ICTACT Journal on Image and Video Processing最新文献

DIMENSIONALITY REDUCTION BASED CLASSIFICATION USING GENERATIVE ADVERSARIAL NETWORKS DATASET GENERATION 基于降维的生成对抗性网络分类数据集生成

ICTACT Journal on Image and Video Processing Pub Date : 2022-08-01 DOI: 10.21917/ijivp.2022.0396

Narendra Gopal, Sivakumar D

{"title":"DIMENSIONALITY REDUCTION BASED CLASSIFICATION USING GENERATIVE ADVERSARIAL NETWORKS DATASET GENERATION","authors":"Narendra Gopal, Sivakumar D","doi":"10.21917/ijivp.2022.0396","DOIUrl":"https://doi.org/10.21917/ijivp.2022.0396","url":null,"abstract":"The term data augmentation refers to an approach that can be used to prevent overfitting in the training dataset, which is where the issue first manifests itself. This is based on the assumption that extra datasets can be improved by include new information that is of use. It is feasible to create an artificially larger training dataset by utilizing methods such as data warping and oversampling. This will allow for the creation of more accurate models. This idea is demonstrated through the application of a variety of different methods, some of which include neural style transfer, adversarial training, and erasure by random erasure, amongst others. By utilizing oversampling augmentations, it is feasible to create synthetic instances that can be incorporated into the training data. This is made possible by the generation of synthetic instances. There are numerous illustrations of this, including image merging, feature space enhancements, and generative adversarial networks, to name a few (GANs). In this paper, we aim to provide evidence that a Generative Adversarial Network can be used to convert regular images into Hyper Spectral Images (HSI). The purpose of the model is to generate data by including a certain amount of unpredictable noise.","PeriodicalId":30615,"journal":{"name":"ICTACT Journal on Image and Video Processing","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49020193","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 OF BRAIN TUMOR USING BEES SWARM OPTIMISATION 基于蜂群优化的脑肿瘤分类

ICTACT Journal on Image and Video Processing Pub Date : 2019-08-01 DOI: 10.21917/ijivp.2019.0287

M. Ramkumar, M. Babu, R. Lakshminarayanan

引用次数: 0

ADVANCED COLOR COVERT IMAGE SHARING USING ARNOLD CAT MAP AND VISUAL CRYPTOGRAPHY 先进的彩色隐蔽图像共享使用阿诺德猫地图和视觉密码学

ICTACT Journal on Image and Video Processing Pub Date : 2019-08-01 DOI: 10.21917/ijivp.2019.0288

B. Sapna, K. Sudha

{"title":"ADVANCED COLOR COVERT IMAGE SHARING USING ARNOLD CAT MAP AND VISUAL CRYPTOGRAPHY","authors":"B. Sapna, K. Sudha","doi":"10.21917/ijivp.2019.0288","DOIUrl":"https://doi.org/10.21917/ijivp.2019.0288","url":null,"abstract":"The demand for effective information security schemes is increasing day by day with the continual growth of the internet. Visual cryptography (VC) is a very important secret sharing scheme. The essential step behind this secret sharing scheme is to convert the color covert image into multiple indecipherable image shares so it cannot reveal the data within the color covert image unless combined along by some mathematical calculation. This paper proposes an advanced color covert image-sharing scheme using Arnold cat map (ACM) and VC. The random matrix-encoding scheme encodes the color covert image into an image matrix. ACM algorithm disrupts the high correlation among the pixels of the image matrix to generate an encrypted image. The generation of shares from this encrypted image is by VC that uses pixel reversal and random matrix generator. The shares one by one does not provide any information concerning the color covert image however put together they offer back the encrypted image. The projected paradigm offers 3 levels of security and through decipherment gives back the covert image without loss of information. Related examples and experimental results reveal the effectiveness of this scheme.","PeriodicalId":30615,"journal":{"name":"ICTACT Journal on Image and Video Processing","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42023624","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

A SPATIAL DOMAIN BASED SECURE AND ROBUST VIDEO WATERMARKING TECHNIQUE USING MODIFIED LSB AND SECRET IMAGE SHARING 基于改进LSB和秘密图像共享的空间域安全鲁棒视频水印技术

ICTACT Journal on Image and Video Processing Pub Date : 2019-08-01 DOI: 10.21917/ijivp.2019.0293

V. Sharma, M. Gangarde, Shruti Oza

引用次数: 1

SMART GESTURE USING REAL TIME OBJECT TRACKING 使用实时对象跟踪的智能手势

ICTACT Journal on Image and Video Processing Pub Date : 2019-08-01 DOI: 10.21917/ijivp.2019.0290

S. Bhat, N. Lavanya, M. Anusuya

引用次数: 0

SECURE TRANSMISSION OF DATA USING IMAGE STEGANOGRAPHY 使用图像隐写术安全传输数据

ICTACT Journal on Image and Video Processing Pub Date : 2019-08-01 DOI: 10.21917/ijivp.2019.0291

Sourabh Chandra, Smita Paira

引用次数: 0

A NOVEL SEGMENTATION AND CLASSIFICATION TECHNIQUES FOR MRI BRAIN TUMOR IMAGES 一种新的mri脑肿瘤图像分割与分类技术

ICTACT Journal on Image and Video Processing Pub Date : 2019-08-01 DOI: 10.21917/ijivp.2019.0289

M. Abinaya, S. Padma

引用次数: 2

STREETLIGHT OBJECTS RECOGNITION BY REGION AND HISTOGRAM FEATURES IN AN AUTONOMOUS VEHICLE SYSTEM 自动驾驶汽车系统中基于区域和直方图特征的路灯目标识别

ICTACT Journal on Image and Video Processing Pub Date : 2019-08-01 DOI: 10.21917/ijivp.2019.0292

Martins E. Irhebhude, M. Shabi, A. Kolawole

引用次数: 2

DETECTION OF SEPTORIA SPOT ON BLUEBERRY LEAF IMAGES USING SVM CLASSIFIER 用SVM分类器检测蓝莓叶片图像中的红斑

ICTACT Journal on Image and Video Processing Pub Date : 2019-05-01 DOI: 10.21917/ijivp.2019.0286

M. Latha, S. Jaya

{"title":"DETECTION OF SEPTORIA SPOT ON BLUEBERRY LEAF IMAGES USING SVM CLASSIFIER","authors":"M. Latha, S. Jaya","doi":"10.21917/ijivp.2019.0286","DOIUrl":"https://doi.org/10.21917/ijivp.2019.0286","url":null,"abstract":"Identification and classification of the plant leaf is efficient way to preventing loss occurred in agricultural field. The Septoria leaf spot is mainly affect the leaves which caused by a fungus, flu, bacteria. The Production of blueberry fruit is decreasing due to the disease affected on its stem and leaf. Small brown spots are frequently visible on blueberry leaves at specific period in the year. The spots, generally surrounded by bright yellow halos, start on the lower leaves and slowly appear on upper leaves over time. Image processing technology has been proved to be an efficient analysis to identify and detect the disease on a leaf. This proposed paper intends to focus to detect and classify a Septoria leaf spot on blueberry using Image Processing techniques such as, k-means clustering (k-nearest neighbor) for Segmentation, Gray-Level Co-occurrence Matrix for feature extraction and Support Vector Machine classifier to detect the leaf stage whether it is affected by Septoria spot or not. Totally 13 features have been extracted from each Blueberry leaf images where dataset of 40 images were taken for training and testing process partially and obtained the accuracy level was 96.77% using F-measure.","PeriodicalId":30615,"journal":{"name":"ICTACT Journal on Image and Video Processing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49076665","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

QUALITATIVE ANALYSIS OF VARIOUS EDGE DETECTION TECHNIQUES APPLIED ON CERVICAL HERNIATED SPINE IMAGES 各种边缘检测技术应用于颈椎突出症图像的定性分析

ICTACT Journal on Image and Video Processing Pub Date : 2019-05-01 DOI: 10.21917/ijivp.2019.0282

C. Malarvizhi, P. Balamurugan

引用次数: 0