Practical OpenCV 3 Image Processing with Python
0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
9 students enrolled
Wishlisted Wishlist

Please confirm that you want to add Practical OpenCV 3 Image Processing with Python to your Wishlist.

Add to Wishlist

Practical OpenCV 3 Image Processing with Python

Get familiar with Open CV 3 and learn to build amazing computer vision applications
0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
9 students enrolled
Created by Packt Publishing
Last updated 8/2017
English
Curiosity Sale
Current price: $10 Original price: $125 Discount: 92% off
30-Day Money-Back Guarantee
Includes:
  • 2 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Learn Morphology and Open, Close operations
  • Work with Histogram Generation and Manipulation
  • Discover Background Subtraction from Images
  • Build an Image Search Engine from Scratch based on feature extraction
  • Explore scene understanding and automatic labeling from images
  • Look into Delaunay Triangulation and Voronoi Tessellation
  • Essentials of medical imaging and segmentation
  • Apply image processing techniques
View Curriculum
Requirements
  • Viewers are expected to be familiar with OpenCV's concepts and Python libraries. A basic knowledge of Python programming is expected and assumed.
Description

OpenCV is a native cross-platform C++ Library for computer vision, machine learning, and image processing. It is increasingly being adopted in Python for developing applications to process visual data such as photographs or videos. OpenCV has C++/C, Python, and Java interfaces with support for Windows, Linux, Mac, iOS, and Android, and offers extensive libraries with over 500 functions.
This video demonstrates how to develop a series of intermediate-to-advanced projects using OpenCV and Python, rather than teaching the core concepts of OpenCV in theoretical lessons. Instead, the working projects developed in this video teach the viewer how to apply their theoretical knowledge to topics such as image manipulation, augmented reality, object tracking, 3D scene reconstruction, statistical learning, and object categorization.
By the end of this video course, viewers will be OpenCV experts whose newly gained experience allows them to develop their own advanced computer vision applications.

About the Author

Riaz Munshi has a Bachelor's and a Master's degree in Computer Science from the University at Buffalo, NY. He is a computer vision and machine learning enthusiast. Riaz possess over three years' experience working on challenging problems in mobility, computing, and augmented reality. He has a solid foundation in Computer Science, with strong competencies in data structures, algorithms, and software design. Currently he is working at Yahoo as a Software Engineer, exploring use-cases that harness the power of AR in controlling robots. He makes robots perform more efficiently at their job by guiding them remotely via holograms.

Who is the target audience?
  • This video is for intermediate users of OpenCV who aim to augment their skills by developing advanced practical applications.
  • Boost your knowledge of Computer Vision and image processing by developing real-world projects in OpenCV 3
Students Who Viewed This Course Also Viewed
Curriculum For This Course
18 Lectures
01:50:38
+
Building an Image Search Engine from Scratch
6 Lectures 28:17

This video gives an overview of the entire course.

Preview 03:23

In this video, we will be using Hough Transformation to detect lines/circles, or some other basic Shapes in an Image.

Learning about Hough Transformations
06:27

This video shows, how to Stretch, Shrink, Warp, and Rotate an Image using OpenCV 3.

Stretch, Shrink, Warp, and Rotate Using OpenCV 3
04:18

In this video, we will be computing image derivatives on images using kernels for edge and blob detection.

Image Derivatives
03:59

In this video, we will be correcting the exposure in images with Histogram Equalization and Project one Overview.
Histogram Equalization
04:46

In this video, we will be building a reverse image search engine using RGB histogram as feature vector.
Reverse Image Search
05:24
+
Finding Targets and Number Plate Recognition in Video Stream
6 Lectures 32:35

In this video, we will segment binary images by extracting contours of arbitrary shapes and sizes.

Preview 08:07

In this video, we will find templates in an image using sliding window based operation for object detection.
Template Matching for Object Detection
02:57

In this video, we will take a look at Background Subtraction and different ways of achieving it.
Background Subtraction from Images
04:32

In this video, we will introduce to techniques like Delaunay Triangulation and Voronoi Tessellation which are widely used to determine the spatial dimension of an object.

Delaunay Triangulation and Voronoi Tessellation
05:14

In this video, we will learn mean-shift segmentation, and how can we use concept from mean-shift for object tracking, and also getting started with the project for the section.

Mean-Shift Segmentation
05:44

This video will show applications of computer vision in medical imaging and segmentation. And we will build systems to automatically detect number plates.

Medical Imaging and Segmentation
06:01
+
Scene Understanding and Automatic Labeling from Images
6 Lectures 49:46

In this video, we will learn the concepts behind Harris Corner Detection and implementing Harris Corner Detection from scratch.

Preview 09:04

This video will make you understand and check the various different algorithms to find features in OpenCV3 like SIFT, SURF, FAST, BRIEF, and ORB.

SIFT, SURF, FAST, BRIEF, and ORB Algorithms
05:55

In this video, we will match features between sequential images using FLANN matcher and also using homography for finding known objects in complex images.

Feature Matching and Homography to Recognize Objects
06:38

In this video, we will learn about how Mean-Shift and Cam-Shift can be used to track objects in video and Optical flow to trace flow of an image objects in videos.

Mean-Shift, Cam-Shift, and Optical Flow
07:26

In this video, we will use Convolutional Neural Nets to learn features from images and learn how to recognize numbers using LeNet-5 architecture.

Feature Extraction Using Convolutional Neural Nets (CNNs)
10:42

In this video, we will learn how we can perform visual object recognition using CNNs and we will also implement the project for scene understanding and an automatic labelling from images.

Visual Object Recognition and Classification Using CNNs
10:01
About the Instructor
Packt Publishing
3.9 Average rating
7,336 Reviews
52,385 Students
616 Courses
Tech Knowledge in Motion

Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

With an extensive library of content - more than 4000 books and video courses -Packt's mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what's important to them now.

From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Packt Udemy courses continue this tradition, bringing you comprehensive yet concise video courses straight from the experts.