Intelligent Computer Vision 1(개요)

목차

• 수업교재
• Computer Vision
• Image formation
• Digital Image Formation

수업교재

Class Materials

• Multiple view geometry in computer vision (2nd)
• Computer Vision: Algorithms and Applications (2010)

Computer Vision

Szeliski p.1~28

A theme in the development of this field has been to duplicate the abilites of human vision by electronically perceiving and understanding an image.

Goal of computer vision
=> Write computer programs that can interpret images.

Can computers match human vision

=> Mostly no

• humans are much better at "hard" things
• computers can be better at "easy" things

Current state of the art (Application Area)

Univ. Washington cse455 2012

• Earth Viewers (3D modeling)
• Motion Capture
• Optical Character Recognition (OCR)
• Face detection
• Smile detection
• Object recognition
• Face recognition
• Motion capture
• Sports
• Smart cars
• Self-driving cars
• Robotics
• Vision in space
• Medical imaging

Image Formation

E(x, y, z, lambda) ※ 좌표 + 빛의 파장

Camera(x, y, z, lambda) = E * R

E: Incident light R: Reflectivity function

Projection

Camera_projection(x', y', lambda) = Projection(Camera)

• Perspective: Pinhole
  => Pinhole effect
  big: blurring
  small: diffraction

• Orthogonal

Sensitivity

V_2(lambda) - lambda

Summary

The Image function f_c(x', y') (C = R, G, B) is formed as:
f_c(x', y') = Int{ Camera_projection * V_c(lambda) } delta lambda

Degital Image Formation

We have to discretize:

1. x', y' => x'_i, y'_j => Sampling
2. f_c(x'_i, y'_j) ∈ Range => Quantization

Quantization to P levels

Typically P = 2^8 = 256 (8bit quantization)

Color Depth

• Monochrome (1bit)
• Gray-scale (8bits)
• Color (8 or 16 bits)
• Ture color (24 or 32 bits)
  RGB (24 bits)
  RGB + Alpha (32bits)

Color Space

• Humans (Hue Saturation Lightness/Brightness, HSL or HSB)
• CRT monitors (RGB) *additive
• Printers (Cyan Magenta Yellow Black, CMYK) *subtractive
• Compression (Luminance and Chrominance, YIQ YUV YChCr)
  Y = 0.3R + 0.59G + 0.11B
  U = (B-Y) 0.493
  V = (R-Y) 0.877

Visible Spectrum

UV(400nm) ~ IR (700nm)

Tips

== Classical Vision ==

1. Overview
2. Filtering (Edge detection)
3. Keypoint detection (Corner detection, SIFT ..)
4. Hough transform
   == Multiple View Geometry ==
5. 2D geometry
6. 3D geometry
7. Camera Models and Calibration
   == Deep Learnings ==
8. Image Classification (BoW)
9. Object detection (VJ)
10. Image Segmentation (Graph-cuts)
11. Optical flow (Lukas-Kanade)
12. Pose estimation (DPM)
13. 3D Point cloud

Reference

2021 MIPAL, Gwak Nojun