Course Websites

ECE 549 - Computer Vision

Last offered Fall 2022

Official Description

Information processing approaches to computer vision, algorithms, and architectures for artificial intelligence and robotics systems capable of vision: inference of three-dimensional properties of a scene from its images, such as distance, orientation, motion, size and shape, acquisition, and representation of spatial information for navigation and manipulation in robotics. Course Information: Same as CS 543. Prerequisite: ECE 448 or CS 225.

Related Faculty

Subject Area

  • Robotics, Vision, and Artificial Intelligence

Course Director

Description

Examines information processing approaches to computer vision, and algorithms and architectures for artificial intelligence and robotics systems capable of vision: inference of three-dimensional properties of a scene from its images, such as distance, orientation, motion, size and shape, acquisition and representation of spatial information for navigation and manipulation in robotics.

Notes

Same as CS 543.

Topics

  • Image formation: imaging techniques - monocular, binocular; range, brightness, color; digital images
  • Early vision: reflection and brightness; edge detection; human stereo vision, range from stereo images; surface orientation from shading; motion perception in humans; motion estimation from optical flow and motion stereo; motion from optical flow; human texture perception and surface shape from texture gradient; surface shape from contours; active vision
  • Segmentation: multiresolution and multiscale image representations; segmentation; texture models, recognition and discrimination, texture based segmentation; segmentation; texture models, recognition and discrimination, texture based segmentation; segmentation from motion
  • Representation and robotics: image boundary and region representation, shape description; three-dimensional surface representation, object and viewer-centered representations; dynamic representation; navigation
  • Matching and recognition: three-dimensional model generation; object recognition; relaxation processes
  • Computer architectures for vision: model of computer vision; planar array and hierarchial multiprocessor architectures for image analysis and processing

Detailed Description and Outline

Topics:

  • Image formation: imaging techniques - monocular, binocular; range, brightness, color; digital images
  • Early vision: reflection and brightness; edge detection; human stereo vision, range from stereo images; surface orientation from shading; motion perception in humans; motion estimation from optical flow and motion stereo; motion from optical flow; human texture perception and surface shape from texture gradient; surface shape from contours; active vision
  • Segmentation: multiresolution and multiscale image representations; segmentation; texture models, recognition and discrimination, texture based segmentation; segmentation; texture models, recognition and discrimination, texture based segmentation; segmentation from motion
  • Representation and robotics: image boundary and region representation, shape description; three-dimensional surface representation, object and viewer-centered representations; dynamic representation; navigation
  • Matching and recognition: three-dimensional model generation; object recognition; relaxation processes
  • Computer architectures for vision: model of computer vision; planar array and hierarchial multiprocessor architectures for image analysis and processing

Same as CS 543.

Texts

Forsyth and Ponce, Computer Vision, Prentice Hall, 2003.

Collateral Reading:
B. Horn, Robot Vision, McGraw-Hill.

TitleSectionCRNTypeHoursTimesDaysLocationInstructor
Computer VisionA37131LCD41100 - 1215 M W  1404 Siebel Center for Comp Sci Svetlana Lazebnik
Computer VisionONL76187OLC4 -    Svetlana Lazebnik