中国图象图形学报最新文献_第5页

Responses to Sad Emotion in Autistic and Normal Developing Children: Is There a Difference? 自闭症儿童和正常发育儿童对悲伤情绪的反应:有区别吗?

中国图象图形学报 Pub Date : 2023-03-01 DOI: 10.18178/joig.11.1.40-46

Mohamed Basel Almourad, Emad Bataineh, Zelal Wattar

引用次数: 3

Evaluation of Transfer Learning for Handwritten Character Classification Using Small Training Samples 小样本手写体字符分类迁移学习评价

中国图象图形学报 Pub Date : 2023-03-01 DOI: 10.18178/joig.11.1.21-25

Y. Mitani, Naoki Yamaguchi, Y. Fujita, Y. Hamamoto

{"title":"Evaluation of Transfer Learning for Handwritten Character Classification Using Small Training Samples","authors":"Y. Mitani, Naoki Yamaguchi, Y. Fujita, Y. Hamamoto","doi":"10.18178/joig.11.1.21-25","DOIUrl":"https://doi.org/10.18178/joig.11.1.21-25","url":null,"abstract":"In pattern recognition fields, it is worthwhile to develop a pattern recognition system that hears one and knows ten. Recently, classification of printed characters that are the same fonts is almost possible, but classification of handwritten characters is still difficult. On the other hand, there are a large number of writing systems in the world, and there is a need for efficient character classification even with a small sample. Deep learning is one of the most effective approaches for image recognition. Despite this, deep learning causes overtrains easily, particularly when the number of training samples is small. For this reason, deep learning requires a large number of training samples. However, in a practical pattern recognition problem, the number of training samples is usually limited. One method for overcoming this situation is the use of transfer learning, which is pretrained by many samples. In this study, we evaluate the generalization performance of transfer learning for handwritten character classification using a small training sample size. We explore transfer learning using a fine-tuning to fit a small training sample. The experimental results show that transfer learning was more effective for handwritten character classification than convolution neural networks. Transfer learning is expected to be one method that can be used to design a pattern recognition system that works effectively even with a small sample.","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":"82 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77906336","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

Efficient Hybrid Algorithm for Human Action Recognition 人体动作识别的高效混合算法

中国图象图形学报 Pub Date : 2023-03-01 DOI: 10.18178/joig.11.1.72-81

Mostafa A. Abdelrazik, A. Zekry, W. A. Mohamed

{"title":"Efficient Hybrid Algorithm for Human Action Recognition","authors":"Mostafa A. Abdelrazik, A. Zekry, W. A. Mohamed","doi":"10.18178/joig.11.1.72-81","DOIUrl":"https://doi.org/10.18178/joig.11.1.72-81","url":null,"abstract":"Recently, researchers have sought to find the ideal way to recognize human actions through video using artificial intelligence due to the multiplicity of applications that rely on it in many fields. In general, the methods have been divided into traditional methods and deep learning methods, which have provided a qualitative leap in the field of computer vision. Convolutional neural network CNN and recurrent neural network RNN are the most popular algorithms used with images and video. The researchers combined the two algorithms to search for the best results in a lot of research. In an attempt to obtain improved results in motion recognition through video, we present in this paper a combined algorithm, which is divided into two main parts, CNN and RNN. In the first part there is a preprocessing stage to make the video frame suitable for the input of both CNN networks which consist of a fusion of Inception-ResNet-V2 and GoogleNet to obtain activations, with the previously trained wights in Inception-ResNet-V2 and GoogleNet and then passed to a deep Gated Recurrent Units (GRU) connected to a fully connected SoftMax layer to recognize and distinguish the human action in the video. The results show that the proposed algorithm gives better accuracy of 97.97% with the UCF101 dataset and 73.12% in the hdmb51 data set compared to those present in the related literature.","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74601985","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: 3

Development of a Previsualization Proxy Plug-in Tool 一个可视化代理插件工具的开发

中国图象图形学报 Pub Date : 2023-03-01 DOI: 10.18178/joig.11.1.26-31

Balgum Song

引用次数: 0

Optical Flow-Based Algorithm Analysis to Detect Human Emotion from Eye Movement-Image Data 基于光流的眼动图像情感检测算法分析

中国图象图形学报 Pub Date : 2023-03-01 DOI: 10.18178/joig.11.1.53-60

T. T. Zizi, S. Ramli, Muslihah Wook, M. Shukran

引用次数: 5

Plant Species Classification Using Leaf Edge Feature Combination with Morphological Transformations and SIFT Key Point 叶缘特征结合形态变换和SIFT关键点的植物物种分类

中国图象图形学报 Pub Date : 2023-03-01 DOI: 10.18178/joig.11.1.91-97

Jiraporn Thomkaew, Sarun Intakosum

引用次数: 1

Deep Learning in Grapevine Leaves Varieties Classification Based on Dense Convolutional Network 基于密集卷积网络的葡萄叶品种深度学习分类

中国图象图形学报 Pub Date : 2023-03-01 DOI: 10.18178/joig.11.1.98-103

H. A. Ahmed, Hersh M. Hama, S. I. Jalal, M. Ahmed

引用次数: 5

Pineapple Sweetness Classification Using Deep Learning Based on Pineapple Images 基于菠萝图像的深度学习菠萝甜度分类

中国图象图形学报 Pub Date : 2023-03-01 DOI: 10.18178/joig.11.1.47-52

Sarunya Kanjanawattana, Worawit Teerawatthanaprapha, Panchalee Praneetpholkrang, G. Bhakdisongkhram, Suchada Weeragulpiriya

引用次数: 2

Application of Medical Image 3D Visualization Web Platform in Auxiliary Diagnosis and Preoperative Planning 医学影像三维可视化Web平台在辅助诊断和术前规划中的应用

中国图象图形学报 Pub Date : 2023-03-01 DOI: 10.18178/joig.11.1.32-39

Shengyu Bai, Chenxin Ma, Xinjun Wang, Shaolong Zhou, Hongyu Jiang, Ling Ma, Huiqin Jiang

{"title":"Application of Medical Image 3D Visualization Web Platform in Auxiliary Diagnosis and Preoperative Planning","authors":"Shengyu Bai, Chenxin Ma, Xinjun Wang, Shaolong Zhou, Hongyu Jiang, Ling Ma, Huiqin Jiang","doi":"10.18178/joig.11.1.32-39","DOIUrl":"https://doi.org/10.18178/joig.11.1.32-39","url":null,"abstract":"Three-dimensional visualization of medical image data can enable doctors to observe images from more angles and higher dimensions. It is of great significance for doctors to assist in diagnosis and preoperative planning. Most 3D visualization systems are based on desktop applications, which are too dependent on hardware and operating system. This makes it difficult to use across platforms and maintain. Web-based systems tend to have limited capabilities. To this end, we developed a web application, which not only provides DICOM (Digital Imaging and Communications in Medicine) image browsing and annotation functions, but also provides three-dimensional post-processing functions of multiplanar reconstruction, volume rendering, lung parenchyma segmentation and brain MRI (Magnetic Resonance Imaging) analysis. In order to improve the rendering speed, we use the Marching Cube algorithm for 3D reconstruction in the background in an asynchronous way, and save the reconstructed model as glTF (GL Transmission Format). At the same time, Draco compression algorithm is used to optimize the glTF model to achieve more efficient rendering. After performance evaluation, the system reconstructed a CT (Computed Tomography) series of 242 slices and the optimized model was only 6.37mb with a rendering time of less than 2.5s. Three-dimensional visualization of the lung parenchyma clearly shows the volume, location, and shape of pulmonary nodules. The segmentation and reconstruction of different brain tissues can reveal the spatial three-dimensional structure and adjacent relationship of glioma in the brain, which has great application value in auxiliary diagnosis and preoperative planning.","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86869350","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

ImECGnet: Cardiovascular Disease Classification from Image-Based ECG Data Using a Multibranch Convolutional Neural Network ImECGnet:使用多分支卷积神经网络从基于图像的心电数据中分类心血管疾病

中国图象图形学报 Pub Date : 2023-03-01 DOI: 10.18178/joig.11.1.9-14

Amir Ghahremani, C. Lofi

{"title":"ImECGnet: Cardiovascular Disease Classification from Image-Based ECG Data Using a Multibranch Convolutional Neural Network","authors":"Amir Ghahremani, C. Lofi","doi":"10.18178/joig.11.1.9-14","DOIUrl":"https://doi.org/10.18178/joig.11.1.9-14","url":null,"abstract":"Reliable Cardiovascular Disease (CVD) classification performed by a smart system can assist medical doctors in recognizing heart illnesses in patients more efficiently and effectively. Electrocardiogram (ECG) signals are an important diagnostic tool as they are already available early in the patients’ health diagnosis process and contain valuable indicators for various CVDs. Most ECG processing methods represent ECG data as a time series, often as a matrix with each row containing the measurements of a sensor lead; and/or the transforms of such time series like wavelet power spectrums. While methods processing such time-series data have been shown to work well in benchmarks, they are still highly dependent on factors like input noise and sequence length, and cannot always correlate lead data from different sensors well. In this paper, we propose to represent ECG signals incorporating all lead data plotted as a single image, an approach not yet explored by literature. We will show that such an image representation combined with our newly proposed convolutional neural network specifically designed for CVD classification can overcome the aforementioned shortcomings. The proposed (Convolutional Neural Network) CNN is designed to extract features representing both the proportional relationships of different leads to each other and the characteristics of each lead separately. Empirical validation on the publicly available PTB, MIT-BIH, and St.-Petersburg benchmark databases shows that the proposed method outperforms time seriesbased state-of-the-art approaches, yielding classification accuracy of 97.91%, 99.62%, and 98.70%, respectively.","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87574480","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