Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Monday, 01 December 2003 William H. Hsu Department of Computing and Information Sciences, KSU Readings: Chapter 24, Russell and Norvig Computer Vision 1 of 2 Lecture 39

Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Lecture Outline Read Chapter 24, Russell and Norvig Reference: Robot Vision, B. K. P. Horn The Vision Problem –Early vs. late vision –Marr’s 2 ½ - D sketch –Waltz diagrams Shape from Shading –Ikeuchi-Horn method –Subproblems: edge detection, segmentation Optical Flow

Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Terminology Vision Problem –Early vs. late vision –Marr’s 2 ½ - D sketch –Waltz diagrams Shape from Shading –Ikeuchi-Horn method –Subproblems: edge detection, segmentation Optical Flow

Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Summary Points Reference: Robot Vision, B. K. P. Horn The Vision Problem –Early vs. late vision –Marr’s 2 ½ - D sketch –Waltz diagrams Shape from Shading –Ikeuchi-Horn method –Subproblems: edge detection, segmentation Optical Flow Next Week –Natural Language Processing survey –Final exam review

Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Lecture Outline Read Chapter 24, Russell and Norvig References –Robot Vision, B. K. P. Horn –Courses: –UCB CS 280: The Vision Problem –Early vs. late vision –Marr’s 2 ½ - D sketch –Waltz diagrams Shape from Shading –Ikeuchi-Horn method –Subproblems: edge detection, segmentation Optical Flow

Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Adapted from slides © 1999 J. Malik, UC Berkeley (CS 280 Computer Vision) Line Drawing Interpretation

Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Adapted from slides © 1999 J. Malik, UC Berkeley (CS 280 Computer Vision) Line Labeling [1]: Solid Polyhedra and Other Shapes Waltz, others

Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Adapted from slides © 1999 J. Malik, UC Berkeley (CS 280 Computer Vision) Line Labeling [2]: Junctions Junctions occur at tangent discontinuities False T-junctions

Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Adapted from slides © 1999 T. Leung, UC Berkeley (CS 280 Computer Vision) Orientation and Texture Discrimination (Textons) [1]

Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Adapted from slides © 1999 J. Malik, UC Berkeley (CS 280 Computer Vision) Orientation and Texture Discrimination (Textons) [2]

Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Adapted from slides © 1999 J. Malik, UC Berkeley (CS 280 Computer Vision) Segmentation (Grouping) [1]: Definition

Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Adapted from slides © 1999 J. Malik, UC Berkeley (CS 280 Computer Vision) Segmentation (Grouping) [2]: Physical Factors

Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Adapted from slides © 1999 J. Malik, UC Berkeley (CS 280 Computer Vision) Edge Detection [1]: Convolutional Filters and Gaussian Smoothing

Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Adapted from slides © 1999 J. Malik, UC Berkeley (CS 280 Computer Vision) Edge Detection [2]: Difference of Gaussian

Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Adapted from slides © 1999 J. Malik, UC Berkeley (CS 280 Computer Vision) Binocular Stereo [1]: Stereo Correspondence – Properties

Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Adapted from slides © 1999 J. Malik, UC Berkeley (CS 280 Computer Vision) Binocular Stereo [2]: Stereo Correspondence – Open Problems

Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Adapted from slides © 1999 J. Malik, UC Berkeley (CS 280 Computer Vision) Optical Flow

Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Terminology Vision Problem –Early vs. late vision –Marr’s 2 ½ - D sketch –Waltz diagrams Shape from Shading –Ikeuchi-Horn method –Subproblems: edge detection, segmentation Optical Flow

Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Summary Points References –Robot Vision, B. K. P. Horn – The Vision Problem –Early vs. late vision –Marr’s 2 ½ - D sketch –Waltz diagrams Shape from Shading –Ikeuchi-Horn method –Subproblems: edge detection, segmentation Optical Flow Next Week –Natural Language Processing (NLP) survey –Final review