中国图象图形学报最新文献_第3页

Systematic Configuration for Hyperparameters Optimization in Transferring of CNN Model to Disaster Events Classification from UAV Images CNN模型转移到无人机图像灾害事件分类中的超参数优化系统配置

中国图象图形学报 Pub Date : 2023-09-01 DOI: 10.18178/joig.11.3.263-270

Supaporn Bunrit, Nittaya Kerdprasop, Kittisak Kerdprasop

{"title":"Systematic Configuration for Hyperparameters Optimization in Transferring of CNN Model to Disaster Events Classification from UAV Images","authors":"Supaporn Bunrit, Nittaya Kerdprasop, Kittisak Kerdprasop","doi":"10.18178/joig.11.3.263-270","DOIUrl":"https://doi.org/10.18178/joig.11.3.263-270","url":null,"abstract":"Deep learning and computer vision-based approaches incorporated with the evolution of the relevant technologies of Unmanned Aerial Vehicles (UAVs) and drones have significantly motivated the advancements of disaster management applications. This research studied a classification method for disaster event identification from UAV images that is suitable for disaster monitoring. A Convolution Neural Network (CNN) of GoogleNet models that were pretrained from ImageNet and Place365 datasets was explored to find the appropriate one for fine-tuning to classify the disaster events. In order to get the optimal performance, a systematic configuration for searching the hyperparameters in fine-tuning the CNN model was proposed. The top three hyperparameters that affect the performance, which are the initial learning rate, the number of epochs, and the minibatch size, were systematically set and tuned for each configuration. The proposed approach consists of five stages, during which three types of trials were used to monitor different sets of the hyperparameters. The experimental result revealed that by applying the proposed approach the model performance can increase up to 5%. The optimal performance achieved was 98.77 percent accuracy. For UAV/drone applications, where a small onboard model is preferred, GoogleNet that is quite small in model size and has a good structure for further fine tuning is suitable to deploy.","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74403775","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

An Efficient Backbone for Early Forest Fire Detection Based on Convolutional Neural Networks 基于卷积神经网络的森林火灾早期检测的高效主干

中国图象图形学报 Pub Date : 2023-09-01 DOI: 10.18178/joig.11.3.227-232

D. Mahanta, D. Hazarika, V. K. Nath

引用次数: 0

An Enhanced Security in Medical Image Encryption Based on Multi-level Chaotic DNA Diffusion 基于多级混沌DNA扩散的医学图像加密安全性提高

中国图象图形学报 Pub Date : 2023-06-01 DOI: 10.18178/joig.11.2.153-160

Mousumi Gupta, Snehashish Bhattacharjee, Biswajoy Chatterjee

{"title":"An Enhanced Security in Medical Image Encryption Based on Multi-level Chaotic DNA Diffusion","authors":"Mousumi Gupta, Snehashish Bhattacharjee, Biswajoy Chatterjee","doi":"10.18178/joig.11.2.153-160","DOIUrl":"https://doi.org/10.18178/joig.11.2.153-160","url":null,"abstract":"A novel medical image encryption technique has been proposed based on the features of DNA encodingdecoding in combination with Logistic map approach. The approach is proven for encryption of highly sensitive medical images with 100 percent integrity or negligible data loss. Testing is done on both high and low-resolution images. Proposed encryption technique consists of two levels of diffusion using the actual structure of the DNA. In the first level of diffusion process, we have used DNA encoding and decoding operations to generate DNA sequence of each pixel. The originality of the work is to use a long DNA structure stored in a text file stored on both sender and receiver’s end to improve the performance of the proposed method. In this initial level of diffusion, DNA sequences are generated for each pixe-land in each of the DNA sequence. Index values are obtained by employing a search operation on the DNA structure. This index values are further modified and ready to be used for next diffusion process. In the second level diffusion, a highly chaotic logistic map is iterated to generate sequences and is employed to extract the chaotic values to form the cipher images. The correlation coefficient analysis, Histogram analysis, Entropy analysis, NPCR, and UACI exhibit significant results. Therefore; the proposed technique can play an important role in the security of low-resolution medical images as well as other visible highly sensitive images.","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84970956","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

Mobile Dermatoscopy: Class Imbalance Management Based on Blurring Augmentation, Iterative Refining and Cost-Weighted Recall Loss 移动皮肤镜:基于模糊增强、迭代精炼和成本加权召回损失的类不平衡管理

中国图象图形学报 Pub Date : 2023-06-01 DOI: 10.18178/joig.11.2.161-169

Nauman Ullah Gilal, Samah Ahmed Mustapha Ahmed, J. Schneider, Mowafa J Househ, Marco Agus

{"title":"Mobile Dermatoscopy: Class Imbalance Management Based on Blurring Augmentation, Iterative Refining and Cost-Weighted Recall Loss","authors":"Nauman Ullah Gilal, Samah Ahmed Mustapha Ahmed, J. Schneider, Mowafa J Househ, Marco Agus","doi":"10.18178/joig.11.2.161-169","DOIUrl":"https://doi.org/10.18178/joig.11.2.161-169","url":null,"abstract":"We present an end-to-end framework for real-time melanoma detection on mole images acquired with mobile devices equipped with off-the-shelf magnifying lens. We trained our models by using transfer learning through EfficientNet convolutional neural networks by using public domain The International Skin Imaging Collaboration (ISIC)-2019 and ISIC-2020 datasets. To reduce the class imbalance issue, we integrated the standard training pipeline with schemes for effective data balance using oversampling and iterative cleaning through loss ranking. We also introduce a blurring scheme able to emulate the aberrations produced by commonly available magnifying lenses, and a novel loss function incorporating the difference in cost between false positive (melanoma misses) and false negative (benignant misses) predictions. Through preliminary experiments, we show that our framework is able to create models for real-time mobile inference with controlled tradeoff between false positive rate and false negative rate. The obtained performances on ISIC-2020 dataset are the following: accuracy 96.9%, balanced accuracy 98%, ROCAUC=0.98, benign recall 97.7%, malignant recall 97.2%.","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82984587","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

The Performance Analysis of Facial Expression Recognition System Using Local Regions and Features 基于局部区域和特征的面部表情识别系统性能分析

中国图象图形学报 Pub Date : 2023-06-01 DOI: 10.18178/joig.11.2.104-114

Yining Yang, Vuksanovic Branislav, Hongjie Ma

引用次数: 0

ResMLP_GGR: Residual Multilayer Perceptrons- Based Genotype-Guided Recurrence Prediction of Non-small Cell Lung Cancer 基于残差多层感知器的非小细胞肺癌基因型复发预测

中国图象图形学报 Pub Date : 2023-06-01 DOI: 10.18178/joig.11.2.185-194

Yang Ai, Yinhao Li, Yen-Wei Chen, Panyanat Aonpong, Xianhua Han

{"title":"ResMLP_GGR: Residual Multilayer Perceptrons- Based Genotype-Guided Recurrence Prediction of Non-small Cell Lung Cancer","authors":"Yang Ai, Yinhao Li, Yen-Wei Chen, Panyanat Aonpong, Xianhua Han","doi":"10.18178/joig.11.2.185-194","DOIUrl":"https://doi.org/10.18178/joig.11.2.185-194","url":null,"abstract":"Non-small Cell Lung Cancer (NSCLC) is one of the malignant tumors with the highest morbidity and mortality. The postoperative recurrence rate in patients with NSCLC is high, which directly endangers the lives of patients. In recent years, many studies have used Computed Tomography (CT) images to predict NSCLC recurrence. Although this approach is inexpensive, it has low prediction accuracy. Gene expression data can achieve high accuracy. However, gene acquisition is expensive and invasive, and cannot meet the recurrence prediction requirements of all patients. In this study, a low-cost, high-accuracy residual multilayer perceptrons-based genotype-guided recurrence (ResMLP_GGR) prediction method is proposed that uses a gene estimation model to guide recurrence prediction. First, a gene estimation model is proposed to construct a mapping function of mixed features (handcrafted and deep features) and gene data to estimate the genetic information of tumor heterogeneity. Then, from gene estimation data obtained using a regression model, representations related to recurrence are learned to realize NSCLC recurrence prediction. In the testing phase, NSCLC recurrence prediction can be achieved with only CT images. The experimental results show that the proposed method has few parameters, strong generalization ability, and is suitable for small datasets. Compared with state-of-the-art methods, the proposed method significantly improves recurrence prediction accuracy by 3.39% with only 1% of parameters.","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83616358","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

Local Bit-Plane Domain 3D Oriented Arbitrary and Circular Shaped Scanning Patterns for Bio-Medical Image Retrieval 局部位平面域三维定向任意和圆形扫描模式的生物医学图像检索

中国图象图形学报 Pub Date : 2023-06-01 DOI: 10.18178/joig.11.2.212-226

D. Mahanta, D. Hazarika, V. K. Nath

{"title":"Local Bit-Plane Domain 3D Oriented Arbitrary and Circular Shaped Scanning Patterns for Bio-Medical Image Retrieval","authors":"D. Mahanta, D. Hazarika, V. K. Nath","doi":"10.18178/joig.11.2.212-226","DOIUrl":"https://doi.org/10.18178/joig.11.2.212-226","url":null,"abstract":"A new feature descriptor called local bit-plane domain 3D oriented arbitrary and circular shaped scanning pattern (LB-3D-OACSP) is proposed for biomedical image retrieval in this study. Unlike the circular, zigzag and other scanning structures, the LB-3D-OACSP descriptor calculates the association between reference-pixel and its surrounding pixels in bit-plane domain in a 3D plane using multi-directional 3D arbitrary and 3D circular shaped scanning patterns. In contrast to other scanning structures, the multi-directional 3-D arbitrary shaped patterns provide more continual angular dissimilarity among the sampling positions with the aim to capture more frequent changes in the local textures. The total of sixteen number of discriminative 3D arbitrary and 3D circular shaped patterns oriented in various directions are applied on a 3D plane constructed using respective bit-planes of three multi-scale images which ensures the maximum extraction of inter-scale geometrical information across the scales which very effectively captures not only the uniform but non-uniform textures too. The multi-scale images are generated by processing the input image with Gaussian filter banks generating three multi-scale images. The LB-3D-OACSP descriptor is able to capture most of the very fine to coarse image textures through encoding of bit-planes. The performance of LB-3D-OACSP is tested on three popular biomedical image databases both in terms of % average retrieval precision (ARP) and % average retrieval recall (ARR). The experiments demonstrate an encouraging enhancement in terms of %ARP and %ARR as compared to many existing state of the art descriptors.","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91355411","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 Model Based on U-Net for Crack Detection from Asphalt Pavement Images 基于U-Net的沥青路面图像裂缝检测分类模型

中国图象图形学报 Pub Date : 2023-06-01 DOI: 10.18178/joig.11.2.121-126

Y. Fujita, Taisei Tanaka, Tomoki Hori, Y. Hamamoto

引用次数: 0

Mobile Surveillance Siren Against Moving Object as a Support System for Blind PeopleMobile Surveillance Siren Against Moving Object as a Support System for Blind People 作为盲人支持系统的移动防移动物监控警报器移动防移动物监控警报器

中国图象图形学报 Pub Date : 2023-06-01 DOI: 10.18178/joig.11.2.170-177

D. H. Hareva, A. Sebastian, A. Mitra, Irene A. Lazarusli, C. Haryani

{"title":"Mobile Surveillance Siren Against Moving Object as a Support System for Blind PeopleMobile Surveillance Siren Against Moving Object as a Support System for Blind People","authors":"D. H. Hareva, A. Sebastian, A. Mitra, Irene A. Lazarusli, C. Haryani","doi":"10.18178/joig.11.2.170-177","DOIUrl":"https://doi.org/10.18178/joig.11.2.170-177","url":null,"abstract":"Visually impaired people can use smartphone navigation applications to arrive at their destination. However, those applications do not provide the means to detect moving objects. This paper presents an Android application that uses the smartphone’s camera to provide real-time object detection. Images captured by the camera are to be processed digitally. The model then predicts objects from the processed image using a Convolutional Neural Network (CNN) stored in mobile devices. The model returns bounding boxes for each of the detected objects. These bounding boxes are used to calculate the distance from the object to the camera. The model used is SSD MobileNet V1, which is pre-trained using the Common Objects in Context (COCO) dataset. System testing is divided into object distance and accuracy testing. Results show that the margin of error for calculating distance is below 5% for distances under 8 meters. The mean average precision is 0.9393, while the mean average recall is 0.4479. It means that the system can recognize moving objects through the embedded model in a smartphone.","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83167665","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

Instant Counting & Vehicle Detection during Hajj Using Drones 在朝觐期间使用无人机进行即时计数和车辆检测

中国图象图形学报 Pub Date : 2023-06-01 DOI: 10.18178/joig.11.2.204-211

Abdullah M. Algamdi, Hammam M. AlGhamdi

{"title":"Instant Counting & Vehicle Detection during Hajj Using Drones","authors":"Abdullah M. Algamdi, Hammam M. AlGhamdi","doi":"10.18178/joig.11.2.204-211","DOIUrl":"https://doi.org/10.18178/joig.11.2.204-211","url":null,"abstract":"During the past decade, artificial intelligence technologies, especially Computer Vision (CV) technologies, have experienced significant breakthroughs due to the development of deep learning models, particularly Convolutional Neural Networks (CNNs). These networks have been utilized in various research applications, including astronomy, marine sciences, security, medicine, and pathology. In this paper, we build a framework utilizing CV technology to support decision-makers during the Hajj season. We collect and process real-time/instant images from multiple aircraft/drones, which follow the pilgrims while they move around the holy sites during Hajj. These images, taken by multiple drones, are processed in two stages. First, we purify the images collected from multiple drones and stitch them, producing one image that captures the whole holy site. Second, the stitched image is processed using a CNN to provide two pieces of information: (1) the number of buses and ambulances; and (2) the estimated count of pilgrims. This information could help decision-makers identify needs for further support during Hajj, such as logistics services, security personnel, and/or ambulances.","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72488825","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