Overview
How can computers understand the visual world of humans will be discussed in this course. This course treats vision as a process of inference from noisy and uncertain data and emphasizes probabilistic, statistical, data-driven approaches. Topics include image processing; segmentation, grouping, and boundary detection; recognition and detection; motion estimation and structure from motion. This will emphasize the core vision tasks of scene understanding and recognition. We will train and evaluate classifiers to recognize various visual phenomena.
The goal of computer vision is to enable computers see the world. By using a camera as the eye of a computer, studies in computer vision seek to develop better means to capture and extract useful visual information from images and videos and to use such information to automatically interpret the beautiful world surrounding us. This course provides an introduction to computer vision. The first half of this course will focus on fundamental models and algorithms in computer vision, including such topics as image formation, image sensing, image filtering, edge extraction, brightness and reflectance.
Learning Objectives
Upon completion of this course, students will:
- This course aims for students to understand and apply fundamental mathematical and computationaltechniques in computer vision;
- This course aims for students to implement basic computer vision applications;
- Be familiar with both the theoretical and practical aspects of computing with images;
- Have described the foundation of image formation, measurement, and analysis;
- Have implemented common methods for robust image matching and alignment;
- Understand the geometric relationships between 2D images and the 3D world;
- Have gained exposure to object and scene recognition and categorization from images;
- Developed the practical skills necessary to build computer vision applications.
9. Writing MatLab Script for Computer Vision
10. Writing program for daily live problems
Prerequisites
Digital Image Processing ( Pengolahan Citra Digital)
Software
We will use MatLab 2014 above for the course.
Reading References ( e-book)
· Mathwork. Computer Vision Tool Box: User’s Guide
· Gonzales, Rafael and Richard Woods. 2002. Digital Image Processing. Prentice Hall, Upper Saddle River, New Jersey, USA.
· Lindo Saphiro and George Stockman. 2001. Computer Vision. Upper Saddle River, New Jersey, USA.
- Pengajar: Suhendro Irianto