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Keypoint Detection

Keypoint Detection is essential for analyzing and interpreting images in computer vision. It involves simultaneously detecting and localizing interesting points in an image. Keypoints, also known as interest points, are spatial locations or points in the image that define what is interesting or what stands out. They are invariant to image rotation, shrinkage, translation, distortion, etc. Keypoints examples are body joints, facial landmarks, or any other salient points in objects. Keypoints have uses in problems such as pose estimation, object detection and tracking, facial analysis, and augmented reality.

( Image credit: PifPaf: Composite Fields for Human Pose Estimation; "Learning to surf" by fotologic, license: CC-BY-2.0 )

Papers

Showing 131–140 of 339 papers

TitleStatusHype
Stereophotoclinometry Revisited—0
SuperEvent: Cross-Modal Learning of Event-based Keypoint Detection—0
Multiscale Feature Importance-based Bit Allocation for End-to-End Feature Coding for Machines—0
Keypoint Detection and Description for Raw Bayer Images—0
Spatial regularisation for improved accuracy and interpretability in keypoint-based registrationCode0
Periodontal Bone Loss Analysis via Keypoint Detection With Heuristic Post-Processing—0
A Novel Streamline-based diffusion MRI Tractography Registration Method with Probabilistic Keypoint Detection—0
CNSv2: Probabilistic Correspondence Encoded Neural Image Servo—0
Automatic Temporal Segmentation for Post-Stroke Rehabilitation: A Keypoint Detection and Temporal Segmentation Approach for Small Datasets—0
Rewards-based image analysis in microscopy—0
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