Howtosignalprocessing
- Why SURF is better than SIFT?
- What is SURF feature extraction?
- What is the difference between SIFT and SURF?
- What is image matching?
Why SURF is better than SIFT?
The SIFT algorithm performs better than SURF under blur and illumination changes. It also holds true for two different images where one image is being subjected to such property changes. The SURF will always perform faster than SIFT.
What is SURF feature extraction?
In computer vision, speeded up robust features (SURF) is a patented local feature detector and descriptor. It can be used for tasks such as object recognition, image registration, classification, or 3D reconstruction. It is partly inspired by the scale-invariant feature transform (SIFT) descriptor.
What is the difference between SIFT and SURF?
SIFT is an algorithm used to extract the features from the images. SURF is an efficient algorithm is same as SIFT performance and reduced in computational complexity. SIFT algorithm presents its ability in most of the situation but still its performance is slow.
What is image matching?
Image matching is an important concept in computer vision and object recognition. Images of the same item can be taken from any angle, with any lighting and scale. This as well as occlusion may cause problems for recognition. But ultimately, they still show the same item and should be categorized that way.
