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Computer Vision (Python)

This is an introductory course in the dynamic field of using computers to determine useful information from images.

0

Created By : Marco A. Gutiérrez

Part Time

6 weeks

2.5 h/session

Computer Vision (Python)

0 Part Time 6 weeks 2.5 h/session

Objectives

- Understand how Computer Vision algorithm works
- Develop real Computer Vision applications using Python
- Learn how to develop OpenCV based applications
- Obtain, Modify and Process Image for different purposes
- Detect and match features to understand images
- Make the computer detect objects and locate them in images
- Learn how to use PCL to develop 3D data based applications
- Obtain and manage organized and unorganized clouds of 3D points
- Detect and match different geometric shapes in 3D point clouds
- Detect and match features from 3D point clouds
- Use basic Machine Learning on Images and 3D point clouds
- Use basic Deep Learning to detect object in images
- How Convolutional Neural Networks work

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Course Plan

Lesson 1: Introduction to Computer Vision and to Images

- Introduction to Computer Vision
- Source of Images, structure and visualization
- Image Basic Processing
- Image Transformations

Lesson 2: Image Features

- Edges detection
- Shapes detection: lines, circles, ellipses…
- Corners
- Other Features: SIFT, SURF, FAST, BRIEF, etc.

Lesson 3: Object Recognition and Detection

- Brute Force Features Matching
- Flann Based Features Matching
- Template Matching
- Homographies
- Histogram Backpropagation
- Images Segmentation

Lesson 4: Video Processing and 3D from Images

- Video Processing
- Camera Calibration Process
- Obtaining 3D from 2D data
- 3D Pose Estimation
- Stereo Vision

Lesson 5: Computer Vision with 3D data

- Sources for 3D data, structure and visualization
- 3D data basic operations
- 3D preprocessing
- Segmenting 3D data
- Features based on 3D information

Lesson 6: Machine Learning for Computer Vision

- K-means clustering
- Support Vector Machines
- Deep Neural Networks Overview
- Haar-cascade
- k-Nearest Neighbors

Prerequisites

- Students need to have a basic understanding of the Python language.
If you do not have any mastery over Python, you are encouraged to join our Python Development course.
- Students need a laptop with at least 20Gb of free HD space, VirtualBox installed and capable of running a virtual machine with at least 4Gb of RAM.

About this Course:

Computer Vision is an introductory course in the dynamic field of using computers to determine useful information from images. Applications range from image search, robotics, and drone automation to self driving cars and video information extraction.

Students will learn practical skills and applied techniques in computer vision. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon, and then demonstrated using Python code to experiment with and build upon. Every module comes along with extra practical exercises you can bring home to reinforce the learned concepts. They will all be real applications that you can actually showcase as project examples or even use them yourself. For the final project, students will work with a real application, using real data and giving a solution to a problem that can be readily used by the industry. At the end of the course you will have the sufficient knowledge to move into this hot career path.

Assessment

Attendees should complete and submit the following exercises:

Modify at least two of the provided algorithms to perform a different task (Implement Changes)
Finish at least one of the individual coding exercises (Perform Coding Exercises)
Creating a final real object recognition application
Curriculum/Course Schedule

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