Introduction
Scale-Invariant Feature Transform is a feature detection, matching and description algorithm used for various Computer Vision tasks, such as object recognition, robotic mapping, image stiching, etc.
This lab will take interest in the object recognition task. The goal will be to match interest points between a natural image pair. A natural pair is a set of two pictures of the same object taken separately. This means that the second image is not a simple geometrical transformation of the original image (translation, rotation, etc.).
Using such a matching allows to classify images by comparing it to an already labeled database and attributing it the label of the closest images found in said database.
We will be using MatLab and following this lab’ statement.
Part I: Sparse features for matching specific objects in images
Stage A: SIFT Features Descriptor
Firstly, we are going to visualize the SIFT feature detections for two different images of the same object (just below). The second image is the same as the first but was rescaled and rotated. A SIFT frame is a circle with an orientation and is specified by four parameters: the center tx, ty, the scale s, and the rotation θ (in radians), resulting in a vector of four parameters (s, θ, tx, ty).