Sift algorithm steps

In general, SIFT algorithm can be decomposed into four steps:

1. Feature point (also called keypoint) detection.
2. Feature point localization.
3. Orientation assignment.
4. Feature descriptor generation.

1. How does SIFT algorithm work?
2. What is SIFT feature extraction?
3. How does SIFT achieve scale invariance?
4. How do you improve SIFT algorithm?
5. How do you make a SIFT contrast invariant?

How does SIFT algorithm work?

Scale-Invariant Feature Transform (SIFT)—SIFT is an algorithm in computer vision to detect and describe local features in images. It is a feature that is widely used in image processing. The processes of SIFT include Difference of Gaussians (DoG) Space Generation, Keypoints Detection, and Feature Description.

What is SIFT feature extraction?

SIFT is a feature extraction method that reduces the image content to a set of points used to detect similar patterns in other images. This algorithm is usually related to computer vision applications, including image matching and object detection.

How does SIFT achieve scale invariance?

The SIFT features are local and based on the appearance of the object at particular interest points, and are invariant to image scale and rotation. They are also robust to changes in illumination, noise, and minor changes in viewpoint.

How do you improve SIFT algorithm?

To improve SIFT feature matching algorithm efficiency, the method of reducing similar measure matching cost is mentioned. Euclidean distance is replaced by the linear-combination of city block distance and chessboard distance, and reduce character point in calculating with results of part feature.

How do you make a SIFT contrast invariant?

To obtain contrast invariance, the SIFT descriptor is normalized to unit sum. In this way, the weighted entries in the histogram will be invariant under local affine transformations of the image intensities around the interest point, which improves the robustness of the image descriptor under illumination variations.