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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

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Showing 161–170 of 339 papers

TitleStatusHype
SHIC: Shape-Image Correspondences with no Keypoint Supervision—0
RADA: Robust and Accurate Feature Learning with Domain Adaptation—0
A Self-Supervised Method for Body Part Segmentation and Keypoint Detection of Rat Images—0
SkelFormer: Markerless 3D Pose and Shape Estimation using Skeletal Transformers—0
Self-supervised 3D Patient Modeling with Multi-modal Attentive Fusion—0
A Self-supervised Pressure Map human keypoint Detection Approch: Optimizing Generalization and Computational Efficiency Across Datasets—0
3D Kinematics Estimation from Video with a Biomechanical Model and Synthetic Training Data—0
To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detectionCode0
SD2Event:Self-supervised Learning of Dynamic Detectors and Contextual Descriptors for Event Cameras—0
FC-GNN: Recovering Reliable and Accurate Correspondences from InterferencesCode0
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