Stereoscopic Image Correction Using Feature Point Maldistribution Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing stereoscopic image generation methods face challenges in accurately correcting image positions when feature points are maldistributed, leading to inadequate correction parameters and increased calculation requirements, especially in areas with low feature point density, such as uniform objects like walls and skies.
Innovation Solution
A stereoscopic image generating device that extracts feature points from images, calculates correction parameters, assesses correction errors, determines the maldistribution degree of feature points, sets a threshold for correction errors, and discards inadequate parameters to ensure accurate stereoscopic image generation by correcting image positions using adequate parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feature points are extracted from images to calculate correction parameters, then image position correction can be performed, but when feature points are maldistributed (especially in low-density areas), the correction parameters become inadequate and calculation efficiency decreases
Solution Approach 1:
The patent performs preliminary assessment of correction parameter adequacy by calculating correction errors for each feature point pair and comparing against thresholds before finalizing the correction process. This preliminary check prevents wasting computational resources on inadequate parameter sets, thereby improving calculation efficiency while maintaining correction accuracy
Solution Approach 2:
The system implements feedback mechanisms by evaluating correction errors and maldistribution degrees, then using this information to determine whether to accept or reject calculated correction parameters. This feedback loop ensures that only adequate parameters are used for final correction, resolving the contradiction between accuracy and efficiency
2Measurement precision
If manual calibration process is performed by user adjusting object positions, then correction parameters can be obtained, but the process becomes inconvenient and difficult especially for beginners
Solution Approach 1:
The system performs automatic calibration by extracting feature points, calculating correction parameters, and assessing their adequacy without requiring manual user intervention. The automated process includes evaluating maldistribution degrees and making decisions about parameter acceptance, making the system easy to operate while maintaining high correction accuracy
Solution Approach 2:
The patent replaces manual mechanical adjustment operations with automated computational processes. Instead of users physically adjusting object positions, the system uses algorithms to extract feature points, calculate transformation parameters, and perform corrections automatically, significantly improving ease of operation
3Area of stationary object
If feature points are extracted in areas with low density (such as uniform objects), then more comprehensive coverage is achieved, but it becomes difficult to extract sufficient feature points leading to association errors
Solution Approach 1:
The system dynamically adjusts evaluation parameters such as correction error thresholds based on the maldistribution degree of feature points. In areas with low feature point density, the system modifies its assessment criteria to appropriately evaluate the adequacy of correction parameters, enabling accurate correction even when feature points are sparse or difficult to extract
Data Source
AI summary
A stereoscopic image generating device includes: a correction parameter calculating unit that calculates correction parameters based on a plurality of pairs of feature points corresponding to the same points on the object, from the first image and a second image photographing the object; a correction error calculating unit that, for each pair of feature points, corrects the position of the feature point on at least one image, using the correction parameters, and calculates the amount of correction error; a maldistribution degree calculating unit that finds the degree of maldistribution of feature points; a threshold value determining unit that determines a threshold value such that the threshold value is smaller when the degree of maldistribution increases; and a correction unit that, when the amount of correction error is equal to or lower than the threshold value, corrects the position of the object in the images using the correction parameters.