Welcome

Who I am

My name is Qiaosong Wang and I'm currently a Ph.D. student at the Department of Computer and Information Sciences, University of Delaware. I received my BEng in Automation from Xi'an Jiaotong University, China in May, 2011. CV

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Research Interests

My research interests include computer vision and mobile robot perception.

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Projects

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Publications

• Stereo Vision based Depth of Field Rendering on a Mobile Device
IS&T/SPIE Electronic Imaging (EI) 2014
pdf bibtex
							@inproceedings{wang9023stereo,
							  title={Stereo Vision based Depth of Field Rendering on a Mobile Device},
							  author={Wang, Qiaosong and Yu, Zhan and Rasmussen, Christopher and Yu, Jingyi},
							  booktitle={Proc. of SPIE Vol},
							  volume={9023},
							  pages={902307--1}
							}
							
best paper award

• Stereo Vision based Depth of Field Rendering on a Mobile Device
SPIE Journal of Electronic Imaging (JEI) 2014
pdf bibtex
							@article{wang2014stereo,
							  title={Stereo vision--based depth of field rendering on a mobile device},
							  author={Wang, Qiaosong and Yu, Zhan and Rasmussen, Christopher and Yu, Jingyi},
							  journal={Journal of Electronic Imaging},
							  volume={23},
							  number={2},
							  pages={023009--023009},
							  year={2014},
							  publisher={International Society for Optics and Photonics}
							}							
						  

• Perception and Control Strategies for Driving Utility Vehicles with a Humanoid Robot
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2014
pdf bibtex
							@inproceedings{rasmussen2014perception,
							  title={Perception and control strategies for driving utility vehicles with a humanoid robot},
							  author={Rasmussen, Christopher and Sohn, Kiwon and Wang, Qiaosong and Oh, Paul},
							  booktitle={Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International Conference on},
							  pages={973--980},
							  year={2014},
							  organization={IEEE}
							}					
						

• Automatic Layer Separation using Light Field Imaging
arxiv 2015
pdf bibtex
							@article{wang2015automatic,
							  title={Automatic Layer Separation using Light Field Imaging},
							  author={Wang, Qiaosong and Lin, Haiting and Ma, Yi and Kang, Sing Bing and Yu, Jingyi},
							  journal={arXiv preprint arXiv:1506.04721},
							  year={2015}
							}			
						

• Im2Fit: Fast 3D Model Fitting and Anthropometrics using Single Consumer
Depth Camera and Synthetic Data

IS&T/SPIE Electronic Imaging (EI) 2016
pdf bibtex
							@article{wang2016im2fit,
							  title={Im2fit: Fast 3d model fitting and anthropometrics using single consumer depth camera and synthetic data},
							  author={Wang, Qiaosong and Jagadeesh, Vignesh and Ressler, Bryan and Piramuthu, Robinson},
							  journal={Electronic Imaging},
							  volume={2016},
							  number={21},
							  pages={1--7},
							  year={2016},
							  publisher={Society for Imaging Science and Technology}
							}	
						   	
						

• GraB: Visual Saliency via Novel Graph Model and Background Priors
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016
pdf bibtex
							@inproceedings{wang2016grab,
							  title={GraB: Visual Saliency via Novel Graph Model and Background Priors},
							  author={Wang, Qiaosong and Zheng, Wen and Piramuthu, Robinson},
							  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
							  pages={535--543},
							  year={2016}
							}	
						
saliency maps

• Advanced Multi-modal Integrated 3-D Reconstruction System
Esri Imaging & Mapping Forum 2016
pdf bibtex

• Fast, Deep Detection and Tracking of Birds and Nests
International Symposium on Visual Computing (ISVC) 2016
pdf bibtex
							@inproceedings{wang2016fast,
							  title={Fast, Deep Detection and Tracking of Birds and Nests},
							  author={Wang, Qiaosong and Rasmussen, Christopher and Song, Chunbo},
							  booktitle={International Symposium on Visual Computing},
							  pages={146--155},
							  year={2016},
							  organization={Springer}
							} 	
						

• Object Recognition in Aerial Images Using Convolutional Neural Networks
Journal of Imaging 2017
pdf bibtex
						    @Article{radovic2017aerial,
								author = {Radovic, Matija and Adarkwa, Offei and Wang, Qiaosong},
								title = {Object Recognition in Aerial Images Using Convolutional Neural Networks},
								journal = {Journal of Imaging},
								volume = {3},
								year = {2017},
								number = {2},
								article number = {21},
								url = {http://www.mdpi.com/2313-433X/3/2/21},
								issn = {2313-433X},
								abstract = {There are numerous applications of unmanned aerial vehicles (UAVs) in the management of civil infrastructure assets. A few examples include routine bridge inspections, disaster management, power line surveillance and traffic surveying. As UAV applications become widespread, increased levels of autonomy and independent decision-making are necessary to improve the safety, efficiency, and accuracy of the devices. This paper details the procedure and parameters used for the training of convolutional neural networks (CNNs) on a set of aerial images for efficient and automated object recognition. Potential application areas in the transportation field are also highlighted. The accuracy and reliability of CNNs depend on the network’s training and the selection of operational parameters. This paper details the CNN training procedure and parameter selection. The object recognition results show that by selecting a proper set of parameters, a CNN can detect and classify objects with a high level of accuracy (97.5%) and computational efficiency. Furthermore, using a convolutional neural network implemented in the “YOLO” (“You Only Look Once”) platform, objects can be tracked, detected (“seen”), and classified (“comprehended”) from video feeds supplied by UAVs in real-time.},
								doi = {10.3390/jimaging3020021}
							}
						   	
						

• Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection
arxiv 2017
pdf bibtex
							@article{najibi2017towards,
							  title={Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection},
							  author={Najibi, Mahyar and Yang, Fan and Wang, Qiaosong and Piramuthu, Robinson},
							  journal={arXiv preprint arXiv:1708.00079},
							  year={2017}
							}
						   	
						

• Visual Search at eBay
ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD) 2017
pdf bibtex
							@inproceedings{yang2017search,
							 author = {Yang, Fan and Kale, Ajinkya and Bubnov, Yury and Stein, Leon and Wang, Qiaosong and Kiapour, Hadi and Piramuthu, Robinson},
							 title = {Visual Search at eBay},
							 booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
							 series = {KDD '17},
							 year = {2017},
							 isbn = {978-1-4503-4887-4},
							 location = {Halifax, NS, Canada},
							 pages = {2101--2110},
							 numpages = {10},
							 url = {http://doi.acm.org/10.1145/3097983.3098162},
							 doi = {10.1145/3097983.3098162},
							 acmid = {3098162},
							 publisher = {ACM},
							 address = {New York, NY, USA},
							 keywords = {deep learning, e-commerce, search engine, semantics, visual search},
							} 
							

						   	
						

Patents

• Multi-functional Intelligent Surveillance and Control System (MISCS) CHINA Patent Issued Feb 24, 2010 CN 200920032192
• Wiimote-Based Infrayed VR Teaching Assistant System CHINA Patent Issued Feb 9, 2011 CN 201020185719
• A Novel Cruise Robot CHINA Patent Issued May 11, 2011 CN 201020185799
• Fast 3D Model Fitting and Anthropometrics Using Synthetic Data US Patent Issued Apr 21, 2016 US 20160110595; CHINA Patent Issued Aug 29, 2017 CN 201580055935; EU Patent Issued Aug 23, 2017 EP3207525
• Automated Saliency Map Estimation US Patent Filed Jul 15, 2016 US 15/192551
• Category Prediction From Semantic Image Clustering US Patent Filed Oct 16, 2016 US 15/294756
• Intelligent Online Personal Assistant With Multi-turn Dialog Based On Visual Search US Patent Filed Oct 16, 2016 US 15/294765
• Determining An Item That Has Confirmed Characteristics US Patent Filed Oct 18, 2016 US 62/375855
• Image Analysis and Prediction Based Visual Search US Patent Filed Oct 20, 2016 US 15/294773
• Intelligent Online Personal Assistant With Offline Visual Search Database US Patent Filed Oct 20, 2016 US 15/294767
• Anchored Search US Patent Filed Dec 6, 2016 US P3311PRV
• Commercial Integration of 3D Models US Patent Filed Mar 2017 US P3320PRV

Awards

• National Third Prize, 11th Chinese National Challenge Cup Competition (Team Leader) China Association for Science and Technology (CAST), Ministry of Education of China (MOE), Ministry of Industry and Information Technology of China (MIIT), 2009 Certificate
• Honorable Mention Prize, Mathematical Contest in Modeling (MCM) (Team Leader) Consortium for Mathematics and its Applications (COMAP), U.S. National Security Agency (NSA), 2010 Certificate
• Excellence Award, Microsft Student Challenge Microsoft Research Asia (MSRA), 2010 Certificate
• Best Student Paper Award, IS&T/SPIE International Conference on Digital Photography The International Society for Optics and Photonics (SPIE), Society for Imaging Science and Technology (IS&T), 2014 Certificate
• 8th Place Finish, DARPA Robotics Challenge Finals (Team Member) Defense Advanced Research Projects Agency (DARPA), 2015 Link

Contact

Email: {my first name}@udel.edu

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