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

• Đồ họa - Thiết kế - Flash
• Computer Vision
• Imagine processing
• Computer Science
• Computer vision difficult
• Aerial perspective
• Local ambiguity
• Computer vision algorithms
• Computer vision applications
• Image formation
• Feature detection
• Journal of technology and science education
• Experiences using an open source software library
• Teach computer vision subjects
• Computer vision subjects
• Computer vision teaching
• Introduction to computer vision
• Brief history
• Nearby fields
• Related disciplines
• International Journal of Computer Networks and Communications Security
• Application intelligence and vision in internet of things
• Vision in internet of things
• Wireless sensor and actuators
• Radio Frequency Identification
• Practical Computer
• Vision
• with SimpleCV
• Nathan Oostendorp
• Anthony Oliver
• and Katherine Scott
• Mastering OpenCV
• Vision Projects
• Daniel Lélis Baggio
• Khvedchenia Ievgen
• Scripting Cookbook
• Themes Design
• Mapping with Drupal
• OpenCV 2
• Computer Vision Application
• Programming Cookbook
• Generating Image Descriptions
• Computer Vision Detections
• Margaret Mitchell
• scientific reports
• model language
• process natural language
• Nuclear energy
• Nuclear power plant outage
• Automatically detecting abnormal human
• Field operation and preparation
• Data driven simulation
• The pinhole projection model
• Cameras with lenses
• Digital cameras
• Lesson Capturing light
• Capturing light
• Radiometry of thin lenses
• The physics of light
• Cone sensitivity
• Uniform color spaces
• Lesson Linear filtering
• Linear filtering
• Key properties
• Edge detection
• Origin of edges
• characterizing edges
• Feature extraction
• Corners and blobs
• Finding corners
• Vertical least squares
• Total least squares
• General curves
• The hough transform
• Voting schemes
• Hough transform
• Image alignment
• Bing streetside images
• Single view geometry
• Pinhole camera model
• Principal point
• Two view geometry
• View geometry
• epipolar geometry
• Binocular stereo
• Depth from disparity
• Stereo matching
• Multiple baseline stereo
• Plane sweep stereo
• Volumetric stereo
• Structure from motion
• Projective ambiguity
• Similarity ambiguity
• Object recognition
• History and overview
• Scene categorization