Adaptive Feature Point Detection for Uniform Image Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing technologies face challenges in uniformly distributing feature points across images, leading to inconsistent detection sensitivity that can result in excessive or insufficient feature points, increasing the likelihood of noise detection and mismatch errors.
Innovation Solution
An image processing apparatus that analyzes the distribution of feature points across entire and divided image regions, sets target and expected point numbers for each region, and adjusts detection sensitivity based on comparison results to achieve uniform feature point distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If detection sensitivity for feature points is increased, then the number of feature points increases, but the possibility of detecting noise points increases
Solution Approach 1:
The patent divides the image into multiple regions and sets different detection sensitivities for each region based on local characteristics. Regions with high feature density use lower sensitivity to avoid noise, while regions with low feature density use higher sensitivity to ensure sufficient feature detection. This local adaptation resolves the contradiction by allowing high overall feature count while maintaining reliability in each specific region.
Solution Approach 2:
The patent dynamically adjusts detection sensitivity parameters based on the distribution characteristics of feature points in different image regions. By changing the sensitivity parameter according to local feature density, the system achieves both high feature point quantity and low noise detection rate, resolving the contradiction between quantity and reliability.
2Quantity of substance
If detection sensitivity for matching points is increased, then the number of matching points increases, but mismatch between feature points occurs
Solution Approach 1:
The patent applies different matching detection sensitivities to different image regions based on their characteristics. In regions prone to mismatch, lower sensitivity is used to ensure accuracy, while in regions with good distinguishability, higher sensitivity increases matching point quantity. This resolves the contradiction between quantity and precision of matching points.
3Device complexity
If constant detection sensitivity is used, then processing is simple, but matching points are not uniformly distributed in image regions
Solution Approach 1:
The patent transitions from static constant sensitivity to dynamic region-adaptive sensitivity. The system automatically adjusts detection sensitivity based on local feature distribution characteristics, achieving uniform matching point distribution across different image regions. The complexity increase is justified by the significant improvement in distribution uniformity and overall system performance.
4Manufacturing precision
If region-based adaptive sensitivity is implemented, then uniform feature point distribution is achieved, but processing complexity increases
Solution Approach 1:
The patent divides the image into multiple regions and processes each region independently with its own optimized sensitivity parameters. This segmentation approach achieves uniform feature point distribution across the entire image while managing complexity through modular regional processing. Each region can be processed with simpler local rules, and the overall system complexity remains manageable through this divide-and-conquer strategy.
Data Source
AI summary
An image analysis unit of an image processing apparatus acquires a distribution condition of feature points in an entire input image and in each of a plurality of small regions in the input image. A target point number setting unit sets a target point number for each small region based on the distribution condition of feature points in the entire input image. An expected point number setting unit sets an expected point number for each small region based on the distribution condition of feature points in the small region. A comparison unit compares the target point number and the expected point number. A sensitivity setting unit sets detection sensitivity based on the comparison result. A feature point detection unit performs feature point detection according to the detection sensitivity.