Howtosignalprocessing
- What is SIFT feature extraction?
- How does SIFT algorithm work?
- What is the difference between SIFT and surf?
- What is descriptor explain SIFT descriptor in detail?
What is SIFT feature extraction?
Scale-invariant feature transform (SIFT) is a broadly adopted feature extraction method in image classification tasks. The feature is invariant to scale and orientation of images and robust to illumination fluctuations, noise, partial occlusion, and minor viewpoint changes in the images.
How does SIFT algorithm work?
SIFT helps locate the local features in an image, commonly known as the 'keypoints' of the image. These keypoints are scale & rotation invariant that can be used for various computer vision applications, like image matching, object detection, scene detection, etc.
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 descriptor explain SIFT descriptor in detail?
A SIFT descriptor is a 3-D spatial histogram of the image gradients in characterizing the appearance of a keypoint. The gradient at each pixel is regarded as a sample of a three-dimensional elementary feature vector, formed by the pixel location and the gradient orientation.
