GPRS and satellite telemetry. Damage to the buoy systems by humans, boats, ships etc., intentional or otherwise, causes loss of data, and inhibits early warning systems. It also has financial implications due to the loss of the instruments, repair & reinstallation charges, and the time a ship spends to fix the buoy. Challenges arise while analyzing the video footage as they are unstable and shaky, due to the continuous movements of floating ocean buoy platforms caused by the state of the sea. This paper explores object detection algorithms for detecting eight different objects commonly found in the camera video footage transmitted by the buoy platforms at sea. The object detection training implementation gave us a best accuracy of 0.867MAP@0.5IOU. The object detection will help in solutions like Object Search, detection of floating marine plastic debris, understanding the direction of motion of ships, boats etc. In a broader perspective, it can help in Surveillance, Market Survey and Fish Detection in underwater cameras for fish abundance study.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"27 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":"122045164","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

Improvement of attention modules for image captioning using pixel-wise semantic information 基于逐像素语义信息的图像字幕注意模块改进

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644743

Zhihao Chen, Keisuke Doman, Y. Mekada

引用次数: 0

Mineralization information extraction of metal mining area based on Landsat images 基于Landsat图像的金属矿区矿化信息提取

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2643522

Rui-Feng Wang, Yuxin Gao, Wenlong Yu, Rubing Huang

{"title":"Mineralization information extraction of metal mining area based on Landsat images","authors":"Rui-Feng Wang, Yuxin Gao, Wenlong Yu, Rubing Huang","doi":"10.1117/12.2643522","DOIUrl":"https://doi.org/10.1117/12.2643522","url":null,"abstract":"Remote sensing technology can quickly identify hydro thermal alteration related to mineralization and provide help for prospecting through efficient and accurate analysis. In this study, the Zhaoping fault zone is taken as the research area, Landsat8 OLI remote sensing images with good imaging quality are used as the data source, and ENVI software is used to perform pruning, radiometric calibration, atmospheric correction, and preprocessing of removing interference information including water and vegetation from remote sensing images in 2015 and 2020 respectively. The mineralized alteration information of this area is extracted by CROSTA method, and the extracted thematic information is analyzed and verified. In addition, by comparing the extraction maps of mineralization information in 2015 and 2020, it is found that the situation of over exploitation of mineral resources still exists. The results are in good agreement with the known ore points in the study area, indicating that the extraction of metal mineralization information through remote sensing images is of great significance to the study and rational utilization of mineral resources.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"57 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":"130537931","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

Automated osteoporosis prediction system using artificial intelligence to calculate cortical thickness index from hip X-rays 利用人工智能计算髋部x光片皮质厚度指数的骨质疏松症自动预测系统

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644476

Wei Ying Ling, K. W. Choo, Tiehua Du, Waiming Kong, Jerry Delphi Chen Yongqiang, Eu Jin Tan

{"title":"Automated osteoporosis prediction system using artificial intelligence to calculate cortical thickness index from hip X-rays","authors":"Wei Ying Ling, K. W. Choo, Tiehua Du, Waiming Kong, Jerry Delphi Chen Yongqiang, Eu Jin Tan","doi":"10.1117/12.2644476","DOIUrl":"https://doi.org/10.1117/12.2644476","url":null,"abstract":"Early diagnosis and regular monitoring of osteoporosis is key to prevent further deterioration and fractures in osteoporosis patients. Dual-energy X-ray Absorptiometry (DXA), despite being a gold standard for diagnosing osteoporosis, is not routinely ordered due to limited availability of DXA machine, especially in developing countries. As a result, orthopedists often lack DXA results at the time of examination. This study aims to develop an automated AI system to predict osteoporosis based on a plain x-ray scan of patient’s femur and demographic data, such as age, height and weight. The system first performs instance segmentation on the X-ray scan to locate femur, followed by image processing techniques to measure the inner and outer diameter of the femur, and then compute cortical thickness index (CTI). The CTI value, together with patient’s demographic data, is incorporated into a classification model to predict if the patient is suffering from osteoporosis. We found that the CTI calculated by the AI system is comparable to the manually calculated CTI. The AI system can predict at an accuracy of 85.3% using CTI and patient data.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"22 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":"121291787","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

Rotated target recognition of sonar images via convolutional neural networks with rotated inputs 基于旋转输入卷积神经网络的声纳图像旋转目标识别

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644531

Peng Zhang, Jinsong Tang, Heping Zhong, Mingqiang Ning, Yue Fan

{"title":"Rotated target recognition of sonar images via convolutional neural networks with rotated inputs","authors":"Peng Zhang, Jinsong Tang, Heping Zhong, Mingqiang Ning, Yue Fan","doi":"10.1117/12.2644531","DOIUrl":"https://doi.org/10.1117/12.2644531","url":null,"abstract":"Rotated target recognition is a challenge for Convolutional Neural Networks (CNN), and the current solution is to make CNN rotational invariant through data augmentation. However, data augmentation makes CNN easy to overfit small scale sonar image datasets, and increases its numbers of parameters and training time. This paper proposes to recognize rotated targets of sonar images using a novel CNN with Rotated Inputs (RICNN), which doesn’t need data augmentation. During training, RICNN was trained with sonar images of targets only at one orientation, which avoid it to learn multiple rotated versions of the same targets, and reduces both number of parameters and training time of CNN. During testing, RICNN calculated classification scores for each test image and its all-possible rotated versions. The max of these classification scores were used to simultaneously estimate the category and orientation of each target. Besides, to improve the generalization of RICNN on imbalanced sonar datasets, this paper also designs an imbalanced data sampler. Experiments on a self-made small, imbalanced sonar image rotated target recognition dataset show that the improved RICNN achieves 4.25% higher classification accuracy than data augmentation, and reduces the number of parameters and training time to 2.25% and 19.2% of that of data augmentation method. Moreover, RICNN achieves comparable orientation estimation accuracy with a CNN orientation regressor trained with data augmentation. Codes, dataset are publicly available.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"430 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":"116184688","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

Using Sentinel-2 imagery for detecting oil spills via spatial roughness of mixed normalized difference index 基于混合归一化差分指数空间粗糙度的Sentinel-2图像溢油检测

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644658

Yaowamal Raphiphan, Suphongsa Khetkeeree

{"title":"Using Sentinel-2 imagery for detecting oil spills via spatial roughness of mixed normalized difference index","authors":"Yaowamal Raphiphan, Suphongsa Khetkeeree","doi":"10.1117/12.2644658","DOIUrl":"https://doi.org/10.1117/12.2644658","url":null,"abstract":"The free and open access optical sensor data from the Sentinel-2 constellation can be used for supporting the operation of oil spill monitoring. It has several spectral bands from visible to shortwave infrared with medium to high resolution, which is suitable for detecting the oil spills. However, the spectral signature of the oil spill often has similar to the surrounding environment. Moreover, it also depends on many parameters, such as sensing angle, sea depth, wave characteristics, etc. In this paper, we propose the method for detecting the oil spill by using the Sentinel-2 images. It is based on the Mixed Normalized Difference Index (MNDI) derived from the Normalized Difference Vegetation Index (NDVI) and the Reversed version of the Normalized Difference Index (NDI) applied for the forest fire monitoring. This index will give the high variation values in the oil spill area, which can estimate the oil spill area by observing its spatial roughness. Four study areas in Saudi Arabia, Greece, Azerbaijan, and Indonesia were used to evaluate the detectable performance compared with the current methods. The visualized results show that our algorithm gives noticeable results and high contrast, including low noise, except the oil spills in Greece.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"53 11 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":"126325073","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

Classification of fundus diseases based on meta-data and EB-IRV2 network 基于元数据和EB-IRV2网络的眼底疾病分类

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644254

Xiangyu Deng, Feifei Ding

引用次数: 1