INFRARED.bib

@INPROCEEDINGS{7301290, 
author={E. Gundogdu and H. Ozkan and H. S. Demir and H. Ergezer and E. Akagündüz and S. K. Pakin}, 
booktitle={2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)}, 
title={Comparison of infrared and visible imagery for object tracking: Toward trackers with superior IR performance}, 
year={2015}, 
volume={}, 
number={}, 
pages={1-9}, 
keywords={infrared imaging;object tracking;video signal processing;visible spectra;IR-visible band video conjugate;infrared imagery;infrared video;tracker superior IR performance;visible imagery;visual object tracking;Correlators;Heuristic algorithms;Imaging;Object tracking;Switches;Target tracking;Videos}, 
doi={10.1109/CVPRW.2015.7301290}, 
ISSN={2160-7508}, 
month={June},}

@INPROCEEDINGS{7532521, 
author={E. Gundogdu and A. Koç and A. A. Alatan}, 
booktitle={2016 IEEE International Conference on Image Processing (ICIP)}, 
title={Object classification in infrared images using deep representations}, 
year={2016}, 
volume={}, 
number={}, 
pages={1066-1070}, 
keywords={binary decision diagrams;decision trees;feature extraction;image classification;image representation;infrared imaging;learning (artificial intelligence);video signal processing;IR object classification;IR sensors;IR target collection;binary decision tree structure;deep neural network learning;deep representations;feature extraction;individual deep CNN training;infrared images;infrared object classification;object appearance space hierarchical division;video records;Decision trees;Feature extraction;Helicopters;Neural networks;Target tracking;Training;Visualization;Infrared;classification;thermal dataset generation;thermal targets;tree-based classification}, 
doi={10.1109/ICIP.2016.7532521}, 
ISSN={}, 
month={Sept},}

@INPROCEEDINGS{7789533, 
author={E. Gundogdu and A. Koc and B. Solmaz and R. I. Hammoud and A. A. Alatan}, 
booktitle={2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)}, 
title={Evaluation of Feature Channels for Correlation-Filter-Based Visual Object Tracking in Infrared Spectrum}, 
year={2016}, 
volume={}, 
number={}, 
pages={290-298}, 
keywords={correlation methods;feature extraction;feedforward neural nets;image classification;image colour analysis;infrared imaging;object tracking;CNN features;LTIR dataset;Linköping thermal infrared dataset;color channels;correlation-filter based methods;correlation-filter-based tracker;correlation-filter-based visual object tracking;deep convolutional neural networks features;feature channel evaluation;feature maps;gradient orientations;infrared spectra;infrared spectrum;longwave infrared videos;medium wave infrared videos;overlap metric;raw image intensities;supervised classification task;visible imagery;visible spectra;Correlation;Decision support systems;Feature extraction;Object tracking;Robustness;Target tracking;Visualization}, 
doi={10.1109/CVPRW.2016.43}, 
ISSN={}, 
month={June},}