Computer Vision

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Computer Vision

• No. 1
• What is the Computer Vision?

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Instructor

• Katsushi Ikeuchi
• Pointers
• 03-5452-6242
• cvl-sec_at_iis.u-tokyo.ac.jp
• 4-6-1 Komaba Meguro-ku
• http//www.cvl.iis.u-tokyo.ac.jp

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Evaluation

• attendance 50
• report 50

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Schedule

• Shape-from-X
• Analysis of line-drawing
• Shape-from-shading
• Binocular stereo
• Interpretation
• Interpolation
• Representation
• Special topics
• Modeling from reality

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Katsu Ikeuchi
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Demonstration Videos
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Photometric Stereo (1980)

• Brightness difference -gt 3D shape
• 3D shape -gt 3D Pose determination
• 3DPose -gt Grasping

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Bin Picking
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Assembly Plan from Observation (1990)
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Recent Result Assembly plan from observation
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(No Transcript)
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Learning Human Dance
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Motion Capture Data
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Robot Dancing
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Modeling Cultural Heritage
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Virtual City
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Virtual City
Vehicle
Pedestrian
Speed10km/h
Vehicle
Near Yoyogi park
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Computer Vision (CV)

• To make a computer to recognize the 3D world as
  we do
• To generate 3D representations from 2D images

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CV and related areas
Image Understanding (AI)
Pattern Recognition (Mathematical theories)
Image Processing (Signal processing)
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CV and related areas
Image Understanding (AI)
Pattern Recognition (Mathematical theories)
Image Processing (Signal processing)
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Image Processing

• To get better images 2D-to-2D

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CV and related areas
Image Understanding (AI)
Pattern Recognition (Mathematical theories)
Image Processing (Signal processing)
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Pattern Recognition

• Decision making mathematical theories

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CV and related areas
Image Understanding (AI)
Pattern Recognition (Mathematical theories)
Image Processing (Signal processing)
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Image Understanding

• Scene description

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Why difficult ?

• A lot of data
• Ambiguity
• Projection of a 3D world to a 2D image
• Many factors to influence the image
• Illumination condition
• Object shape
• Camera characteristics

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Image
Foggy golden triangle in Pittsburgh
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But
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A lot of data

• Landsat image
• 1scene 3300 x 2300 x 4 30000000 bytes
• 200 scenes/ day
• Color TV image
• 512 x 512 x 3 x 30 25000000 bytes/sec

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Why difficult ?

• A lot of data
• Ambiguity
• Projection of a 3D world to a 2D image
• Many factors to influence the image
• Illumination condition
• Object shape
• Camera characteristics

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Illusion due to the projection
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Why difficult ?

• A lot of data
• Ambiguity
• Projection of a 3D world to a 2D image
• Many factors to influence to the image
• Illumination condition
• Object shape
• Camera characteristics

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Image

• A image is a matrix of pixels
• Each pixel
• brightness
• Color
• Distance

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Inside and Outside (Gestalt)
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Common sense

• To formulate the common sense
• ? research topics

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Current issues

• A lot of data
• Computational sensor
• Vision board
• Ambiguity
• Projective geometry
• constraints
• Many factors
• Physics-based vision

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Application areas
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Application areas
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What is Computer Vision?

• Vision is an information processing task that
  constructs efficient symbolic descriptions of the
  world from images. (Marr)
• Vision is inverse graphics.
• Vision is looks easy, but is difficult.
• Vision is difficult, but is fun. (Kanade)
• Vision is an engineering science to create
• an alternative of human visual systems on
  computers (Ikeuchi)

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References

• Journals
• Inter. J. Computer Vision
• IEEE Trans. Pattern Analysis and Machine
  Intelligence
• IEICE D-2
• IPSJ Trans CVIM
• International conferences
• Inter. Conf. Computer Vision (ICCV)
• Computer Vision and Pattern Recognition (CVPR)
• Asian Conf. Computer Vision (ACCV)
• Special interest groups
• IPSJ CVIM
• IEICE PRMU

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Schedule
4/13 Introduction 4/20 Line drawing 4/27 No
class 5/4 Holiday 5/11 Perspective
projection 5/18 Binocular stereo 5/25 Motion
analysis
6/2 Shape from shading 6/8 No class 6/15
Interpolation 6/22 No class 6/29 Object
representation 7/6 Modeling from reality 7/13
Virtual City