OpenCV Computer Vision Application Programming – Packt Publishing

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OpenCV Computer Vision Application Programming

Incorporate OpenCV’s powerful computer vision application programming techniques to build and make your own applications stand out from the crowd

Incorporate OpenCV’s powerful computer vision application programming techniques to build and make your own applications stand out from the crowd

About This Video

Learn everything you need to get started with OpenCV

Contains many practical examples covering different areas of computer vision that can be mixed and matched to build your own application

Packed with code with relevant explanation to demonstrate real results from real image

In Detail

OpenCV (Open Source Computer Vision) is a library of programming functions that can be used for many applications such as augmented reality, robotics, surveillance, medical imaging, identification, to mention only a few. With OpenCV, developers can avoid the use of complex mathematics and algorithms and focus on developing applications, taking advantage of the comprehensive, ready to use functionality of OpenCV to automate tasks that a human performs visually. This course will expose you to the key concepts of OpenCV and enable you to build your own computer vision applications.

“”OpenCV Computer Vision Application Programming”” allows you to dive into the world of computer vision and get many practical benefits from it with minimal effort. You will learn to recognize and identify specific faces among others, or even train your very own object detector to use it for your own specific purposes.

“”OpenCV Computer Vision Application Programming”” helps you get started with the library by first learning how to install OpenCV correctly on your system. You will then explore basic image processing concepts as well as the different interfaces that you can use in OpenCV. Develop techniques to separate foreground and background in your images, create stunning panoramas easily by stitching normal images together, enhance your photographs, calibrate your camera and automatically detect common objects like faces or people on your images. Reduce the distortion of your photographs and make straight lines of the scene look straight instead of bent in your images.

You will learn to change the perspective of your images so that it appears that you are moving around, similar to google street view navigation and develop a 3D representation of a scene using stereoscopic images.

On completion of this course, you will be able to mix and match the provided examples to build your own application

Course Curriculum

Getting Started with OpenCV

• Introduction to OpenCV (2:05)
• Installation on Linux (7:11)
• Installation on Windows (8:09)
• OpenCV Interfaces (2:39)

OpenCV Basics

• C Interface Basics (6:17)
• C++ Interface Basics (5:44)
• Python Interface Basics (3:33)
• Drawing Shapes (5:21)

Image Processing

• Blurring an Image (3:02)
• Understanding Image Morphology (5:44)
• Applying Geometric Transforms to an Image (2:58)
• Understanding Histograms (5:54)

Segmenting Images and Obtaining Interesting Points

• Clustering Data with k-means (6:06)
• Segmenting an Image using the Watershed algorithm (6:58)
• Segmenting an Image using the Grabcut algorithm (3:24)
• Finding and Matching Interesting Points (4:14)

Computational Photography

• Creating a Panorama (3:18)
• Removing Unwanted Objects (2:17)
• Enhancing Low Light Images (1:51)
• Working with HDR Images (3:10)

Recognizing Objects

• Detecting Shapes (3:28)
• Detecting Faces (5:13)
• Detecting People (3:43)
• Training your own detector (7:03)
• Recognizing faces (5:03)

Calibration and Stereo Images

• Calibrating the Camera (4:28)
• Undistorting an Image (2:22)
• Projecting an Image (3:18)
• Understanding Stereo Images (6:38)
• Generating a Depth Map (4:05)

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