IU Computer Vision Lab

Seeing Differently.

The IU Computer Vision Lab investigates and develops advanced statistical and machine learning techniques for automatically analyzing, understanding, and organizing visual information. Our applications include recognizing objects in consumer images, analyzing human activity in video, discovering patterns in large scientific datasets, reconstructing 3-d models of world landmarks, and even studying visual attention in toddlers.

Selected Recent Papers

Feng Cheng, Xizi Wang, Jie Lei, David Crandall, Mohit Bansal, Gedas Bertasius
CVPR 2023
Sam Goree, Weslie Khoo, David Crandall,
AAAI 2023
Tianfei Zhou, Fatih Porikli, David Crandall, Luc Van Gool, Wenguan Wang
PAMI 2023
Weslie Khoo, Long-Jing Hsu, Kyrie Jig Amon, Pranav Vijay Chakilam, Wei-Chu Chen, Zachary Kaufman, Agness Lungu, Hiroki Sato, Erin Seliger, Manasi Swaminathan, others
HRI 2023
Zheng Chen, Zhengming Ding, David Crandall, Lantao Liu
RA-L 2023
Using manual actions to create visual saliency: an outside-in solution to sustained attention and joint attention
Jane Yang, Linda Smith, David Crandall, Chen Yu
CogSci 2023

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