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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 1120 of 339 papers

TitleStatusHype
RDD: Robust Feature Detector and Descriptor using Deformable Transformer0
Enabling Privacy-Aware AI-Based Ergonomic Analysis0
My Emotion on your face: The use of Facial Keypoint Detection to preserve Emotions in Latent Space Editing0
Unsupervised training of keypoint-agnostic descriptors for flexible retinal image registration0
Unsupervised Deep Learning-based Keypoint Localization Estimating Descriptor Matching Performance0
Learning a General Model: Folding Clothing with Topological Dynamics0
EdgePoint2: Compact Descriptors for Superior Efficiency and Accuracy0
UKDM: Underwater keypoint detection and matching using underwater image enhancement techniques0
Stereophotoclinometry Revisited0
SuperEvent: Cross-Modal Learning of Event-based Keypoint Detection0
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