2018 14th Symposium on Neural Networks and Applications (NEUREL)最新文献_第2页

Semi-supervised neural network training method for fast-moving object detection 快速运动目标检测的半监督神经网络训练方法

2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8586986

Igor Sevo

引用次数: 1

Deep Features in Correlation Filters for Thermal Image Tracking 热图像跟踪中相关滤波器的深度特征

2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587030

Milan S. Stojanović, Nataša Vlahović, M. Stanković, Srđan Stanković

{"title":"Deep Features in Correlation Filters for Thermal Image Tracking","authors":"Milan S. Stojanović, Nataša Vlahović, M. Stanković, Srđan Stanković","doi":"10.1109/NEUREL.2018.8587030","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8587030","url":null,"abstract":"Object tracking using thermal infrared cameras has specific properties and challenges which distinguish it from the commonly used visual tracking. Recently, correlation filters (CF) based on deep features have been successfully applied in certain visual tracking scenarios. In this paper, we demonstrate that the success of these methods essentially depends on the way of how the deep features have been obtained. Indeed, the trackers based on CF and deep features use the pre-trained networks, originally trained for the object classification problem; hence, the obtained features are not invariant to changes of object appearance which may result from the change of camera type. We show that CF trackers based on deep features obtained from a convolutional architecture, pre-trained for visual object classification problem, have relatively poor performance when applied to the thermal tracking problem. Specifically, we test the performance of Kernelized Correlation Filter (KCF) on several chosen thermal video datasets, and demonstrate that the tracking results, when using simple feature representations (HOG features), are better than when using the pre-trained deep features. The results suggest that improved architectures and training methods for deep features should be developed in order to get more robust CF trackers.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131122827","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

Blur and Motion Blur Influence on Face Recognition Performance 模糊和运动模糊对人脸识别性能的影响

2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587028

Katarina Knežević, Emilija Mandić, Ranko Petrović, B. Stojanovic

引用次数: 8

Brain - Machine Interfaces in the Context of Artificial Intelligence Development 人工智能发展背景下的脑机接口

2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8586979

D. Lacrama, F. Alexa, F. A. Pintea, T. M. Karnyanszky

引用次数: 2

Reconstruction of Missing Samples in LFM Signals Using the Genetic Algorithm 用遗传算法重建LFM信号中的缺失样本

2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587029

M. Brajović, B. Lutovac, M. Daković, L. Stanković

引用次数: 1

Fusion of Visual and Thermal Imagery for Illumination Invariant Face Recognition System 光照不变人脸识别系统的视觉和热图像融合

2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8586985

Miloš Pavlović, B. Stojanovic, Ranko Petrović, Srđan Stanković

引用次数: 7

Humanoid Robot Detecting Animals via Neural Network 基于神经网络的类人机器人检测动物

2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587017

Y. Yordanov, V. Mladenov

引用次数: 0

Real-time Large Scale Traffic Sign Detection 实时大规模交通标志检测

2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587013

A. Avramović, Domen Tabernik, D. Skočaj

引用次数: 9

Water Resources Management for Ensuring Food and Water Security 确保粮食和水安全的水资源管理

2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8586999

Z. Dokou

{"title":"Water Resources Management for Ensuring Food and Water Security","authors":"Z. Dokou","doi":"10.1109/NEUREL.2018.8586999","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8586999","url":null,"abstract":"Today, water resources of most countries are under unprecedented stress. Given the fast growth of the global population, a 40% shortage between water demand and supply is projected by the World Bank by 2030. Moreover, climate change, climatic extremes and chronic water scarcity are threatening global water security. To achieve sustainability and strengthen water security, countries are investing in water resources management tools that will enable them to make optimal decisions under increasing uncertainty.The Blue Nile Basin (BNB), Ethiopia contributes over 60% of the Nile flow, and its water management decisions deeply influence all of East Africa. Despite the fact that the BNB has the physical resources to drive regional economic growth through irrigated agriculture and hydropower development, its exceptional climate variability and sensitivity to climate change have limited this development.This lecture will discuss water resources management of the BNB in general with a more specific focus on the development of an integrated surface water-groundwater model of the Lake Tana region, the source of the Blue Nile, which can be used as a tool for optimal water resources management. Model simulations using both a physically-based and a data-driven model are discussed and compared. The challenges are multiple – starting from the scarcity of the available in-situ data that inspired the establishment of a citizen science initiative involving high school students, to creating an accurate conceptual model representation and parameterizing the model to match reality as close as possible.The work presented here is part of an NSF (National Science Foundation) funded PIRE (Partnerships for International Research and Education) project. This project is a multi-year collaborative endeavor that aims to craft state-of-the-art tools to enable smallholder farmers in the BNB make practical decisions about water, crops and fertilizers and ultimately gain more secure access to food and water in the face of increasingly challenging climatic extremes (http://pire.engr.uconn.edu).","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116504809","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 Room Impulse Responses using Kohonen Neural Network 基于Kohonen神经网络的房间脉冲响应分类

2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587009

M. Pavlović, G. Zajic, Marija Zajeganović, D. Ristić, I. Reljin, M. Mijic

引用次数: 2