Dynamic Feature Point Distribution for Image Stabilization and Motion Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing technologies fail to dynamically adjust the distribution of feature points for tracking according to specific image capturing status, leading to suboptimal performance in applications like image stabilization and motion detection.
Innovation Solution
An image processing device that divides images into regions, extracts feature points, tracks motion vectors, estimates the priority of feature points, and adjusts their distribution based on the image capturing status to ensure optimal tracking for either background or object focus.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If feature points are uniformly distributed across the screen, then image stabilization and three-dimensional restoration performance is improved, but motion detection of object performance deteriorates
Solution Approach 1:
The patent implements dynamic adjustment of feature point distribution based on the imaging status. The determining unit identifies whether the imaging status corresponds to image stabilization or object motion detection, and the setting unit dynamically reconfigures feature point distribution accordingly - uniform distribution for stabilization, object-concentrated distribution for motion detection. This dynamic adaptation resolves the contradiction by making the system flexible rather than static.
Solution Approach 2:
The patent changes the distribution parameter of feature points based on the imaging status. When image stabilization is detected, feature points are distributed uniformly across the screen. When object motion detection is detected, feature points are redistributed to concentrate around the object. This parameter change allows the system to optimize for different functional requirements.
2Measurement precision
If feature points are concentrated at the periphery of the object, then motion detection of object performance is improved, but image stabilization and three-dimensional restoration performance deteriorates
Solution Approach 1:
The system dynamically switches between concentrated and uniform feature point distribution based on the determined imaging status. The determining unit identifies the imaging purpose, and the setting unit adjusts the distribution pattern in real-time, enabling the system to optimize for motion detection when needed while maintaining stability performance when required.
Solution Approach 2:
The distribution parameter of feature points is changed based on imaging status detection. For object motion detection, feature points are concentrated around the object to improve measurement precision. For image stabilization, they are uniformly distributed to maintain reliability. This parameter adaptation resolves the performance trade-off.
3Ease of manufacture
If feature point distribution is fixed according to a certain rule, then implementation simplicity is improved, but adaptability to different image capturing status deteriorates
Solution Approach 1:
The patent transforms the static feature point distribution into a dynamic system that automatically adapts to different imaging statuses. The determining unit detects the imaging status (image stabilization or object motion detection), and the setting unit adjusts the distribution pattern accordingly. This dynamic approach maintains implementation simplicity through automated detection and adjustment while achieving high adaptability to different capturing scenarios.
Solution Approach 2:
The system performs self-adjustment of feature point distribution based on automatic detection of imaging status. The determining unit autonomously identifies the imaging purpose, and the setting unit autonomously configures the appropriate distribution pattern without requiring manual intervention. This self-service mechanism achieves both simplicity and adaptability.
Data Source
AI summary
A circuitry of an image processing device divides a first image into a plurality of regions, extracts a feature point from each of the regions, tracks the feature point among a plurality of images to detect a motion vector, estimates a notable target of the first image, calculates the priority level of setting of a tracking feature point for each of the regions for tracking motion of the notable target, and sets the tracking feature point to any of the regions based on the priority level.