tailieunhanh - Comp 776: Computer vision

What kind of information can we extract from an image, why study computer vision, why is computer vision difficult,. To help you answer the questions above, you are invited to refer to the content of the curriculum ''Comp 776: Computer vision". Hope this is useful references for you. | COMP 776: Computer Vision Today Introduction to computer vision Course overview Course requirements The goal of computer vision To bridge the gap between pixels and “meaning” What we see What a computer sees Source: S. Narasimhan What kind of information can we extract from an image? Metric 3D information Semantic information Vision as measurement device Real-time stereo Structure from motion NASA Mars Rover Pollefeys et al. Reconstruction from Internet photo collections Goesele et al. Vision as a source of semantic information slide credit: Fei-Fei, Fergus & Torralba Object categorization sky building flag wall banner bus cars bus face street lamp slide credit: Fei-Fei, Fergus & Torralba Scene and context categorization outdoor city traffic slide credit: Fei-Fei, Fergus & Torralba Qualitative spatial information slanted rigid moving object horizontal vertical slide credit: Fei-Fei, Fergus & Torralba rigid moving object non-rigid moving object Why study computer vision? Personal photo albums Surveillance and security Movies, news, sports Medical and scientific images Vision is useful: Images and video are everywhere! Why study computer vision? Vision is useful Vision is interesting Vision is difficult Half of primate cerebral cortex is devoted to visual processing Achieving human-level visual perception is probably “AI-complete” Why is computer vision difficult? Challenges: viewpoint variation Michelangelo 1475-1564 slide credit: Fei-Fei, Fergus & Torralba Challenges: illumination image credit: J. Koenderink Challenges: scale slide credit: Fei-Fei, Fergus & Torralba Challenges: deformation Xu, Beihong 1943 slide credit: Fei-Fei, Fergus & Torralba Challenges: occlusion Magritte, 1957 slide credit: Fei-Fei, Fergus & Torralba Challenges: background clutter Challenges: Motion Challenges: object intra-class variation slide credit: Fei-Fei, Fergus & Torralba Challenges: local ambiguity . | COMP 776: Computer Vision Today Introduction to computer vision Course overview Course requirements The goal of computer vision To bridge the gap between pixels and “meaning” What we see What a computer sees Source: S. Narasimhan What kind of information can we extract from an image? Metric 3D information Semantic information Vision as measurement device Real-time stereo Structure from motion NASA Mars Rover Pollefeys et al. Reconstruction from Internet photo collections Goesele et al. Vision as a source of semantic information slide credit: Fei-Fei, Fergus & Torralba Object categorization sky building flag wall banner bus cars bus face street lamp slide credit: Fei-Fei, Fergus & Torralba Scene and context categorization outdoor city traffic slide credit: Fei-Fei, Fergus & Torralba Qualitative spatial information slanted rigid moving object horizontal vertical slide credit: Fei-Fei, Fergus & Torralba rigid moving object non-rigid moving object Why