ECE 549
ECE 549 - Computer Vision
Fall 2021
Title | Rubric | Section | CRN | Type | Hours | Times | Days | Location | Instructor |
---|---|---|---|---|---|---|---|---|---|
Computer Vision | CS543 | A | 37132 | LCD | 4 | 1100 - 1215 | W F | 1404 Siebel Center for Comp Sci | Svetlana Lazebnik |
Computer Vision | CS543 | ONL | 76188 | OLC | 4 | - | Svetlana Lazebnik | ||
Computer Vision | ECE549 | A | 37131 | LCD | 4 | 1100 - 1215 | W F | 1404 Siebel Center for Comp Sci | Svetlana Lazebnik |
Computer Vision | ECE549 | ONL | 76187 | OLC | 4 | - | Svetlana Lazebnik |
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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.
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.
Last updated
2/13/2013