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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 191–200 of 339 papers

TitleStatusHype
A Fast Keypoint Based Hybrid Method for Copy Move Forgery Detection—0
A Keypoint Detection and Description Network Based on the Vessel Structure for Multi-Modal Retinal Image Registration—0
A Low Rank Promoting Prior for Unsupervised Contrastive Learning—0
An analysis of the factors affecting keypoint stability in scale-space—0
A Novel Streamline-based diffusion MRI Tractography Registration Method with Probabilistic Keypoint Detection—0
A Self-Supervised Method for Body Part Segmentation and Keypoint Detection of Rat Images—0
A Self-supervised Pressure Map human keypoint Detection Approch: Optimizing Generalization and Computational Efficiency Across Datasets—0
A Unified Sequence Interface for Vision Tasks—0
Automatic Temporal Segmentation for Post-Stroke Rehabilitation: A Keypoint Detection and Temporal Segmentation Approach for Small Datasets—0
BALF: Simple and Efficient Blur Aware Local Feature Detector—0
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