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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
Computer-Aided Automated Detection of Gene-Controlled Social Actions of Drosophila—0
Conditional Link Prediction of Category-Implicit Keypoint Detection—0
Corn Ear Detection and Orientation Estimation Using Deep Learning—0
Cross-Domain 3D Hand Pose Estimation With Dual Modalities—0
D4: Text-guided diffusion model-based domain adaptive data augmentation for vineyard shoot detection—0
Deep Learning-Based Automatic Diagnosis System for Developmental Dysplasia of the Hip—0
Deep reinforcement learning to detect brain lesions on MRI: a proof-of-concept application of reinforcement learning to medical images—0
Dense Keypoints via Multiview Supervision—0
Differentiable Hierarchical Graph Grouping for Multi-Person Pose Estimation—0
Disguised Face Identification (DFI) with Facial KeyPoints using Spatial Fusion Convolutional Network—0
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