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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 321330 of 339 papers

TitleStatusHype
Towards Deep Learning based Hand Keypoints Detection for Rapid Sequential Movements from RGB Images0
Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images0
Translating a Visual LEGO Manual to a Machine-Executable Plan0
UKDM: Underwater keypoint detection and matching using underwater image enhancement techniques0
UMDFaces: An Annotated Face Dataset for Training Deep Networks0
Unconstrained Face Recognition using ASURF and Cloud-Forest Classifier optimized with VLAD0
Unsupervised Deep Learning-based Keypoint Localization Estimating Descriptor Matching Performance0
Unsupervised Model Diagnosis0
Unsupervised training of keypoint-agnostic descriptors for flexible retinal image registration0
Unsupervised Visual Attention and Invariance for Reinforcement Learning0
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