2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI)最新文献

Tutorials [3 abstracts] 教程[3个摘要]

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-08-25 DOI: 10.1109/ederc.2014.6924345

引用次数: 0

Visual Recognition of Local Kashmiri Objects with Limited Image Data using Transfer Learning 基于迁移学习的有限图像数据下克什米尔局部物体的视觉识别

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-08-25 DOI: 10.1109/ICETCI51973.2021.9574047

Asrar Nehvi, Rayees M. Dar, Assif Assad

{"title":"Visual Recognition of Local Kashmiri Objects with Limited Image Data using Transfer Learning","authors":"Asrar Nehvi, Rayees M. Dar, Assif Assad","doi":"10.1109/ICETCI51973.2021.9574047","DOIUrl":"https://doi.org/10.1109/ICETCI51973.2021.9574047","url":null,"abstract":"Learning to recognize object categories is a challenging task for computers and the task becomes more difficult if the image data is small in size. Traditional machine learning methods require extensive training data to generalize and produce accurate results. Seeking inspiration from human perception, it has been found using prior knowledge about related tasks helps in learning new tasks. Transfer Learning is based on this natural learning process and can help to reproduce the remarkable human capability of recognizing objects from just one single view. In this manuscript we explored transfer learning techniques along with state-of-the-art object recognition models to discover improved ways of performing Visual Object Recognition for Object categories with limited image data. A small data set was built from scratch having images for four local object categories and then a model was developed using pre trained Inception-v3 model for classifying them. The results were compared with stock 3 Layer Convolutional model. The proposed model obtained a respectable accuracy result of around 90% while the stock model had an accuracy of 70%. Considering the fact that the dataset used is very tiny (training portion of the data set had only 320 images for all categories, 80 per category) the results obtained are encouraging. Thus, this work further strengthens the fact that transfer-based techniques can be utilized for computer vision tasks with limited data.","PeriodicalId":281877,"journal":{"name":"2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI)","volume":"73 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125981768","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

Identification of Multiple Cracks on Beam using Fuzzy Logic 基于模糊逻辑的梁上多裂纹识别

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-08-25 DOI: 10.1109/ICETCI51973.2021.9574059

P. Govardhan, Prafulla Kalapatapu, Venkata Dilip Kumar Pasupuleti

引用次数: 1

A Learning Transition from Machine Learning to Deep Learning: A Survey 从机器学习到深度学习的学习过渡:调查

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-08-25 DOI: 10.1109/ICETCI51973.2021.9574066

Mercy Dol, A. Geetha

引用次数: 4

Machine Learning based Extraction of Electrical Substations from High Resolution Satellite Data: Outcome of the ICETCI 2021 Challenge 基于机器学习的从高分辨率卫星数据中提取变电站:ICETCI 2021挑战赛的结果

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-08-25 DOI: 10.1109/ICETCI51973.2021.9574043

Sreenivasan G, Anju Bajpai, Prakasa Rao D S, Girish Kumar T P, A. Shrivastava, Subrata N. Das, C. Jha, R. Hänsch, Venkata Kranti B., Nikhil Chandra D, Renuka R. Patil, Sita Devulapalli, J. Jacob I., Saurabh Singh, A. Chaube, Ved Dubey, N. Garg, Soumya Snigdha Kundu

{"title":"Machine Learning based Extraction of Electrical Substations from High Resolution Satellite Data: Outcome of the ICETCI 2021 Challenge","authors":"Sreenivasan G, Anju Bajpai, Prakasa Rao D S, Girish Kumar T P, A. Shrivastava, Subrata N. Das, C. Jha, R. Hänsch, Venkata Kranti B., Nikhil Chandra D, Renuka R. Patil, Sita Devulapalli, J. Jacob I., Saurabh Singh, A. Chaube, Ved Dubey, N. Garg, Soumya Snigdha Kundu","doi":"10.1109/ICETCI51973.2021.9574043","DOIUrl":"https://doi.org/10.1109/ICETCI51973.2021.9574043","url":null,"abstract":"The creation of geospatial databases of large power infrastructure such as substations is essential for the planning and management of electricity transmission and distribution. Achieving this task through conventional mapping techniques involves great effort in terms of time, manpower and financial resources. Automatically extracting power infrastructure from high-resolution satellite data using Machine Learning Algorithms is a promising option. However, feature extraction of power infrastructure such as electrical substations is a new challenge since not many attempts have been made in this domain. To nurture the development of deep learning algorithms which can provide solutions for automatic feature extraction of substations, we have undertaken a Challenge at the International Conference on Emerging Techniques in Computational Intelligence (ICETCI-2021). The goal was to explore the feasibility of extracting electrical substations from high resolution satellite data using deep learning algorithms. We evaluated the algorithms for their effectiveness in extracting electrical substations in terms of the accuracy of feature extraction and efficiency of the models with respect to computational resource utilization. We describe the competition design, process and evaluation, and present an overview of the different Machine Learning solutions, and the top three best solutions that provided the best accuracy and efficiency for substation extraction.","PeriodicalId":281877,"journal":{"name":"2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI)","volume":"359 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122768855","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

Automatic Recognition of License Plates 自动识别车牌

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-08-25 DOI: 10.1109/ICETCI51973.2021.9574072

Tanushri Bhagat, Rahul Thakur

引用次数: 5

Temporal Difference Rewards for End-to-end Vision-based Active Robot Tracking using Deep Reinforcement Learning 基于深度强化学习的端到端视觉主动机器人跟踪的时间差奖励

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-08-25 DOI: 10.1109/ICETCI51973.2021.9574071

Pavlos Tiritiris, N. Passalis, A. Tefas

{"title":"Temporal Difference Rewards for End-to-end Vision-based Active Robot Tracking using Deep Reinforcement Learning","authors":"Pavlos Tiritiris, N. Passalis, A. Tefas","doi":"10.1109/ICETCI51973.2021.9574071","DOIUrl":"https://doi.org/10.1109/ICETCI51973.2021.9574071","url":null,"abstract":"Object tracking allows for localizing moving objects in sequences of frames providing detailed information regarding the trajectory of objects that appear in a scene. In this paper, we study active object tracking, where a tracker receives an input visual observation and directly outputs the most appropriate control actions in order to follow and keep the target in its field of view, unifying in this way the task of visual tracking and control. This is in contrast with conventional tracking approaches, as typically developed by the computer vision community, where the problem of detecting the tracked object in a frame is decoupled from the problem of controlling the camera and/or the robot to follow the object. Deep Reinforcement Learning (DLR) methods hold the credentials for overcoming these issues, since they allow for tackling both problems, i.e., detecting the tracked object and providing control commands, at the same time. However, DRL algorithms require a significantly different methodology for training compared to traditional computer vision models, e.g., they rely on dynamic simulations for training instead of static datasets, while they are often notoriously difficult to converge, often requiring reward shaping approaches for increasing convergence speed and stability. The main contribution of this paper is a DRL, vision-based active tracking method, along with an appropriately designed reward shaping approach for active tracking problems. The developed methods are evaluated using a state-of-the-art robotics simulator, demonstrating good generalization on various dynamic trajectories of moving objects under a wide range of different setups.","PeriodicalId":281877,"journal":{"name":"2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122339517","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

[Copyright notice] (版权)

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-08-25 DOI: 10.1109/icetci51973.2021.9574069

引用次数: 0

Genre Classification using Spectrograms as input to CNN on Indian Music 使用谱图作为输入到CNN的印度音乐的类型分类

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-08-25 DOI: 10.1109/ICETCI51973.2021.9574060

Prafulla Kalapatapu, J. Sravani, Sanchit Gupta, Aditi Sharma, Aruna Malapati

引用次数: 0

Phasor Quaternion Neural Network Framework and Its Significance in Big SAR Intelligence 相数四元数神经网络框架及其在大SAR情报中的意义

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-08-25 DOI: 10.1109/ICETCI51973.2021.9574064

A. Hirose

引用次数: 1