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Introduction to Computer Vision Session 01

Course : COMP7116 – Computer Vision. Introduction to Computer Vision Session 01. Prof. Dr. Widodo Budiharto 2018. What is Computer Vision. Computer Vision makes computers understand images and video. What is Computer Vision. Ballard and Brown:

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Introduction to Computer Vision Session 01

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  1. Course : COMP7116 – Computer Vision Introduction to Computer VisionSession 01 Prof. Dr. Widodo Budiharto 2018

  2. What is Computer Vision Computer Vision makes computers understand images and video

  3. What is Computer Vision • Ballard and Brown: • The construction of explicit, meaningful description of physical objects from images • Forsyth and Ponce: • Extracting descriptions of the world from pictures or sequences of pictures

  4. What is Computer Vision

  5. Demo Robot EdukasiEduRobot

  6. Implementation of Computer Vision Computer Vision makes computers understand images and video What kind of scene Where are the cars How far is the building Etc.

  7. Implementation of Computer Vision Optical Character Recognition (OCR) Technology to convert scanned docs to text Digit recognition, AT&T labs http://www.research.att.com/~yann/ License plate readers http://en.wikipedia.org/wiki/Automatic_number_plate_recognition

  8. Implementation of Computer Vision Cap Inspection System Low-level Image Analysis : Identify edges, regions Mid-level : Distinguish "cap" from "no cap" Estimation : What are orientation of cap, height of liquid

  9. Implementation of Computer Vision Face Detection Many new digital cameras now detect faces From http://www-2.cs.cmu.edu/~har/faces.html

  10. Implementation of Computer Vision Vision-based Biometrics How the Afghan Girl was identified by Her Iris Patterns

  11. Implementation of Computer Vision Object Recognition (in mobile phones) Point & Find, NokiaGoogle Goggles

  12. Implementation of Computer Vision Vision in Space NASA'S Mars Exploration Rover Spirit captured this westward view from atop a low plateau where Spirit spent the closing months of 2007. • Vision systems (JPL) used for several tasks • Panorama stitching • 3D terrain modeling • Obstacle detection, position tracking • For more, read “Computer Vision on Mars” by Matthies et al.

  13. Implementation of Computer Vision Mobile Robots NASA’s Mars Spirit Rover http://en.wikipedia.org/wiki/Spirit_rover http://www.robocup.org/

  14. Implementation of Computer Vision Google Cars

  15. Implementation of Computer Vision 3D Reconstruction

  16. Implementation of Computer Vision Medical Imaging Image guided surgery Grimson et al., MIT 3D imaging MRI, CT

  17. High Level Capability Computer Vision System (CVS) is expected to have high level capabilities like Human Visual System (HVS) does such as : • Object detection – is an object present in the scene ? If so, where is its boundaries • Recognition – putting a label on an object • Description – assigning properties to objects • 3D inference – interpreting a 3D object from 2D views • Interpreting motion

  18. Approach in Computer Vision 3-D World Objects Images Edges/Region/Depth Models and Assumptions Features/Surfaces Objects Detection & Recognitions

  19. The Three Processing Level Low-level Processing Standard procedures are applied to improve image quality Procedures are required to have no intelligent capabilities

  20. The Three Processing Level Intermediate-level Processing Extract and characterize components in the image Some intelligent capabilities are required.

  21. The Three Processing Level High-level Processing Recognition and interpretation. Procedures require high intelligent capabilities.

  22. Typical Architecture of Computer Vision System

  23. Exercise (10 menit) Jelaskanperbedaan face detection dengan face recognition system. Buat Program Display Image menggunakan Python import numpy as np import cv2 # Load an color image in grayscale img = cv2.imread('messi5.jpg',0) cv2.imshow('image',img) cv2.waitKey(0) cv2.destroyAllWindows()

  24. Project on Session 13Kelompok 4-5 orang/grup BuatAplikasi Computer Vision yang bergunamenggunakan Python dan OpenCV. DapatdigunakanuntuklombadandikembangkanuntukSkripsi. Dipresentasikanmenggunakan ppt secarakelompok di pertemuan 13 Softcopy Laporanberbentuk paper 5-8 halamandengannama yang lengkapdapatdikirimkeprof.Widodo.Budiharto@gmail.com

  25. References Richard Szeliski. (2011). Computer Vision: Algorithms and applications. 01. Springer. Chapter 1. Widodo Budiharto, Computer Vision. (2015). Andi offset Publisher Yogyakarta. https://cs.brown.edu/courses/cs143/lectures/01.pdf

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