English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 1h 29m | 311 MB
Image classification, object detection, video processing, and more
Unleash the power of computer vision with Python to carry out image processing and computer vision techniques
This video course is a practical guide for developers who want to get started with building computer vision applications using Python 3. The video is divided into six sections:
- The Fundamentals of Image Processing
- Applied Computer Vision
- Object detection
- Making Applications Smarter
- Extending your Capabilities using OpenCV
- Getting Hands on
Throughout this video course, three image processing libraries: Pillow, Scikit-Image, and OpenCV are used to implement different computer vision algorithms.
The course will help you build Computer Vision applications that are capable of working in real-world scenarios effectively. Some of the applications that we look at in the course are Optical Character Recognition, Object Tracking and building a Computer Vision as a Service platform that works over the internet.
What You Will Learn
- Work with open source libraries such Pillow, Scikit-image, and OpenCV
- Write programs such as edge detection, color processing, image feature extraction, and more
- Implement feature detection algorithms such as LBP and ORB
- Understand Convolutional Neural Networks to learn patterns in images
Table of Contents
01 The Course Overview
02 Image Processing and Its Applications
03 Image Processing Libraries – Pillow
04 Geometrical Transformation – Pillow
05 Introduction to scikit-image
06 Image Derivatives
07 Understanding Image Filters
08 Custom Filters and Image Thresholding
09 Edge Detection
10 Harris Corner Detection
11 Local Binary Patterns
12 Oriented FAST and Rotated BRIEF (ORB)
13 Image Stitching
14 Contour Detection and the Watershed Algorithm
15 Superpixels and Normalized Graph Cut
16 Introduction to Machine Learning
17 Logistic Regression
18 Support Vector Machines
19 K-means Clustering
20 Introduction to Neural Network
21 MNIST Digit Classification Using Neural Networks
22 Convolutional Neural Networks
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