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OpenCV with Python By Example

OpenCV with Python By Example

By : Prateek Joshi
3.5 (10)
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OpenCV with Python By Example

OpenCV with Python By Example

3.5 (10)
By: Prateek Joshi

Overview of this book

Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we are getting more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Web developers can develop complex applications without having to reinvent the wheel. This book will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off with applying geometric transformations to images. We then discuss affine and projective transformations and see how we can use them to apply cool geometric effects to photos. We will then cover techniques used for object recognition, 3D reconstruction, stereo imaging, and other computer vision applications. This book will also provide clear examples written in Python to build OpenCV applications. The book starts off with simple beginner’s level tasks such as basic processing and handling images, image mapping, and detecting images. It also covers popular OpenCV libraries with the help of examples. The book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation.
Table of Contents (14 chapters)
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13
Index

Scale Invariant Feature Transform (SIFT)


Even though corner features are "interesting", they are not good enough to characterize the truly interesting parts. When we talk about image content analysis, we want the image signature to be invariant to things such as scale, rotation, illumination, and so on. Humans are very good at these things. Even if I show you an image of an apple upside down that's dimmed, you will still recognize it. If I show you a really enlarged version of that image, you will still recognize it. We want our image recognition systems to be able to do the same.

Let's consider the corner features. If you enlarge an image, a corner might stop being a corner as shown below.

In the second case, the detector will not pick up this corner. And, since it was picked up in the original image, the second image will not be matched with the first one. It's basically the same image, but the corner features based method will totally miss it. This means that corner detector is not exactly...

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OpenCV with Python By Example
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