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Computer Vision Course Introduction (CSE P576) at University of Washington

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The goal of computer vision is to compute properties of the three-dimensional world from digital images. Problems in this field include identifying the 3D shape of an environment, determining how things are moving, and recognizing familiar people and objects, all through analysis of images and video. This course provides an introduction to computer vision, including such topics as feature detection, image segmentation, motion estimation, image mosaics, 3D shape reconstruction, and object recognition.

 

PROFESSOR:

PREREQUISITES

  • Data structures
  • A good working knowledge of C and C++ programming
  • Linear algebra
  • Vector calculus
  • No prior knowledge of vision is assumed.

TEXTBOOKS

Richard Szeliski: Computer Vision: Algorithms and Applications.

GRADING

  • The grade is based on four programming projects
  • There will be no exams or written assignments

SYLLABUS (TENTATIVE)

IMAGE PROCESSING (2 WEEKS)

  • filtering, convolution
  • image pyramids
  • edge detection
  • features
  • hough transform

IMAGE TRANSFORMATION (2 WEEKS)

  • image warping (parametric transformations, resampling, texture mapping)
  • image compositing (alpha blending, color mosaics)
  • segmentation and matting (snakes, scissors)

MOTION ESTIMATION (1 WEEK)

  • optical flow
  • image alignment
  • image mosaics
  • feature tracking

LIGHT (1 WEEK)

  • physics of light
  • color
  • reflection
  • shading
  • shape from shading
  • photometric stereo

3D MODELING (3 WEEKS)

  • projective geometry
  • camera modeling
  • single view metrology
  • camera calibration
  • stereo

OBJECT RECOGNITION AND APPLICATIONS (1 WEEK)

  • eigenfaces
  • applications (graphics, robotics)

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