Int. J. Image Graph.最新文献_第4页

A Review on Rice Crop Disease Classification Using Computational Approach 基于计算方法的水稻病害分类研究进展

Int. J. Image Graph. Pub Date : 2021-12-23 DOI: 10.1142/s0219467822400071

V. Malathi, M. P. Gopinath

引用次数: 2

Graph Theory-Based Brain Network Connectivity Analysis and Classification of Alzheimer's Disease 基于图论的阿尔茨海默病脑网络连通性分析与分类

Int. J. Image Graph. Pub Date : 2021-12-22 DOI: 10.1142/s021946782240006x

A. Thushara, C. UshadeviAmma, Ansamma John

引用次数: 0

Finger Vein Recognition Model for Biometric Authentication Using Intelligent Deep Learning 基于智能深度学习的指纹静脉识别模型

Int. J. Image Graph. Pub Date : 2021-12-22 DOI: 10.1142/s0219467822400046

M. Madhusudhan, V. U. Rani, Chetana Hegde

{"title":"Finger Vein Recognition Model for Biometric Authentication Using Intelligent Deep Learning","authors":"M. Madhusudhan, V. U. Rani, Chetana Hegde","doi":"10.1142/s0219467822400046","DOIUrl":"https://doi.org/10.1142/s0219467822400046","url":null,"abstract":"In recent years, biometric authentication systems have remained a hot research topic, as they can recognize or authenticate a person by comparing their data to other biometric data stored in a database. Fingerprints, palm prints, hand vein, finger vein, palm vein, and other anatomic or behavioral features have all been used to develop a variety of biometric approaches. Finger vein recognition (FVR) is a common method of examining the patterns of the finger veins for proper authentication among the various biometrics. Finger vein acquisition, preprocessing, feature extraction, and authentication are all part of the proposed intelligent deep learning-based FVR (IDL-FVR) model. Infrared imaging devices have primarily captured the use of finger veins. Furthermore, a region of interest extraction process is carried out in order to save the finger part. The shark smell optimization algorithm is used to tune the hyperparameters of the bidirectional long–short-term memory model properly. Finally, an authentication process based on Euclidean distance is performed, which compares the features of the current finger vein image to those in the database. The IDL-FVR model surpassed the earlier methods by accomplishing a maximum accuracy of 99.93%. Authentication is successful when the Euclidean distance is small and vice versa.","PeriodicalId":177479,"journal":{"name":"Int. J. Image Graph.","volume":"42 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120858131","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: 5

Pan-Sharpening for Spectral Details Preservation Via Convolutional Sparse Coding in Non-Subsampled Shearlet Space 基于非下采样Shearlet空间卷积稀疏编码的泛锐化光谱细节保存

Int. J. Image Graph. Pub Date : 2021-12-22 DOI: 10.1142/s0219467823500134

Dharaj. Sangani, R. Thakker, Sumankumar D. Panchal, Rajesh Gogineni

{"title":"Pan-Sharpening for Spectral Details Preservation Via Convolutional Sparse Coding in Non-Subsampled Shearlet Space","authors":"Dharaj. Sangani, R. Thakker, Sumankumar D. Panchal, Rajesh Gogineni","doi":"10.1142/s0219467823500134","DOIUrl":"https://doi.org/10.1142/s0219467823500134","url":null,"abstract":"The optical satellite sensors encounter certain constraints on producing high-resolution multispectral (HRMS) images. Pan-sharpening (PS) is a remote sensing image fusion technique, which is an effective mechanism to overcome the limitations of available imaging products. The prevalent issue in PS algorithms is the imbalance between spatial quality and spectral details preservation, thereby producing intensity variations in the fused image. In this paper, a PS method is proposed based on convolutional sparse coding (CSC) implemented in the non-subsampled shearlet transform (NSST) domain. The source images, panchromatic (PAN) and multispectral (MS) images, are decomposed using NSST. The resultant high-frequency bands are fused using adaptive weights determined from chaotic grey wolf optimization (CGWO) algorithm. The CSC-based model is employed to fuse the low-frequency bands. Further, an iterative filtering mechanism is developed to enhance the quality of fused image. Four datasets with different geographical content like urban area, vegetation, etc. and eight existing algorithms are used for evaluation of the proposed PS method. The comprehensive visual and quantitative results approve that the proposed method accomplishes considerable improvement in spatial and spectral details equivalence in the pan-sharpened image.","PeriodicalId":177479,"journal":{"name":"Int. J. Image Graph.","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124885086","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

Postal Automation System in Gurmukhi Script using Deep Learning 使用深度学习的Gurmukhi脚本邮政自动化系统

Int. J. Image Graph. Pub Date : 2021-12-22 DOI: 10.1142/s0219467823500055

Sandhya Sharma, Sheifali Gupta, Neeraj Kumar, Tanvi Arora

引用次数: 4

Spatio-Temporal Inference Transformer Network for Video Inpainting 用于视频喷漆的时空推理变压器网络

Int. J. Image Graph. Pub Date : 2021-12-18 DOI: 10.1142/s0219467823500079

Gajanan Tudavekar, S. Saraf, Sanjay R. Patil

引用次数: 0

Dimensionality Reduction and Visualization of Bharatanatyam Mudras 梵天手印的降维和可视化

Int. J. Image Graph. Pub Date : 2021-12-18 DOI: 10.1142/s0219467823500018

R. Raj, S. Dharan, T. T. Sunil

{"title":"Dimensionality Reduction and Visualization of Bharatanatyam Mudras","authors":"R. Raj, S. Dharan, T. T. Sunil","doi":"10.1142/s0219467823500018","DOIUrl":"https://doi.org/10.1142/s0219467823500018","url":null,"abstract":"Cultural dances are practiced all over the world. The study of various gestures of the performer using computer vision techniques can help in better understanding of these dance forms and for annotation purposes. Bharatanatyam is a classical dance that originated in South India. Bharatanatyam performer uses hand gestures (mudras), facial expressions and body movements to communicate to the audience the intended meaning. According to Natyashastra, a classical text on Indian dance, there are 28 Asamyukta Hastas (single-hand gestures) and 23 Samyukta Hastas (Double-hand gestures) in Bharatanatyam. Open datasets on Bharatanatyam dance gestures are not presently available. An exhaustive open dataset comprising of various mudras in Bharatanatyam was created. The dataset consists of 15[Formula: see text]396 distinct single-hand mudra images and 13[Formula: see text]035 distinct double-hand mudra images. In this paper, we explore the dataset using various multidimensional visualization techniques. PCA, Kernel PCA, Local Linear Embedding, Multidimensional Scaling, Isomap, t-SNE and PCA–t-SNE combination are being investigated. The best visualization for exploration of the dataset is obtained using PCA–t-SNE combination.","PeriodicalId":177479,"journal":{"name":"Int. J. Image Graph.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130050939","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

A New Method for Arabic Text Detection in Natural Scene Images 自然场景图像中阿拉伯语文本检测的新方法

Int. J. Image Graph. Pub Date : 2021-12-17 DOI: 10.1142/s0219467823500109

Houda Gaddour, S. Kanoun, N. Vincent

{"title":"A New Method for Arabic Text Detection in Natural Scene Images","authors":"Houda Gaddour, S. Kanoun, N. Vincent","doi":"10.1142/s0219467823500109","DOIUrl":"https://doi.org/10.1142/s0219467823500109","url":null,"abstract":"Text in scene images can provide useful and vital information for content-based image analysis. Therefore, text detection and script identification in images are an important task. In this paper, we propose a new method for text detection in natural scene images, particularly for Arabic text, based on a bottom-up approach where four principal steps can be highlighted. The detection of extremely stable and homogeneous regions of interest (ROIs) is based on the Color Stability and Homogeneity Regions (CSHR) proposed technique. These regions are then labeled as textual or non-textual ROI. This identification is based on a structural approach. The textual ROIs are grouped to constitute zones according to spatial relations between them. Finally, the textual or non-textual nature of the constituted zones is refined. This last identification is based on handcrafted features and on features built from a Convolutional Neural Network (CNN) after learning. The proposed method was evaluated on the databases used for text detection in natural scene images: the competitions organized in 2017 edition of the International Conference on Document Analysis and Recognition (ICDAR2017), the Urdu-text database and our Natural Scene Image Database for Arabic Text detection (NSIDAT) database. The obtained experimental results seem to be interesting.","PeriodicalId":177479,"journal":{"name":"Int. J. Image Graph.","volume":"842 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123295540","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

Multiple Imputation by Chained Equations-K-Nearest Neighbors and Deep Neural Network Architecture for Kidney Disease Prediction 基于链方程- k近邻和深度神经网络结构的多重归算肾脏疾病预测

Int. J. Image Graph. Pub Date : 2021-12-17 DOI: 10.1142/s0219467823500146

M. Fathima, R. Hariharan, S. Raja

{"title":"Multiple Imputation by Chained Equations-K-Nearest Neighbors and Deep Neural Network Architecture for Kidney Disease Prediction","authors":"M. Fathima, R. Hariharan, S. Raja","doi":"10.1142/s0219467823500146","DOIUrl":"https://doi.org/10.1142/s0219467823500146","url":null,"abstract":"Chronic kidney disease (CKD) is a health concern that affects people all over the world. Kidney dysfunction or impaired kidney functions are the causes of CKD. The machine learning-based prediction models are used to determine the risk level of CKD and assist healthcare practitioners in delaying and preventing the disease’s progression. The researchers proposed many prediction models for determining the CKD risk level. Although these models performed well, their precision is limited since they do not handle missing values in the clinical dataset adequately. The missing values of a clinical dataset can degrade the training outcomes that leads to false predictions. Thus, imputing missing values increases the prediction model performance. This proposed work developed a novel imputation technique by combining Multiple Imputation by Chained Equations and [Formula: see text]-Nearest Neighbors (MICE–KNN) for imputing the missing values. The experimental results show that MICE–KNN accurately predicts the missing values, and the Deep Neural Network (DNN) improves the prediction performance of the CKD model. Various metrics like mean absolute error, accuracy, specificity, Matthews correlation coefficient, the area under the curve, [Formula: see text]-score, sensitivity, and precision have been used to evaluate the proposed CKD model performance. The performance analysis exhibits that MICE–KNN with deep learning outperforms other classifiers. According to our experimental study, the MICE–KNN imputation algorithm with DNN is more appropriate for predicting the kidney disease.","PeriodicalId":177479,"journal":{"name":"Int. J. Image Graph.","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123292001","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

A Survey on Various Deep Learning Algorithms for an Efficient Facial Expression Recognition System 面向高效面部表情识别系统的各种深度学习算法综述

Int. J. Image Graph. Pub Date : 2021-12-15 DOI: 10.1142/s0219467822400058

Rudranath Banerjee, S. De, Shouvik Dey

引用次数: 1