2021 Thirteenth International Conference on Contemporary Computing (IC3-2021)最新文献_第7页

Image Segmentation based Background Removal and Replacement 基于背景去除和替换的图像分割

2021 Thirteenth International Conference on Contemporary Computing (IC3-2021) Pub Date : 2021-08-05 DOI: 10.1145/3474124.3474135

Meenal Gupta, Ritik Goyal, Siddharth Shekhar, R. Krishnamurthi

{"title":"Image Segmentation based Background Removal and Replacement","authors":"Meenal Gupta, Ritik Goyal, Siddharth Shekhar, R. Krishnamurthi","doi":"10.1145/3474124.3474135","DOIUrl":"https://doi.org/10.1145/3474124.3474135","url":null,"abstract":"Privacy is a great cause of concern. As many of us are concerned and self-conscious about what is visible in our background while having an online meeting. To provide some privacy during an online meeting our main objective is to blur/remove/replace the background of a live webcam video stream using a machine learning model trained to segment out persons in an image, whose results are then refined using image processing techniques. Currently, it is available in online meeting apps like Zoom, Google Meet, Microsoft Teams, etc., but there is no open-source code available for it so we also want to make an online repository, from where anyone can take our code, and implement it in their project. Our program uses a webcam on a computer and proceeds in the following way. First, we capture a live frame from the webcam, and then pre-process it, so that it is suitable for supplying as input to the machine-learning model. This provides us with a rough predicted foreground mask. Then, to refine it, we perform morphological closing, which is used to remove small imperfections in the mask. We then obtain the outline of the detected person using contour finding and fill its interior region with white by using a flood-fill operation. After all, of this, we have generated a foreground mask layer in an alpha channel whose white area shows the foreground, which we have to leave as it, is and the black area shows background, which we have to change. Therefore, we combine this generated mask along with its corresponding frame and, optionally, a background image, to generate its corresponding frame. Repeatedly doing this for every frame we receive from the webcam continuously, we have a live video with edited background with a decent frame rate of 26-28 FPS.","PeriodicalId":144611,"journal":{"name":"2021 Thirteenth International Conference on Contemporary Computing (IC3-2021)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127803748","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

Speech Emotion Recognition Using MFCC and Wide Residual Network 基于MFCC和宽残差网络的语音情感识别

2021 Thirteenth International Conference on Contemporary Computing (IC3-2021) Pub Date : 2021-08-05 DOI: 10.1145/3474124.3474171

M. Gupta, S. Chandra

引用次数: 1

Early Diagnosis of Alzheimer's Disease using Machine Learning Based Methods 基于机器学习方法的阿尔茨海默病早期诊断

2021 Thirteenth International Conference on Contemporary Computing (IC3-2021) Pub Date : 2021-08-05 DOI: 10.1145/3474124.3474134

Muskan Kapoor, Mehak Kapoor, Rohit Shukla, T. Singh

{"title":"Early Diagnosis of Alzheimer's Disease using Machine Learning Based Methods","authors":"Muskan Kapoor, Mehak Kapoor, Rohit Shukla, T. Singh","doi":"10.1145/3474124.3474134","DOIUrl":"https://doi.org/10.1145/3474124.3474134","url":null,"abstract":"Alzheimer's Disease is a gradual, irreversible brain disease that deteriorates a patient's memory, cognitive functions and shrinks the brain's size, eventually leading to death. Based on recent research, it is found that AD is the third leading cause of death. Presently there is no available medication for the treatment of AD. Though, diagnosis of AD at early onset may delay the progression of the disease and thus aid in improving the subject's well-being. Early detection and classification of divergent phases of AD using EHR (Electronic Health Record) and ML (Machine Learning) algorithms can prove to be a productive approach as AD evolves with time and thus patients at distinct stages need to be treated differently. Hence, classification of different stages is crucial for the realization of purpose that it can improve patient's quality of life as treatment of symptoms can be performed accordingly. The use of contemporary computing technology and resources is becoming a boon to new trends in healthcare and diagnosis. EHR is setting a gauge to record patient's data electronically through the replacement of conventional methods that comprise the collection of data in paper-based form. ML with AI techniques can be applied to EHR to provide an accurate and comprehensive diagnosis to improve the quality and productivity of healthcare. In this article, four diverse machine learning algorithms are applied on ADNI-Longitudinal data for the classification of five different stages of AD and thus identifying the most relevant biomarkers and features that can lead to reliable and effective detection and diagnosis of AD. Wherein, RF (Random Forest) exhibits the highest accuracy of 99.8 % followed by ANN (Artificial Neural Network). In this study, we utilized the TADPOLE (The Alzheimer's Disease Prediction of Longitudinal Evolution) grand challenge data generated from ADNI (Alzheimer's Disease Neuroimaging Initiative). The proposed study provides a promising solution for the management of AD.","PeriodicalId":144611,"journal":{"name":"2021 Thirteenth International Conference on Contemporary Computing (IC3-2021)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126364910","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: 6

Active Human Pose Estimation for Assisted Living 辅助生活的主动人体姿态估计

2021 Thirteenth International Conference on Contemporary Computing (IC3-2021) Pub Date : 2021-08-05 DOI: 10.1145/3474124.3474139

Ankur Raj, Divyanshi Singh, C. Prakash

{"title":"Active Human Pose Estimation for Assisted Living","authors":"Ankur Raj, Divyanshi Singh, C. Prakash","doi":"10.1145/3474124.3474139","DOIUrl":"https://doi.org/10.1145/3474124.3474139","url":null,"abstract":"Active and Assisted Living has found itself in one of the application areas of technological advancement the world is witnessing. The objective is to provide the elderly people with facilitated living environment so as to assist them in carrying out daily activities without them being prone to injury or any other undesirable event. These residential facilities prove to be even more beneficial when equipped with technology to prevent any fatalities or aid immediately in case fatality occurs. One such harmful event is falling. Falling especially in the case of elderly can have serious impacts on their health. Hence, several attempts have been made to provide aid immediately whenever such event occurs. This includes usage of different techniques like Wearable sensors, Computer vision or Ambient sensors. This paper aims at exploring Computer vision technique to determine fall. For this, key points of human body are located which are then used to identify if the fall has occurred or not. The proposed algorithm uses publicly available dataset to train on detecting fall. Several classifiers like SVM, AdaBoost, Logistic Regression has been used for classification with SVM reporting 82.07% accuracy, AdaBoost with 99.64% accuracy and Logistic Regression with 98.92% accuracy.","PeriodicalId":144611,"journal":{"name":"2021 Thirteenth International Conference on Contemporary Computing (IC3-2021)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115139117","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