2020 IEEE Bombay Section Signature Conference (IBSSC)最新文献

Contrast Enhancement of Dark Images using Weighted Blending of Bright Channel Prior and Robust Retinex Method 基于加权混合明亮通道先验和鲁棒视网膜方法的暗图像对比度增强

2020 IEEE Bombay Section Signature Conference (IBSSC) Pub Date : 2020-12-04 DOI: 10.1109/IBSSC51096.2020.9332165

Sudeep D. Thepade, Mrunal E. Idhate

{"title":"Contrast Enhancement of Dark Images using Weighted Blending of Bright Channel Prior and Robust Retinex Method","authors":"Sudeep D. Thepade, Mrunal E. Idhate","doi":"10.1109/IBSSC51096.2020.9332165","DOIUrl":"https://doi.org/10.1109/IBSSC51096.2020.9332165","url":null,"abstract":"The image took in the dark light has low contrast, which affects the clarity of details in it. This results in the loss of information and details in poorly illuminated images. Such images are not suitable for computer vision analysis and observations. In many places, images taken in the dark light like CCTV images at night, military, satellite images, medical images, etc. Several methods proposed for contrast enhancement of low light (darker) images like histogram equalization, bright channel prior, camera response model, and robust retinex model. The contrast enhancement gone using existing methods have some limitations like getting blurring effect, getting over the brightening of details. To overcome these disadvantages, the paper proposes the contrast enhancement of darker images with the weighted blending of bright channel prior (BCR) and robust retinex model (RRM) with different assigned weights. For the performance evaluation of the variations of the proposed method, the image entropy value is computed. From the experimentation done on images from the ExDark dataset, it observed that the proposed weighted blending based contrast enhancement method gives better performance over existing BCR and RRM.","PeriodicalId":432093,"journal":{"name":"2020 IEEE Bombay Section Signature Conference (IBSSC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115185133","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

Unsupervised machine learning in industrial applications: a case study in iron mining 工业应用中的无监督机器学习:在铁矿开采中的案例研究

2020 IEEE Bombay Section Signature Conference (IBSSC) Pub Date : 2020-12-04 DOI: 10.1109/IBSSC51096.2020.9332174

L. S. B. Pereira, R. Rodrigues, E. A. C. Neto

引用次数: 0

SlipSwap: Reduce the slippage that is incurred during the swap of tokens using Algorithmic analysis 滑动交换:使用算法分析减少在交换令牌期间产生的滑动

2020 IEEE Bombay Section Signature Conference (IBSSC) Pub Date : 2020-12-04 DOI: 10.1109/IBSSC51096.2020.9332185

Jaineel Shah, Prafful Javare, Divya Khetan

引用次数: 0

FCOS Based Human Detection System Using Thermal Imaging for UAV Based Surveillance Applications 基于FCOS的热成像人体检测系统在无人机监视中的应用

2020 IEEE Bombay Section Signature Conference (IBSSC) Pub Date : 2020-12-04 DOI: 10.1109/IBSSC51096.2020.9332157

Prashanth Kannadaguli

{"title":"FCOS Based Human Detection System Using Thermal Imaging for UAV Based Surveillance Applications","authors":"Prashanth Kannadaguli","doi":"10.1109/IBSSC51096.2020.9332157","DOIUrl":"https://doi.org/10.1109/IBSSC51096.2020.9332157","url":null,"abstract":"This work is related to building a Human Detection system based on Fully Connected One Shot (FCOS). It is one of the most recent Deep Learning approaches primitively built using single shot detection proposal. Unlike the double stage region-based object detection schemes this technique do not follow semantic segmentation, it does not undergo loss of the object information such as disappearance of the gradients and it does not require pre-defined anchors. This technique comprises strong feature extractors and reinforce multi scale object detection and it is very quick in the multithreaded GPU environments. Since our fundamental research is concentrated on object classification related to Unmanned Aerial Vehicle (UAV) applications, as a first step we choose to detect the humans from thermal dataset. Therefore, we used thermal images and videos possessed from thermal cameras of UAV lm to 50m above ground level as our dataset in building the model and testing. The FCOS extracts the features of an object using its efficient per-pixel fashion. Finally, the performance analysis of these model in terms of mean Average Precision (mAP) indicates that the modelling using FCOS performs in a promising way and it can be used in automatic human detection systems.","PeriodicalId":432093,"journal":{"name":"2020 IEEE Bombay Section Signature Conference (IBSSC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131135396","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

RFID Reader with Miniaturized Horn Patch for Microwave Frequency Applications at 5.8 GHz 用于5.8 GHz微波频率应用的微型化喇叭贴片RFID阅读器

2020 IEEE Bombay Section Signature Conference (IBSSC) Pub Date : 2020-12-04 DOI: 10.1109/IBSSC51096.2020.9332164

Jai Mangal

引用次数: 2

Covid19 Identification using Machine Learning Classifiers with Histogram of Luminance Chroma Features of Chest X-ray images 基于胸部x线图像亮度色度直方图的机器学习分类器的covid - 19识别

2020 IEEE Bombay Section Signature Conference (IBSSC) Pub Date : 2020-12-04 DOI: 10.1109/IBSSC51096.2020.9332160

Sudeep D. Thepade, P. Chaudhari, M. Dindorkar, S. Bang

{"title":"Covid19 Identification using Machine Learning Classifiers with Histogram of Luminance Chroma Features of Chest X-ray images","authors":"Sudeep D. Thepade, P. Chaudhari, M. Dindorkar, S. Bang","doi":"10.1109/IBSSC51096.2020.9332160","DOIUrl":"https://doi.org/10.1109/IBSSC51096.2020.9332160","url":null,"abstract":"The outbreak of the novel Coronavirus has caused catastrophic consequences on the entire global economy leading to a huge loss of health and wealth. Mankind has suffered a lot due to this pandemic. Large number of screening tests are performed on the suspected individuals by using Covid-19 test kits. As the rate of spread of this disease is increasing exponentially, medical organizations are finding it difficult to screen the suspected cases due to limited availability of test kits. Early diagnosis of coronavirus infection can be made from chest X-ray images of an individual. Current paper proposes a color space based global texture feature extraction method to identify covid19 infected cases. Luminance Chroma features of chest X-ray images are extracted from YCrCb, Kekre-LUV, and CIE-LUV color spaces. These extracted features are used for training different machine learning classifiers and ensembles to perform 3-class classification as covid19, pneumonia, and normal. Results computed at 10-fold cross-validation show that ensembles perform better than the individual machine learning (ML) classifiers. Performance of the proposed method is calibrated on an open-source dataset: Covid19 by considering Accuracy, Positive predicted value (PPV), Sensitivity (Recall), F Measure, and Matthew’s correlation coefficient (MCC) performance measures.","PeriodicalId":432093,"journal":{"name":"2020 IEEE Bombay Section Signature Conference (IBSSC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125651035","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

Design and Development of Hand Gesture based Communication Device for Deaf and Mute People 基于手势的聋哑人交流设备的设计与开发

2020 IEEE Bombay Section Signature Conference (IBSSC) Pub Date : 2020-12-04 DOI: 10.1109/IBSSC51096.2020.9332208

O. Vaidya, S. Gandhe, Abhishek Sharma, Asit Bhate, Vishal Bhosale, Rushabh Mahale

引用次数: 1

Visibility Enhancement in Low Light Images with Weighted Fusion of Robust Retinex Model and Dark Channel Prior 基于鲁棒视网膜模型和暗通道先验加权融合的弱光图像可见性增强

2020 IEEE Bombay Section Signature Conference (IBSSC) Pub Date : 2020-12-04 DOI: 10.1109/IBSSC51096.2020.9332217

Sudeep D. Thepade, Akshay Shirbhate

引用次数: 2

Covid19 Identification from Chest X-Ray Images using Local Binary Patterns with assorted Machine Learning Classifiers 使用局部二值模式和各种机器学习分类器从胸部x射线图像中识别covid - 19

2020 IEEE Bombay Section Signature Conference (IBSSC) Pub Date : 2020-12-04 DOI: 10.1109/IBSSC51096.2020.9332158

Sudeep D. Thepade, Ketan Jadhav

{"title":"Covid19 Identification from Chest X-Ray Images using Local Binary Patterns with assorted Machine Learning Classifiers","authors":"Sudeep D. Thepade, Ketan Jadhav","doi":"10.1109/IBSSC51096.2020.9332158","DOIUrl":"https://doi.org/10.1109/IBSSC51096.2020.9332158","url":null,"abstract":"The novel corona virus caused by the SARS-CoV2 virus originated in Wuhan, China and spread globally. The massive outbreak of the virus resulted in millions of people being infected. Early detection of the virus is crucial in the complete recovery of the patient but can be fatal if detected in the later stages. The symptoms of the virus being similar to flu make it difficult to detect. This paper attempts an automated system for identification of the Covid19 virus infected images of chest X-Ray. The proposed method uses a dataset which has human chest X-Rays of non infected people as well as patients suffering from pneumonia and Covid19 virus infection. Local binary patterns with variations in its input parameters are used for feature extraction. The resulting feature sets are classified using several machine learning algorithms and ensembles of these individual models. Results of experimentation are obtained across 10 fold cross validation testing. Evaluation metrics accuracy, positive predictive value (PPV), sensitivity and f-measure are used to compare performance. Observations of the results show that the ensemble of RTree-RForest-KNN gives the best classification performance while ensemble models perform better than most individual classifiers. Comparing the input parameters of the LBP, the best performance is given by parameters R=6 (P=48) and R=7 (P=56) gives the best performance for the average of metrics for 10 fold cross validation in the proposed Covid19 identification method from chest X-Ray images.","PeriodicalId":432093,"journal":{"name":"2020 IEEE Bombay Section Signature Conference (IBSSC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121694431","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

Air Pollution Prediction using Machine Learning 利用机器学习进行空气污染预测

2020 IEEE Bombay Section Signature Conference (IBSSC) Pub Date : 2020-12-04 DOI: 10.1109/IBSSC51096.2020.9332184

Shreyas Simu, V. Turkar, Rohit Martires, Vranda Asolkar, Swizel Monteiro, Vaylon Fernandes, Vassant Salgaoncary

引用次数: 7