Feature detectors identify interest points or keypoints in images that are distinctive and can be reliably detected under changes in illumination, scale, rotation, etc. The main components of feature detection and matching are detection of interest points, description of local appearance around each point, and matching of descriptors across images to identify similar features. Popular algorithms for identifying interest points include Harris corner detection, SIFT, SURF, FAST, and ORB. Feature detection and matching have applications in tasks like object tracking, stereo calibration, motion segmentation, recognition, and reconstruction.

1. Feature Detectors

2. • Feature
• keypoint feature

4. Main Component Of Feature Detection
And Matching
• Detection: Identify the Interest Point
• Description: The local appearance around each feature point
is described in some way that is (ideally) invariant under
changes in illumination, translation, scale, and in-plane rotation.
We typically end up with a descriptor vector for each feature
point.
• Matching: Descriptors are compared across the images, to
identify similar features. For two images we may get a set of
pairs (Xi, Yi) ↔ (Xi`, Yi`), where (Xi, Yi) is a feature in one
image and (Xi`, Yi`) its matching feature in the other image.

5. Interest Point
• nterest point is the point at which the direction of the boundary
of the object changes abruptly or intersection point between two
or more edge segments.

7. Properties Of Interest Point
•It has a well-defined position in image space or well
localized.
•It is stable under local and global perturbations in the
image domain as illumination/brightness variations, such
that the interest points can be reliably computed with a
high degree of repeatability.
•Should provide efficient detection.

8. Possible Approaches
•Based on the brightness of an image(Usually by image
derivative).
•Based on Boundary extraction(Usually by Edge detection
and Curvature analysis).

9. Algorithms for Identification
• Harris Corner
• SIFT(Scale Invariant Feature Transform)
• SURF(Speeded Up Robust Feature)
• FAST(Features from Accelerated Segment Test)
• ORB(Oriented FAST and Rotated BRIEF)

10. Application Of Feature Detection And
Matching
• Automate object tracking
• Point matching for computing disparity
• Stereo calibration(Estimation of the fundamental matrix)
• Motion-based segmentation
• Recognition
• 3D object reconstruction
• Robot navigation
• Image retrieval and indexing

11. Harris Corner Detector
• corner can be interpreted as the junction of two edges, where
an edge is a sudden change in image brightness.
• Corners are the important features in the image, and they are
generally termed as interest points which are invar

13. We have to maximize this function E(u,v) for corner
detection. That means, we have to maximize the second
term. Applying Taylor Expansion to the above equation
and using some mathematical steps, we get the final
equation as: