Image Processing Apparatus for Optimal Viewpoint Guidance in 3D Deviation Estimation
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Solution Overview
Problem
In manufacturing diagnosis and product inspection, accurately estimating the deviation between a product's shape and CAD data is challenging, especially when the user is not familiar with the technique, as existing methods fail to provide an appropriate viewpoint for photographing, leading to reduced restoration accuracy and increased user workload.
Innovation Solution
An image processing method that detects feature lines from images taken at different viewpoints, calculates evaluation values for candidate photographing positions, and determines optimal positions for restoring three-dimensional line segments, allowing for accurate deviation estimation and reduced user effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing methods are used for shape restoration without providing appropriate viewpoint guidance, then the user workload increases and the operation becomes difficult, but the system complexity remains low
Solution Approach 1:
The system performs preliminary actions by pre-calculating and providing viewpoint guidance information before the user actually photographs the object. The viewpoint guidance generation unit determines appropriate photographing positions and provides guidance information in advance, so users don't need to understand complex technical concepts about optimal viewpoints. This preliminary preparation reduces user workload while maintaining reasonable system complexity.
Solution Approach 2:
The viewpoint guidance information acts as an intermediary between the complex 3D restoration process and the user. Instead of requiring users to directly understand and execute complex restoration algorithms, the system provides simplified guidance information (such as recommended viewpoints) that mediates the interaction. This intermediary layer reduces the operational difficulty without requiring proportional increases in system complexity.
2Measurement precision
If multiple viewpoint photographs are required for accurate 3D restoration, then the measurement precision improves, but the time required for the process increases
Solution Approach 1:
The system performs preliminary calculation to determine the minimum number of viewpoints needed and provides guidance information about optimal photographing positions before the user takes photos. This allows users to efficiently capture images at the right viewpoints without unnecessary repetitions, improving restoration accuracy while minimizing the time spent on photographing.
Solution Approach 2:
The system dynamically adjusts parameters such as the number of required viewpoints and the specific viewpoint positions based on the object's characteristics and the restoration requirements. By changing these parameters optimally, the system achieves high restoration accuracy without requiring excessive numbers of photographs, thus reducing the time loss.
3Ease of operation
If the system provides detailed viewpoint guidance and evaluation for multiple candidate positions, then the ease of operation improves, but the calculation processing time and system complexity increase
Solution Approach 1:
The system applies partial action by providing viewpoint guidance for only the most critical candidate positions rather than exhaustively analyzing all possible viewpoints. The viewpoint guidance generation unit evaluates multiple candidate positions but provides detailed guidance only for the top candidates, which is sufficient to help users achieve good restoration results without requiring excessive calculation time.
4Measurement precision
If the system automatically determines optimal photographing positions, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The viewpoint guidance information serves as an intermediary that bridges the gap between complex automatic determination algorithms and simple user interaction. The system automatically determines optimal positions using complex calculations, but presents this information in a simplified guidance format that users can easily follow. This intermediary approach achieves high measurement precision without requiring the entire system to be overly complex from the user's perspective.
Data Source
AI summary
An image processing method includes: detecting a plurality of feature lines from a first image captured from a first position; specifying, based on a positional relationship between the plurality of feature lines and a plurality of projection lines generated by projecting each of a plurality of line segments onto the first image, a feature line representing a defective portion of a shape of an object; setting a plurality of candidate positions based on the first position, each of the plurality of candidate positions being a candidate for a second position at which a second image is captured; calculating an evaluation value of each of the plurality of candidate positions; determining any of the plurality of candidate positions based on the evaluation value of each of the plurality of candidate positions; and outputting first information to be used in recognition of the determined candidate position.