TAILIEUCHUNG - Feature extraction: Corners and blobs

Characteristics of good features, applications, finding corners, corner detection basic idea, corner detection mathematics,. As the main contents of the lectures "Feature extraction: Corners and blobs". Invite you to consult for additional documents for the academic needs and research. | Feature extraction: Corners and blobs Why extract features? Motivation: panorama stitching We have two images – how do we combine them? Why extract features? Motivation: panorama stitching We have two images – how do we combine them? Step 1: extract features Step 2: match features Why extract features? Motivation: panorama stitching We have two images – how do we combine them? Step 1: extract features Step 2: match features Step 3: align images Characteristics of good features Repeatability The same feature can be found in several images despite geometric and photometric transformations Saliency Each feature has a distinctive description Compactness and efficiency Many fewer features than image pixels Locality A feature occupies a relatively small area of the image; robust to clutter and occlusion Applications Feature points are used for: Motion tracking Image alignment 3D reconstruction Object recognition Indexing and database retrieval Robot navigation Finding Corners Key property: in the region around a corner, image gradient has two or more dominant directions Corners are repeatable and distinctive and . "A Combined Corner and Edge Detector.“ Proceedings of the 4th Alvey Vision Conference: pages 147--151. Corner Detection: Basic Idea We should easily recognize the point by looking through a small window Shifting a window in any direction should give a large change in intensity “edge”: no change along the edge direction “corner”: significant change in all directions “flat” region: no change in all directions Source: A. Efros Corner Detection: Mathematics Change in appearance for the shift [u,v]: Intensity Shifted intensity Window function or Window function w(x,y) = Gaussian 1 in window, 0 outside Source: R. Szeliski Corner Detection: Mathematics Change in appearance for the shift [u,v]: I(x, y) E(u, v) E(0,0) E(3,2) Corner Detection: Mathematics Change in appearance for the shift [u,v]: Second-order .

• Đồ họa - Thiết kế - Flash
• Computer Vision
• Imagine processing
• Computer science
• Feature extraction
• Corners and blobs
• Finding corners
• 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
• Edge detection
• Origin of edges
• characterizing edges
• 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