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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 211–220 of 339 papers

TitleStatusHype
NeMo: Learning 3D Neural Motion Fields From Multiple Video Instances of the Same Action—0
OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models—0
On Mutual Information in Contrastive Learning for Visual Representations—0
On the Visibility of Point Clouds—0
Orthogonal Decomposition Network for Pixel-Wise Binary Classification—0
Parallel Multi-Scale Networks with Deep Supervision for Hand Keypoint Detection—0
PaRK-Detect: Towards Efficient Multi-Task Satellite Imagery Road Extraction via Patch-Wise Keypoints Detection—0
Periodontal Bone Loss Analysis via Keypoint Detection With Heuristic Post-Processing—0
Processing and Segmentation of Human Teeth from 2D Images using Weakly Supervised Learning—0
RADA: Robust and Accurate Feature Learning with Domain Adaptation—0
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