tailieunhanh - Today: Cameras

The pinhole projection model, cameras with lenses, digital cameras,. is the main content of the lecture "Today: Cameras". Invite you to consult the detailed content lectures to capture details. | Today: Cameras Overview The pinhole projection model Qualitative properties Perspective projection matrix Cameras with lenses Depth of focus Field of view Lens aberrations Digital cameras Types of sensors Color Let’s design a camera Idea 1: put a piece of film in front of an object Do we get a reasonable image? Slide by Steve Seitz Pinhole camera Add a barrier to block off most of the rays This reduces blurring The opening is known as the aperture Slide by Steve Seitz It gets inverted Pinhole camera model Pinhole model: Captures pencil of rays – all rays through a single point The point is called Center of Projection (focal point) The image is formed on the Image Plane Slide by Steve Seitz Figures © Stephen E. Palmer, 2002 Dimensionality Reduction Machine (3D to 2D) 3D world 2D image What have we lost? Angles Distances (lengths) Slide by A. Efros Projection properties Many-to-one: any points along same visual ray map to same point in image Points → points But projection of points on focal plane is undefined Lines → lines (collinearity is preserved) But line through focal point (visual ray) projects to a point Planes → planes (or half-planes) But plane through focal point projects to line Vanishing points Each direction in space has its own vanishing point All lines going in that direction converge at that point Exception: directions parallel to the image plane All directions in the same plane have vanishing points on the same line Vanishing points Each direction in space has its own vanishing point All lines going in that direction converge at that point Exception: directions parallel to the image plane All directions in the same plane have vanishing points on the same line How do we construct the vanishing point/line? One-point perspective Masaccio, Trinity, Santa Maria Novella, Florence, 1425-28 One of the first consistent uses of perspective in Western art Perspective distortion Problem for architectural photography: . | Today: Cameras Overview The pinhole projection model Qualitative properties Perspective projection matrix Cameras with lenses Depth of focus Field of view Lens aberrations Digital cameras Types of sensors Color Let’s design a camera Idea 1: put a piece of film in front of an object Do we get a reasonable image? Slide by Steve Seitz Pinhole camera Add a barrier to block off most of the rays This reduces blurring The opening is known as the aperture Slide by Steve Seitz It gets inverted Pinhole camera model Pinhole model: Captures pencil of rays – all rays through a single point The point is called Center of Projection (focal point) The image is formed on the Image Plane Slide by Steve Seitz Figures © Stephen E. Palmer, 2002 Dimensionality Reduction Machine (3D to 2D) 3D world 2D image What have we lost? Angles Distances (lengths) Slide by A. Efros Projection properties Many-to-one: any points along same visual ray map to same point in image Points → points But .

• Đồ họa - Thiết kế - Flash
• Computer Vision
• Imagine processing
• Computer Science
• The pinhole projection model
• Cameras with lenses
• Digital cameras
• 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
• Computer vision difficult
• Aerial perspective
• Local ambiguity
• 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
• 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