CSC589 2017 Fall

Introduction to Computer Vision

This repository contains the homework solutions and in-class demos used in CSC 589, Introduction to Computer Vision, taught at American University in DC, by Professor Bei Xiao. Problem sets, demo codes, and projects solutions written in Python.

Course Overview:

This course is an introduction to current algorithms used in computer vision and computational photography (automatic image editing and manipulations). We will start from low-level image processing (edges), and then move to mid-level feature analysis (texture, color, motion), and eventually to high-level image and video understanding (objects, faces, scene, human activity). The topics include basic image processing and image analysis, camera models, texture synthesis, motion analysis, automatic image editing, object and scene recognition, face and pose recognition and a gentle survey of deep learning methods for computer vision. We will develop the intuitions and mathematics of the methods in class, and then learn about the difference between theory and practice in projects.

Topics covered:

• Linear Filtering
• Image gradients and edge detection
• Thinking in Frequency
• Image resampling and Gaussian pyramids
• Feature Detection
• Image descriptors
• Feature matching
• Alignment
• Camera models
• Stereos
• Motion
• Color and lighting
• Introduction to Recognition
• Basic Machine Learning Methods
• Deep learning

Lecture slides and demo codes:

All lectures and demos codes are here

Homeworks

Problem set 1: Basic numpy/scipy and image processing

Solution see Problem 1 solution

Problem set 2: Image filtering, histogram equalization and steerable filters

Solution see Problem 2 solution

Problem set 3: Hybrid Images