Computer Vision Genap TA 2015/2016
Mata Kuliah (MK) Computer Vision (CV), Kita belajar bagaimana komputer dapat memahami dunia visual manusia?
MK CV ini melatih visi sebagai proses inferensi dari data pasti dan gangguan dan menekankan pendekatan probabilistik dan statistik.
MK CV ini mencakup persepsi struktur adegan 3D dari stereo, gerak, dan shading; image filtering, smoothing, deteksi tepi; segmentasi dan pengelompokan; analisis tekstur; pembelajaran, recognisi dan pencarian; pelacakan dan estimasi gerak.
Sangat disarankan: telah belajar aljabar linier dasar, kalkulus dan probabilitas.
MK. COMPUTER VISION
1. Introduction to computer vision. [Slide]
Image Formation and Filtering
2. Cameras and optics. [Slide]
3. Light and color. [Slide]
4. Image filtering. [Slide]
5. Thinking in frequency. [Slide]
6. Image pyramids and applications. [Slide]
Feature Detection and Matching
7. Edge detection. [Slide]
8. Interest points and corners. [Slide]
9. Local image features. [Slide]
10. Feature matching and hough transform. [Slide]
11. Model fitting and RANSAC. [Slide]
7. Edge detection. [Slide]
8. Interest points and corners. [Slide]
9. Local image features. [Slide]
10. Feature matching and hough transform. [Slide]
11. Model fitting and RANSAC. [Slide]
Multiple Views and Motion
12. Stereo. [Slide]
13. Epipolar Geometry and Structure from Motion. [Slide]
14. Feature Tracking and Optical Flow. [Slide]
12. Stereo. [Slide]
13. Epipolar Geometry and Structure from Motion. [Slide]
14. Feature Tracking and Optical Flow. [Slide]
