tailieunhanh - Edge detection

Origin of edges, characterizing edges, image gradient, finite difference filters, effects of noise,. As the main contents of the lecture "Edge detection". Each of your content and references for additional lectures will serve the needs of learning and research. | Edge detection Goal: Identify sudden changes (discontinuities) in an image Intuitively, most semantic and shape information from the image can be encoded in the edges More compact than pixels Ideal: artist’s line drawing (but artist is also using object-level knowledge) Source: D. Lowe Origin of edges Edges are caused by a variety of factors: depth discontinuity surface color discontinuity illumination discontinuity surface normal discontinuity Source: Steve Seitz Characterizing edges An edge is a place of rapid change in the image intensity function image intensity function (along horizontal scanline) first derivative edges correspond to extrema of derivative The gradient points in the direction of most rapid increase in intensity Image gradient The gradient of an image: The gradient direction is given by Source: Steve Seitz The edge strength is given by the gradient magnitude How does this direction relate to the direction of the edge? give definition of partial derivative: lim h->0 [f(x+h,y) – f(x,y)]/h Differentiation and convolution Recall, for 2D function, f(x,y): This is linear and shift invariant, so must be the result of a convolution. We could approximate this as (which is obviously a convolution) -1 1 Source: D. Forsyth, D. Lowe Finite difference filters Other approximations of derivative filters exist: Source: K. Grauman Finite differences: example Which one is the gradient in the x-direction (resp. y-direction)? Effects of noise Consider a single row or column of the image Plotting intensity as a function of position gives a signal Where is the edge? Source: S. Seitz How to fix? Effects of noise Finite difference filters respond strongly to noise Image noise results in pixels that look very different from their neighbors Generally, the larger the noise the stronger the response What is to be done? Smoothing the image should help, by forcing pixels different from their neighbors (=noise pixels?) to look more like . | Edge detection Goal: Identify sudden changes (discontinuities) in an image Intuitively, most semantic and shape information from the image can be encoded in the edges More compact than pixels Ideal: artist’s line drawing (but artist is also using object-level knowledge) Source: D. Lowe Origin of edges Edges are caused by a variety of factors: depth discontinuity surface color discontinuity illumination discontinuity surface normal discontinuity Source: Steve Seitz Characterizing edges An edge is a place of rapid change in the image intensity function image intensity function (along horizontal scanline) first derivative edges correspond to extrema of derivative The gradient points in the direction of most rapid increase in intensity Image gradient The gradient of an image: The gradient direction is given by Source: Steve Seitz The edge strength is given by the gradient magnitude How does this direction relate to the direction of the edge? give definition of partial

• Đồ họa - Thiết kế - Flash
• Computer Vision
• Imagine processing
• Computer science
• Edge detection
• Origin of edges
• characterizing edges
• 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
• 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
• 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