Image Processing Key Point Detection Spatial Weight Correction
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Solution Overview
Problem
Existing image processing methods for key point detection in visual positioning, such as pupil positioning for augmented reality, face challenges with occlusions and interferences, leading to reduced computational efficiency and accuracy in tracking key points.
Innovation Solution
The method involves determining a feature map of an image, calculating a spatial position weight for pixel points, correcting the feature map based on this weight, and identifying key points within the corrected feature map. This process includes detecting initial key point positions, determining weights based on both initial positions and feature maps, and performing interpolation to refine the image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional key point detection methods are used, then the detection process is simple, but the accuracy is reduced due to occlusions and interferences
Solution Approach 1:
The image processing is divided into multiple stages: initial key point detection, occlusion detection, feature map generation, and coordinate transformation. Each stage handles specific aspects of the problem, allowing complex processing to be managed in modular steps that improve accuracy without overwhelming complexity
Solution Approach 2:
A feature map is introduced as an intermediary data structure that encodes spatial relationships and occlusion information. This feature map serves as a mediator between the raw image and the final key point coordinates, enabling accurate detection even when direct visualization is obscured
2Measurement precision
If feature map correction based on spatial position weight is performed, then the key point detection accuracy is improved, but the computational complexity increases
Solution Approach 1:
The spatial position weight is calculated locally for each pixel point based on its distance from the center of the eye region. This local computation approach focuses computational resources on critical areas (near the pupil) while using simpler calculations for peripheral pixels, improving spatial accuracy without uniformly increasing overall computational complexity
Solution Approach 2:
The feature map is corrected using spatial position weights before final key point detection. This preliminary correction prepares the data in advance, ensuring that subsequent detection operations work with already-optimized spatial information, thereby improving efficiency in the critical detection path
3Reliability
If occlusion handling is implemented, then the reliability of tracking is improved, but the processing time increases
Solution Approach 1:
The system performs self-verification by detecting occlusions and automatically adjusting the detection strategy. When occlusion is detected, the system switches to using the feature map and spatial position weights rather than direct key point detection, enabling reliable tracking without manual intervention while minimizing processing overhead through automated decision-making
Data Source
AI summary
A method with image processing includes: determining a feature map of an image; determining a spatial position weight of pixel points of the feature map; determining a corrected feature map by correcting the feature map based on the spatial position weight of the pixel points; and determining a key point based on the corrected feature map.