中国图象图形学报最新文献

A Hybrid DWT-SVD Based Adaptive Image Watermarking Scheme 基于 DWT-SVD 的混合自适应图像水印方案

中国图象图形学报 Pub Date : 2023-12-01 DOI: 10.18178/joig.11.4.414-427

Sachin Gaur, Navneet Tripathi, Jyoti Pandey

{"title":"A Hybrid DWT-SVD Based Adaptive Image Watermarking Scheme","authors":"Sachin Gaur, Navneet Tripathi, Jyoti Pandey","doi":"10.18178/joig.11.4.414-427","DOIUrl":"https://doi.org/10.18178/joig.11.4.414-427","url":null,"abstract":"In the digital age, protecting the ownership and data veracity of digital documents is a major challenge. To address the issues concerning copyright protection and data verification of digital media, digital watermarking has emerged as a solution. In this paper, we aspire to make a modest contribution to this emerging and exciting field by presenting our proposed adaptive hybrid image watermarking approach that combines Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD). Our method involves applying DWT to both the host image and watermark, followed by singular decomposition using SVD on the Low-Low (LL) component of both images. Now modify the singular values of the host image by the singular values of the watermark, and then inverse SVD is applied, followed by inverse DWT, to obtain the watermarked image. After that, the reverse process is applied to obtain the watermark image. Finally, we evaluate our approach’s performance by measuring the Peak Signal-to-Noise Ratio (PSNR) between the original and watermarked image as well as the Normalized Cross-Correlation (NCC) between the original and extracted watermark. Simulation results indicate that the proposed method is rich in terms of robustness, imperceptibility and capacity than the previously presented schemes.","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138620978","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

Reg-PointNet++: A CNN Network Based on PointNet++ Architecture for 3D Reconstruction of 3D Objects Modeled by Supershapes Reg-PointNet++：基于 PointNet++ 架构的 CNN 网络，用于超形状建模的三维物体的三维重建

中国图象图形学报 Pub Date : 2023-12-01 DOI: 10.18178/joig.11.4.405-413

Hassnae Remmach, Raja Mouachi, M. Sadgal, Aziz El Fazziki

{"title":"Reg-PointNet++: A CNN Network Based on PointNet++ Architecture for 3D Reconstruction of 3D Objects Modeled by Supershapes","authors":"Hassnae Remmach, Raja Mouachi, M. Sadgal, Aziz El Fazziki","doi":"10.18178/joig.11.4.405-413","DOIUrl":"https://doi.org/10.18178/joig.11.4.405-413","url":null,"abstract":"The use of 3D reconstruction in computer vision applications has opened up new avenues for research and development. It has a significant impact on a range of industries, from healthcare to robotics, by improving the performance and abilities of computer vision systems. In this paper we aim to improve 3D reconstruction quality and accuracy. The objective is to develop a model that can learn to extract features, estimate a Supershape parameters and reconstruct 3D directly from input points cloud. In this regard, we present a continuity of our latest works, using a CNN-based Multi-Output and Multi-Task Regressor, for 3D reconstruction from 3D point cloud. We propose another new approach in order to refine our previous methodology and expand our findings. It is about “Reg-PointNet++”, which is mainly based on a PointNet++ architecture adapted for multi-task regression, with the goal of reconstructing a 3D object modeled by Supershapes from 3D point cloud. Given the difficulties encountered in applying convolution to point clouds, our approach is based on the PointNet ++ architecture. It is used to extract features from the 3D point cloud, which are then fed into a Multi-task Regressor for predicting the Supershape parameters needed to reconstruct the shape. The approach has shown promising results in reconstructing 3D objects modeled by Supershapes, demonstrating improved accuracy and robustness to noise and outperforming existing techniques. Visually, the predicted shapes have a high likelihood with the real shapes, as well as a high accuracy rate in a very reasonable number of iterations. Overall, the approach presented in the paper has the potential to significantly improve the accuracy and efficiency of 3D reconstruction, enabling its use in a wider range of applications.","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138627535","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

Roselle Pest Detection and Classification Using Threshold and Template Matching 利用阈值和模板匹配进行洛泽尔害虫检测和分类

中国图象图形学报 Pub Date : 2023-12-01 DOI: 10.18178/joig.11.4.330-342

Ade Bastian, Adie Iman Nurzaman, Tri Ferga Prasetyo, Sri Fatimah

引用次数: 0

An Enhanced Security in Medical Image Encryption Using Dynamic Chaotic Fuzzy Based Technique 利用基于动态混沌模糊技术提高医学图像加密的安全性

中国图象图形学报 Pub Date : 2023-12-01 DOI: 10.18178/joig.11.4.376-383

Snehashish Bhattacharjee, Mousumi Gupta, Biswajoy Chatterjee

{"title":"An Enhanced Security in Medical Image Encryption Using Dynamic Chaotic Fuzzy Based Technique","authors":"Snehashish Bhattacharjee, Mousumi Gupta, Biswajoy Chatterjee","doi":"10.18178/joig.11.4.376-383","DOIUrl":"https://doi.org/10.18178/joig.11.4.376-383","url":null,"abstract":"As IoT and cloud computing have grown in popularity, medical images are now often transmitted between devices or accessed directly from the cloud. With this, the security is always a concern as these images are prone to many types of attack. We have proposed a proven method that is efficient in terms of security, time complexity, and integrity in order to be cloud-friendly so that it may be launched into the cloud and made accessible to users at any time. The goal of the work is to create a dynamic key that, depending on fuzzy values, alters the reproduction rate parameters with each repetition. By applying the last chaotic value created from the previous iteration, the fuzzy triangular membership function has been used in this manner to generate the reproduction rate parameter. The uniqueness and major benefit of the suggested strategy are that it can increase the security of the algorithm that makes use of a chaotic map and a static key. The method has been put forth when designing algorithms so that it should not only demonstrate security against different attacks but also provide efficiency towards computational complexity. The technique has been tested against a set of images and an existing algorithm using a variety of security metrics, including the correlation coefficient, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), and entropy. It has been determined from the comparative analysis that the proposed approach can make the existing algorithm more secure.","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138615226","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

Melanoma Detection Based on SVM Using MATLAB 使用 MATLAB 基于 SVM 检测黑色素瘤

中国图象图形学报 Pub Date : 2023-12-01 DOI: 10.18178/joig.11.4.353-358

Radhwan M. W. Khaleel, N. M. Basheer

引用次数: 0

Using Random Forest Algorithm to Grading Mango's Quality Based on External Features Extracted from Captured Images 使用随机森林算法根据从拍摄图像中提取的外部特征对芒果的质量进行分级

中国图象图形学报 Pub Date : 2023-12-01 DOI: 10.18178/joig.11.4.391-396

Nguyen Minh Trieu, Nguyen Truong Thinh

引用次数: 0

Human Action Recognition with Skeleton and Infrared Fusion Model 利用骨架和红外融合模型识别人体动作

中国图象图形学报 Pub Date : 2023-12-01 DOI: 10.18178/joig.11.4.309-320

Amine Mansouri, Toufik Bakir, S. Femmam

引用次数: 0

An Intra- and Inter-Modality Fusion Model with Invariant- and Specific-Constraints Using MR Images for Prediction of Glioma Isocitrate Dehydrogenase Mutation Status 使用磁共振图像预测胶质瘤异柠檬酸脱氢酶突变状态的具有无创和特异性限制的模内和模间融合模型

中国图象图形学报 Pub Date : 2023-12-01 DOI: 10.18178/joig.11.4.321-329

Xiaoyu Shi, Yinhao Li, Yen-wei Chen, Jingliang Cheng, J. Bai, Guohua Zhao

{"title":"An Intra- and Inter-Modality Fusion Model with Invariant- and Specific-Constraints Using MR Images for Prediction of Glioma Isocitrate Dehydrogenase Mutation Status","authors":"Xiaoyu Shi, Yinhao Li, Yen-wei Chen, Jingliang Cheng, J. Bai, Guohua Zhao","doi":"10.18178/joig.11.4.321-329","DOIUrl":"https://doi.org/10.18178/joig.11.4.321-329","url":null,"abstract":"In the 2021 World Health Organization classification of gliomas, it is proposed that Isocitrate Dehydrogenase (IDH) plays a key role. The prognosis of glioma is largely affected by IDH mutation status. Therefore, IDH mutation status needs to be predicted in advance before surgery. In the past decade, with the development of machine learning, more and more machine learning methods, especially deep learning methods, have been applied to the development of computer-aided diagnosis systems. At present, in this field, many deep learning and radiomics based methods have been proposed for IDH prediction using multimodal Magnetic Resonance Imaging (MRI). In this study, we proposed an intra- and inter-modality fusion model with invariant- and specific- constraints to improve the performance of IDH status prediction. First, MRI-based radiomics features were fused with deep learning features in each modality (intra-modality fusion) and then the features extracted from each modality of brain MRI were fused by using an inter-modality fusion model with invariant and specific constraints. We experimented our proposed method on the dataset provided by the Affiliated Hospital of Zhengzhou University in Zhengzhou, China and demonstrated the effectiveness of the proposed method. In our study, we propose two inter-modality fusion models, and our experimental results show that our best proposed method outperformed state-of-the-art methods with an accuracy of 0.79, precision of 0.80, recall of 0.75, and F1 score of 0.78. Thus, we predicted the IDH mutation status for glioma treatment with a 2% increase in accuracy and 4% increase in precision to predict the IDH mutation status for glioma treatment.","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138623852","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

Evaluation of SSD Architecture for Small Size Object Detection: A Case Study on UAV Oil Pipeline MonitoringEvaluation of SSD Architecture for Small Size Object Detection: A Case Study on UAV Oil Pipeline Monitoring 用于小尺寸物体检测的固态硬盘架构评估：用于小尺寸物体检测的固态硬盘架构评估：无人机石油管道监测案例研究固态硬盘架构评估：无人机石油管道监测案例研究无人机石油管道监控案例研究

中国图象图形学报 Pub Date : 2023-12-01 DOI: 10.18178/joig.11.4.384-390

Annisa Istiqomah Arrahmah, Rissa Rahmania, D. E. Saputra

{"title":"Evaluation of SSD Architecture for Small Size Object Detection: A Case Study on UAV Oil Pipeline MonitoringEvaluation of SSD Architecture for Small Size Object Detection: A Case Study on UAV Oil Pipeline Monitoring","authors":"Annisa Istiqomah Arrahmah, Rissa Rahmania, D. E. Saputra","doi":"10.18178/joig.11.4.384-390","DOIUrl":"https://doi.org/10.18178/joig.11.4.384-390","url":null,"abstract":"Oil pipeline monitoring using Unmanned Airborne Vehicles (UAV) can be done by utilizing Deep Learning. Deep Learning can be used to automatically detect harmed or unauthorized objects near the pipeline for further action by the authority. Input video in the pipeline area taken from the UAV has unique characteristics. It has low resolution with dense composition object in the image. The detected object also has a small scale as the objects are far away from the UAV. Thus, the selection of the Deep Learning algorithm is important to get a desirable result with the following conditions. Single Shot Multi-Box (SSD) is one of the popular Deep Learning algorithms with fast calculation compared to others and suitable for real-time object detection. Previous works on this topic using low to medium altitude dataset (20–200 m). This paper provides an evaluation of SSD implementation to detect vehicles on high-altitude dataset (300 m). As much as 2482 dataset is fed into SSD architecture and trained to detect 3 class of vehicles. The result shows the mAP and mAR are 0.026360 and 0.067377, respectively. However, the low lost function value shows that the model is able to classify the object correctly. In conclusion, the SSD cannot process low density information to correctly locate the object.","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138614664","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

Improving Brain Tumor Classification Efficacy through the Application of Feature Selection and Ensemble Classifiers 应用特征选择和集合分类器提高脑肿瘤分类效率

中国图象图形学报 Pub Date : 2023-12-01 DOI: 10.18178/joig.11.4.397-404

Y. Yuhandri, A. Windarto, Muhammad Noor Hasan Siregar

{"title":"Improving Brain Tumor Classification Efficacy through the Application of Feature Selection and Ensemble Classifiers","authors":"Y. Yuhandri, A. Windarto, Muhammad Noor Hasan Siregar","doi":"10.18178/joig.11.4.397-404","DOIUrl":"https://doi.org/10.18178/joig.11.4.397-404","url":null,"abstract":"Accurate brain tumor detection is crucial due to its high mortality rate. However, existing automated methods suffer from limited accuracy and high false-positive rates. In this study, we aimed to improve brain tumor classification by comparing 17 different classifiers organized into six groups: Decision Tree (DT) Model, Support Vector Machine (SVM), Naive Bayes Classifier, Logistic Regression, Generalized Linear Model (GLM) Classifier, and Neural Network. We utilized a dataset of 3,762 Magnetic Resonance Imaging (MRI) scans of brain tumors from Kaggle, with each image having dimensions of 240 × 240 pixels and labeled as tumor or non-tumor. Our approach involved three main steps: extracting visual information using 17 predictor classes, optimizing feature extraction through weight optimization, and comparing different sets of classifier models. We evaluated the models’ performance using the confusion matrix and Receiver Operating Characteristics (ROC) curves. Our results showed that optimizing feature selection and utilizing ensemble classifiers improved the accuracy of brain tumor classification. The DT Model with ensemble classifiers emerged as the best-performing classifier, achieving an accuracy of 98.11% and an AUC of 0.99. Notably, Random Tree (RT) exhibited the highest accuracy within the ensemble classifier set, with a significant increase compared to other models. Our proposed method outperformed the standard approach, demonstrating its potential for enhancing brain tumor detection accuracy. This study contributes to the field by providing a more accurate method for detecting brain tumors, potentially enabling earlier detection and improved patient outcomes. Future research should focus on further improving brain tumor diagnosis and treatment through the application of machine learning techniques.","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138614904","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