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Intelligent Computer Vision 1

· 3 min read
Jeongwon Her

computer-vision Intelligent Computer Vision 1(개요)

목차

수업교재

Class Materials

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