• Sam Goree, Bardia Doosti, David Crandall, Norman Su, "Yes, websites really are starting to look more similar," The Conversation, 2020. [PDF] [bibtek]
  • Satoshi Tsutsui, Arjun Chandrasekaran, Md Alimoor Reza, David Crandall, Chen Yu, "A Computational Model of Early Word Learning from the Infant's Point of View," Annual Conference of the Cognitive Science Society (CogSci), 2020. [bibtek]
  • Rakibul Hasan, David Crandall, Mario Fritz, Apu Kapadia, "Automatically Detecting Bystanders in Photos to Reduce Privacy Risks," IEEE Security and Privacy (Oakland), 2020. [bibtek]
  • Bardia Doosti, Shujon Naha, Majid Mirbagheri, David Crandall, "HOPE-Net: A Graph-based Model for Hand-Object Pose Estimation," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. (Poster, 22.1% acceptance rate) [PDF] [bibtek] [Project page]
  • Xiankai Lu, Wenguan Wang, Jianbing Shen, Yu-Wing Tai, David Crandall, Steven Hoi, "Learning Video Object Segmentation from Unlabeled Videos," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. (Poster, 22.1% acceptance rate) [PDF] [bibtek]
  • Zehua Zhang, Ashish Tawari, Sujitha Martin, David Crandall, "Interaction Graph for Object Importance Estimation in On-road Driving Videos," IEEE Conference on Robotics and Automation (ICRA), 2020. (Oral, 42% acceptance rate) [PDF] [bibtek]
  • Lei Yuan, Violet Xiang, David Crandall, Linda Smith, "Learning the generative principles of a symbol system from limited examples," Cognition, 2020. (impact factor = 3.537) [bibtek]
  • Roberto Hoyle, Luke Stark, Qatrunnada Ismail, David Crandall, Apu Kapadia, Denise Anthony, "Privacy Norms and Preferences for Photos Posted Online," ACM Transactions on Computer-Human Interaction, 2020. (impact factor = 2.227) [bibtek]
  • David Leake, David Crandall, "Bringing Case Based Reasoning to Deep Learning," International Conference on Case-Based Reasoning Special Track on Challenges and Promises, 2020. [bibtek]
  • Md Alimoor Reza, Kai Chen, Akshay Naik, David Crandall, Soon-Heung Jung, "Automatic dense annotation for monocular 3d scene understanding," IEEE Access, 2020. (impact factor = 4.098) [bibtek]
  • Md Alimoor Reza, Zhenhua Chen, David Crandall, "Deep Neural Network-based Detection and Verification of Microelectronic Images," Journal of Hardware and Systems Security, 2020. [bibtek]
  • Shujon Naha, Qingyang Xiao, Prianka Banik, Md Alimoor Reza, David Crandall, "Pose-guided knowledge transfer for object part segmentation," IEEE Conference on Computer Vision and Pattern Recognition Workshop on Visual Learning with Limited Labels, 2020. [bibtek]
  • Oluwanisola Ibikunle, John Paden, Maryam Rahnemoonfar, David Crandall, Masoud Yari, "Snow Radar Layer Tracking using an Iterative Neural Network Approach," IEEE International Geoscience and Remote Sensing Symposium, 2020. [bibtek]


















  • Jiebo Luo, David Crandall, Amit Singhal, Matthew Boutell, Robert Gray, "Psychophysical study of image orientation perception," Spatial Vision, 2003. (impact factor = 1.037) [PDF] [bibtek]
  • Jiebo Luo, Amit Singhal, David Crandall, Robert Gray, "A Psychophysical Study of Image Orientation Determination," SPIE Conference on Human Vision and Image Processing, 2003. [bibtek]





The IU Computer Vision Lab's projects and activities have been funded, in part, by grants and contracts from the Air Force Office of Scientific Research (AFOSR), the Defense Threat Reduction Agency (DTRA), Dzyne Technologies, EgoVid, Inc., ETRI, Facebook, Google, Grant Thornton LLP, IARPA, the Indiana Innovation Institute (IN3), the IU Data to Insight Center, the IU Office of the Vice Provost for Research through an Emerging Areas of Research grant, the IU Social Sciences Research Commons, the Lilly Endowment, NASA, National Science Foundation (IIS-1253549, CNS-1834899, CNS-1408730, BCS-1842817, CNS-1744748, IIS-1257141, IIS-1852294), NVidia, ObjectVideo, Office of Naval Research (ONR), Pixm, Inc., and the U.S. Navy. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the U.S. Government, or any sponsor.