i-manager’s Journal on Image Processing最新文献

BANK TRANSACTION USING IRIS RECOGNITION SYSTEM 银行交易使用虹膜识别系统

i-manager’s Journal on Image Processing Pub Date : 1900-01-01 DOI: 10.26634/jip.8.3.18124

Kumar R. Nithesh, D. Rahul, V. Dhanakoti, S. Saran

引用次数: 0

LABVIEW FOR MOTION DETECTION USING WEBCAM Labview的运动检测使用网络摄像头

i-manager’s Journal on Image Processing Pub Date : 1900-01-01 DOI: 10.26634/jip.8.3.18235

Thotakura Sushma, Harshithakanneganti Baby

引用次数: 0

Breast cancer disease prediction using ensemble techniques 使用集合技术预测乳腺癌疾病

i-manager’s Journal on Image Processing Pub Date : 1900-01-01 DOI: 10.26634/jip.10.1.19238

Rao T. Chalapathi, Naik Kshiramani

引用次数: 0

Implementation of haze removal algorithm to enhance low light images 实现雾霾去除算法，增强弱光图像

i-manager’s Journal on Image Processing Pub Date : 1900-01-01 DOI: 10.26634/jip.9.2.18796

K. Maheswari, Kadapa R. Charan

引用次数: 0

Comparative analysis of facial emotion recognition 面部情绪识别的对比分析

i-manager’s Journal on Image Processing Pub Date : 1900-01-01 DOI: 10.26634/jip.10.2.19397

Khandelwal Prerak, Pimple Aaryan, Punatar Devang, Patil Ashwini

{"title":"Comparative analysis of facial emotion recognition","authors":"Khandelwal Prerak, Pimple Aaryan, Punatar Devang, Patil Ashwini","doi":"10.26634/jip.10.2.19397","DOIUrl":"https://doi.org/10.26634/jip.10.2.19397","url":null,"abstract":"This paper provides an overview of the phases, methods, and datasets used in modern Facial Emotion Recognition (FER). FER has been a crucial topic in computer vision and Machine Learning (ML) for decades. By using Convolutional Neural Networks (CNN) to recognize facial expressions, valuable insights into people's emotional states can be gained, leading to improved services such as personalized healthcare, enhanced customer service, and more effective marketing. Automated FER can be used in various settings, including healthcare, education, criminal investigations, and Human Robot Interface (HRI). The study includes a comparative analysis of the performance and conclusions of several models such as Visual Geometry Group 16 (VGG16), Residual Network 50 (ResNet50), MobileNet, Deep CNN and the proposed pretrained VGG 16 architecture. These models can be integrated into different systems for various purposes such as obtaining feedback on products, services, or virtual learning platforms. Ultimately, Facial Emotion Recognition using Convolutional Neural Networks (CNN) can help reduce bias in decision-making processes by providing an unbiased assessment of a person's emotional state.","PeriodicalId":292215,"journal":{"name":"i-manager’s Journal on Image Processing","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123202666","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

OVERLAPPING SICKLE CELLS DETECTION AND SEPARATION USING MARKER-BASED WATERSHED SEGMENTATION 基于标记分水岭分割的重叠镰状细胞检测与分离

i-manager’s Journal on Image Processing Pub Date : 1900-01-01 DOI: 10.26634/jip.6.4.16752

Kenneth O. Mary, J. Agushaka, I. O. Oyefolahan

引用次数: 0

BRAIN TUMOUR DETECTION USING BUTTERWORTH HIGH PASS FILTER IN FREQUENCY DOMAIN AND MORPHOLOGICAL RECONSTRUCTION 基于巴特沃斯高通滤波器的脑肿瘤检测及形态学重构

i-manager’s Journal on Image Processing Pub Date : 1900-01-01 DOI: 10.26634/jip.6.4.16681

A. Karanam, Venkata Ramana Kompella

引用次数: 0

Wasserstein GAN-gradient penalty with deep transfer learning based alzheimer disease classification on 3D MRI scans 基于深度迁移学习的Wasserstein gan梯度惩罚在三维MRI扫描上的阿尔茨海默病分类

i-manager’s Journal on Image Processing Pub Date : 1900-01-01 DOI: 10.26634/jip.9.4.19282

Rao Thota Narasimha, D. Vasumathi

{"title":"Wasserstein GAN-gradient penalty with deep transfer learning based alzheimer disease classification on 3D MRI scans","authors":"Rao Thota Narasimha, D. Vasumathi","doi":"10.26634/jip.9.4.19282","DOIUrl":"https://doi.org/10.26634/jip.9.4.19282","url":null,"abstract":"There has been growing interest in using neuroimaging data, such as MRI scans, for the detection of Alzheimer's Disease (AD). Computer vision and deep learning models have shown promise in developing effective Computer-Aided Diagnosis (CAD) models for AD detection and classification. However, many existing models struggle due to their reliance on large training datasets and effective hyper parameter tuning strategies. To address these issues, transfer learning is often used to adjust the final fully connected layers of pre-trained DL models for use with smaller datasets. This paper proposes a new AD classification model based on a combination of Wasserstein GAN-Gradient Penalty (WGANGP) and Deep Transfer Learning (DTL) techniques, aimed at achieving accurate identification and classification of AD on 3D MRI scans. The WGANGP technique is used to increase the size of the dataset, and the model utilizes image enhancement and 3D Spatial Fuzzy C-means (3DS-FCM) techniques for image segmentation. Additionally, feature extraction is performed using the Ant Lion Optimizer (ALO) with the Inception v3 model, while the Deep Belief Network (DBN) model is employed for AD classification. The experimental validation of the WGANGP-DTL model is conducted using a benchmark 3D MRI dataset, and the results show that the proposed model outperforms recent approaches in several aspects.","PeriodicalId":292215,"journal":{"name":"i-manager’s Journal on Image Processing","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124182378","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

PRIMARY SCREENING TECHNIQUE FOR DETECTING BREAST CANCER 检测乳腺癌的初级筛查技术

i-manager’s Journal on Image Processing Pub Date : 1900-01-01 DOI: 10.26634/jip.6.2.15556

Raju C. Naga, Bindu A. Hima

引用次数: 0

Impact analysis of feature selection techniques on cyberstalking detection 特征选择技术对网络跟踪检测的影响分析

i-manager’s Journal on Image Processing Pub Date : 1900-01-01 DOI: 10.26634/jip.9.4.19138

Kumar Gautam Arvind, Bansal Abhishek

{"title":"Impact analysis of feature selection techniques on cyberstalking detection","authors":"Kumar Gautam Arvind, Bansal Abhishek","doi":"10.26634/jip.9.4.19138","DOIUrl":"https://doi.org/10.26634/jip.9.4.19138","url":null,"abstract":"Internet-based applications are making the habitual society and exploring new ways to perform online-based crimes. Numerous cybercriminals are engaged in the different platforms of the internet-based virtual world, carrying out cybercrimes according to predetermined and preplanned agendas. As technology advances, cyberstalking, cyberbullying, and other forms of cyber harassment are growing on social media, email, and other online platforms. Cyberstalking uses internet-based technology to harass, intimidate, and undermine individuals online with different approaches. In order to examine the impact of feature selection strategies for improving model performance, this paper proposes a machine learning-based cyberstalking detection model. The proposed model used the Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction method to extract features, and three distinct approaches, TF-IDF + Chi-Square Test, and TF-IDF + Information Gain, were used to select the different numbers of relevant features. In the cyberstalking detection model, a Support Vector Machine (SVM) was employed for classification purposes. Based on the SVM classifier's performance, each feature selection approach's impact on the various feature sets was assessed. According to experimental findings, the TF-IDF + Chi-Square Test outperformed other applied approaches and improved detection mode performance. Additionally, experimental findings demonstrate that the TFIDF + Chi-Square Test approach also performs better in a small collection of relevant features than other approaches that have been utilized.","PeriodicalId":292215,"journal":{"name":"i-manager’s Journal on Image Processing","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116913735","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