What are features, detectors, and keypoints? Features in an image are unique regions that the computer can easily tell apart. Corners are good examples of features. Finding these unique features is called feature detection. Once features are detected we need to find similar ones in a different image. This means we need to describe the features. Once you have the features and its description, you can find same features in all images and align them, stitch them or do whatever you want.
Harris corner detector is a good example of feature detector.
Keypoints are the same thing as points of interest. They are spatial locations, or points in the image that define what is interesting or what stand out in the image. The reason why keypoints are special is because no matter how the image changes... whether the image rotates, shrinks/expands, is translated, or distorted you should be able to find the same keypoints in this modified image when comparing with the original image.
Usually when we want to detect keypoints, we just take a look at the locations. However, if you want to match keypoints between images, then you definitely need the scale and the orientation to facilitate this.
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