Feature detection (SIFT, SURF, ORB) – OpenCV 3.4 with python part 2

#pyresearch

We’re going to learn in this tutorial how to find features on an image.
We have two different algorithms that we can use:

1 - SIFT
2 - SURF

Goal
In this video,

We will see the basics of SURF
We will see SURF functionalities in OpenCV
Theory
In the last chapter, we saw SIFT for keypoint detection and description. But it was comparatively slow and people needed a more speeded-up version. In 2006, three people, Bay, H., Tuytelaars, T. and Van Gool, L, published another paper, "SURF: Speeded Up Robust Features" which introduced a new algorithm called SURF. As the name suggests, it is a speeded-up version of SIFT.

SIFT is an algorithm used to extract the features from the images. SURF is an efficient algorithm that is the same as SIFT performance and reduced computational complexity. SIFT algorithm presents its ability in most situations but still, its performance is slow. SURF algorithm is the same as SIFT with the fastest one and good performance.

Image Matching Using SIFT, SURF, BRIEF, and ORB: Performance Comparison for Distorted Images.

1 - Feature extraction and description using Fast Hessian and SURF
2 - Feature extraction and description using DOG and SIFT

#Pyresearch #SIFT #SURF #python #python3 #pythonprogramming #opencv #opencvpython #computervision #opencvpython

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