Int. J. Image Graph.最新文献_第9页

Secret Key-Based Image Steganography in Spatial Domain 基于密钥的空间域图像隐写

Int. J. Image Graph. Pub Date : 2021-05-06 DOI: 10.1142/S0219467822500140

Rajashree Gajabe, Syed Taqi Ali

{"title":"Secret Key-Based Image Steganography in Spatial Domain","authors":"Rajashree Gajabe, Syed Taqi Ali","doi":"10.1142/S0219467822500140","DOIUrl":"https://doi.org/10.1142/S0219467822500140","url":null,"abstract":"Day by day, the requirement for secure communication among users is rising in a digital world, to protect the message from the undesirable users. Steganography is a methodology that satisfies the user’s necessity of secure communication by inserting a message into different formats. This paper proposes a secret key-based image steganography to secure the message by concealing the grayscale image inside a cover image. The proposed technique shares the 20 characters long secret key between two clients where the initial eight characters of a secret key are utilized for bit permutation of characters and pixels while the last 12 characters of secret key decide the encryption keys and position of pixels of a grayscale image into the cover. The grayscale image undergoes operation such as encryption and chaotic baker followed by its hiding in a cover to form a stego image. The execution of the proposed strategy is performed on Matlab 2018. It shows that the proposed approach manages to store the maximum message of size 16[Formula: see text]KB into the cover of size [Formula: see text]. The image quality of stego images has been evaluated using PSNR, MSE. For a full payload of 16[Formula: see text]KB, PSNR is around 51[Formula: see text]dB to 53[Formula: see text]dB which is greater than satisfactory PSNR.","PeriodicalId":177479,"journal":{"name":"Int. J. Image Graph.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130656035","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

Analytical Review of Biometric Technology Employing Vivid Modalities 采用生动模式的生物识别技术分析综述

Int. J. Image Graph. Pub Date : 2021-04-26 DOI: 10.1142/S0219467822500048

Navdeep Kaur

引用次数: 1

Surface Reconstruction: Roles in the Field of Computer Vision and Computer Graphics 表面重建:在计算机视觉和计算机图形学领域中的作用

Int. J. Image Graph. Pub Date : 2021-04-26 DOI: 10.1142/S0219467822500085

Soumia Dhar, Shyamosree Pal

引用次数: 0

Histogram of Marked Background (HMB) Feature Extraction Method for Arabic Handwriting Recognition 阿拉伯语手写识别的标记背景直方图(HMB)特征提取方法

Int. J. Image Graph. Pub Date : 2021-04-24 DOI: 10.1142/S0219467822500152

M. Gagaoua, H. Ghilas, A. Tari, M. Cheriet

引用次数: 3

A Study on Darwinian Crow Search Algorithm for Multilevel Thresholding 多级阈值的达尔文乌鸦搜索算法研究

Int. J. Image Graph. Pub Date : 2021-04-22 DOI: 10.1142/S0219467822500127

E. Ehsaeyan, A. Zolghadrasli

{"title":"A Study on Darwinian Crow Search Algorithm for Multilevel Thresholding","authors":"E. Ehsaeyan, A. Zolghadrasli","doi":"10.1142/S0219467822500127","DOIUrl":"https://doi.org/10.1142/S0219467822500127","url":null,"abstract":"Multilevel thresholding is a basic method in image segmentation. The conventional image multilevel thresholding algorithms are computationally expensive when the number of decomposed segments is high. In this paper, a novel and powerful technique is suggested for Crow Search Algorithm (CSA) devoted to segmentation applications. The main contribution of our work is to adapt Darwinian evolutionary theory with heuristic CSA. First, the population is divided into specified groups and each group tries to find better location in the search space. A policy of encouragement and punishment is set on searching agents to avoid being trapped in the local optimum and premature solutions. Moreover, to increase the convergence rate of the proposed method, a gray-scale map is applied to out-boundary agents. Ten test images are selected to measure the ability of our algorithm, compared with the famous procedure, energy curve method. Two popular entropies i.e. Otsu and Kapur are employed to evaluate the capability of the introduced algorithm. Eight different search algorithms are implemented and compared to the introduced method. The obtained results show that our method, compared with the original CSA, and other heuristic search methods, can extract multi-level thresholding more efficiently.","PeriodicalId":177479,"journal":{"name":"Int. J. Image Graph.","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128248721","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 Two-Stage PAN-Sharpening Algorithm Based on Sparse Representation for Spectral Distortion Reduction 基于稀疏表示的两阶段泛锐化光谱失真抑制算法

Int. J. Image Graph. Pub Date : 2021-04-22 DOI: 10.1142/S0219467822500073

Rajesh Gogineni, Dharaj. Sangani

{"title":"A Two-Stage PAN-Sharpening Algorithm Based on Sparse Representation for Spectral Distortion Reduction","authors":"Rajesh Gogineni, Dharaj. Sangani","doi":"10.1142/S0219467822500073","DOIUrl":"https://doi.org/10.1142/S0219467822500073","url":null,"abstract":"Inspite of technological advancement, inherent processing capability of current age sensors limits the desired details in the acquired image for variety of remote sensing applications. Pan-sharpening is a prominent scheme to integrate the essential spatial details inferred from panchromatic (PAN) image and the desired spectral information of multispectral (MS) image. This paper presents an effective two-stage pan-sharpening method to produce high resolution multispectral (HRMS) image. The proposed method is based on the premise that the HRMS image can be formulated as an amalgam of spectral and spatial components. The spectral components are estimated by processing the interpolated MS image with a filter approximated with modulation transfer function (MTF) of the sensor. Sparse representation theory is adapted to construct the spatial components. The high-frequency details extracted from the PAN image and its low resolution variant are utilized to construct dual dictionaries. The dictionaries are jointly learned by an efficient training algorithm to enhance the adaptability. The hypothesis of sparse coefficients invariance over scales is also incorporated to reckon the appropriate spatial information. Further, an iterative filtering mechanism is developed to enhance the quality of fused image. Four distinct datasets generated from QuickBird, IKONOS, Pléiades and WorldView-2 sensors are used for experimentation. The comprehensive assessment at reduced-scale and full-scale persuade the effectiveness of proposed method in the retention of spectral information and intensification of the spatial details.","PeriodicalId":177479,"journal":{"name":"Int. J. Image Graph.","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117155672","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: 2

Human Activity Recognition Algorithm in Video Sequences Based on Integration of Magnitude and Orientation Information of Optical Flow 基于光流大小和方向信息集成的视频序列人体活动识别算法

Int. J. Image Graph. Pub Date : 2021-04-22 DOI: 10.1142/S0219467822500097

A. Kushwaha, A. Khare, M. Khare

{"title":"Human Activity Recognition Algorithm in Video Sequences Based on Integration of Magnitude and Orientation Information of Optical Flow","authors":"A. Kushwaha, A. Khare, M. Khare","doi":"10.1142/S0219467822500097","DOIUrl":"https://doi.org/10.1142/S0219467822500097","url":null,"abstract":"Human activity recognition from video sequences has emerged recently as pivotal research area due to its importance in a large number of applications such as real-time surveillance monitoring, healthcare, smart homes, security, behavior analysis, and many more. However, lots of challenges also exist such as intra-class variations, object occlusion, varying illumination condition, complex background, camera motion, etc. In this work, we introduce a novel feature descriptor based on the integration of magnitude and orientation information of optical flow and histogram of oriented gradients which gives an efficient and robust feature vector for the recognition of human activities for real-world environment. In the proposed approach first we computed magnitude and orientation of the optical flow separately then a local-oriented histogram of magnitude and orientation of motion flow vectors are computed using histogram of oriented gradients followed by linear combination feature fusion strategy. The resultant features are then processed by a multiclass Support Vector Machine (SVM) classifier for activity recognition. The experimental results are performed over different publically available benchmark video datasets such as UT interaction, CASIA, and HMDB51 datasets. The effectiveness of the proposed approach is evaluated in terms of six different performance parameters such as accuracy, precision, recall, specificity, [Formula: see text]-measure, and Matthew’s correlation coefficient (MCC). To show the significance of the proposed method, it is compared with the other state-of-the-art methods. The experimental result shows that the proposed method performs well in comparison to other state-of-the-art methods.","PeriodicalId":177479,"journal":{"name":"Int. J. Image Graph.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131689147","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: 8

Biometric Authentication Using Finger-Vein Patterns with Deep-Learning and Discriminant Correlation Analysis 基于深度学习和判别相关分析的指纹静脉模式生物识别认证

Int. J. Image Graph. Pub Date : 2021-04-22 DOI: 10.1142/s0219467822500139

Aldjia Boucetta, Leila Boussaad

{"title":"Biometric Authentication Using Finger-Vein Patterns with Deep-Learning and Discriminant Correlation Analysis","authors":"Aldjia Boucetta, Leila Boussaad","doi":"10.1142/s0219467822500139","DOIUrl":"https://doi.org/10.1142/s0219467822500139","url":null,"abstract":"Finger-vein identification, a biometric technology that uses vein patterns in the human finger to identify people. In recent years, it has received increasing attention due to its tremendous advantages compared to fingerprint characteristics. Moreover, Deep-Convolutional Neural Networks (Deep-CNN) appeared to be highly successful for feature extraction in the finger-vein area, and most of the proposed works focus on new Convolutional Neural Network (CNN) models, which require huge databases for training, a solution that may be more practicable in real world applications, is to reuse pretrained Deep-CNN models. In this paper, a finger-vein identification system is proposed, which uses Squeezenet pretrained Deep-CNN model as feature extractor from the left and the right finger vein patterns. Then, combines this Deep-based features by using a feature-level Discriminant Correlation Analysis (DCA) to reduce feature dimensions and to give the most relevant features. Finally, these composite feature vectors are used as input data for a Support Vector Machine (SVM) classifier, in an identification stage. This method is tested on two widely available finger vein databases, namely SDUMLA-HMT and FV-USM. Experimental results show that the proposed finger vein identification system achieves significant high mean accuracy rates.","PeriodicalId":177479,"journal":{"name":"Int. J. Image Graph.","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133320001","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: 4

Classification of Low-Resolution Satellite Images Using Fractal Augmented Descriptors 基于分形增广描述子的低分辨率卫星图像分类

Int. J. Image Graph. Pub Date : 2021-04-19 DOI: 10.1142/S0219467822500024

Rajalaxmi Padhy, S. Swain, S. Dash, Jibitesh Mishra

引用次数: 0

Enhancement of MRI Brain Images Using Notch Filter Based on Discrete Wavelet Transform 基于离散小波变换的陷波滤波增强MRI脑图像

Int. J. Image Graph. Pub Date : 2021-04-17 DOI: 10.1142/S0219467822500103

M. Ravikumar, B. Shivaprasad, D. Guru

引用次数: 3