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Oriented FAST and Rotated BRIEF (ORB) is utilized in real-time video analysis, panorama stitching, and object recognition. It is a fast feature detector and descriptor that is rotation invariant and resistant to noise.

Harris Corner Detector finds corners by detecting the change in intensity in all directions. It is used in image processing to identify features for tracking and 3D reconstruction.

Scale-Invariant Feature Transform (SIFT) is used for object recognition, robotic mapping, and image stitching. It is robust to changes in scale, rotation, and lighting.

Fast Retina Keypoint (FREAK) is inspired by the human retina, used for pattern recognition and machine vision. It produces binary strings as a descriptor and is efficient for computation on devices with limited resources.

Accelerated-KAZE (AKAZE) is used for image matching and object detection. It features a fast detection of keypoints and robustness to deformations and noise, often used in computer vision tasks.

Shi-Tomasi Corner Detector is an improvement over Harris, identifying corners by a specific minimum eigenvalue. It's widely used for tracking and motion analysis.

Binary Robust Independent Elementary Features (BRIEF) provides a shortcut to find binary descriptors. It is useful in image retrieval and matching for applications like visual search.

Features from Accelerated Segment Test (FAST) is used for corner detection in real-time systems. It identifies pixels that stand out when considering a circle of neighboring pixels.

Speeded Up Robust Features (SURF) is used for real-time applications, 3D reconstruction, and tracking. It approximates LoG with Box Filters and is faster than SIFT.