TY - GEN
T1 - Emerging images
AU - Mitra, Niloy J.
AU - Chu, Hungkuo
AU - Lee, Tongyee
AU - Wolf, Lior
AU - Yeshurun, Hezy
AU - Cohen-Or, Daniel
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2009
Y1 - 2009
N2 - Emergence refers to the unique human ability to aggregate information from seemingly meaningless pieces, and to perceive a whole that is meaningful. This special skill of humans can constitute an effective scheme to tell humans and machines apart. This paper presents a synthesis technique to generate images of 3D objects that are detectable by humans, but difficult for an automatic algorithm to recognize. The technique allows generating an infinite number of images with emerging figures. Our algorithm is designed so that locally the synthesized images divulge little useful information or cues to assist any segmentation or recognition procedure. Therefore, as we demonstrate, computer vision algorithms are incapable of effectively processing such images. However, when a human observer is presented with an emergence image, synthesized using an object she is familiar with, the figure emerges when observed as a whole. We can control the difficulty level of perceiving the emergence effect through a limited set of parameters. A procedure that synthesizes emergence images can be an effective tool for exploring and understanding the factors affecting computer vision techniques. © 2009 ACM.
AB - Emergence refers to the unique human ability to aggregate information from seemingly meaningless pieces, and to perceive a whole that is meaningful. This special skill of humans can constitute an effective scheme to tell humans and machines apart. This paper presents a synthesis technique to generate images of 3D objects that are detectable by humans, but difficult for an automatic algorithm to recognize. The technique allows generating an infinite number of images with emerging figures. Our algorithm is designed so that locally the synthesized images divulge little useful information or cues to assist any segmentation or recognition procedure. Therefore, as we demonstrate, computer vision algorithms are incapable of effectively processing such images. However, when a human observer is presented with an emergence image, synthesized using an object she is familiar with, the figure emerges when observed as a whole. We can control the difficulty level of perceiving the emergence effect through a limited set of parameters. A procedure that synthesizes emergence images can be an effective tool for exploring and understanding the factors affecting computer vision techniques. © 2009 ACM.
UR - http://hdl.handle.net/10754/575743
UR - http://portal.acm.org/citation.cfm?doid=1661412.1618509
UR - http://www.scopus.com/inward/record.url?scp=77749264880&partnerID=8YFLogxK
U2 - 10.1145/1661412.1618509
DO - 10.1145/1661412.1618509
M3 - Conference contribution
SN - 9781605588582
BT - ACM SIGGRAPH Asia 2009 papers on - SIGGRAPH Asia '09
PB - Association for Computing Machinery (ACM)
ER -