Ocular Measurement Using Consumer Sensor and Reference Object
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for determining pupillary distance, mono-pupillary distance, and heights for corrective lenses are inaccurate and require expert expertise, complex equipment, or high-quality cameras, making them impractical for precise measurement, especially with consumer-grade cameras and poor image quality.
Innovation Solution
A method using a consumer-type digital image sensor to acquire a single image of the user's head with an object of known size, allowing for accurate measurement of ocular distances without expert intervention, using calibration parameters that can be unknown or poorly defined, and employing interactive positioning aids and depth mapping techniques to ensure precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard methods are used to determine pupillary distance and heights, then measurement can be obtained, but measurement precision is insufficient for precise lens manufacture
Solution Approach 1:
A flat object of known size is introduced as an intermediary reference element in the image scene. This mediator provides a scalable reference that enables accurate measurement of ocular features without requiring complex calibrated equipment. The known dimensions of this intermediate object serve as a basis for calculating real-world distances between pupils and other facial features.
Solution Approach 2:
The method uses a 2D image capture to create a digital copy of the user's facial features and the reference object. By analyzing the relative positions and sizes of features in this image copy, the system can determine ocular measurements without physical contact or specialized measurement devices. The image serves as a reproducible record that can be measured precisely through image processing.
2Measurement precision
If expert assistance and complex equipment are used, then measurement precision improves, but ease of operation deteriorates
Solution Approach 1:
The system enables users to perform their own ocular measurements without requiring expert operators. By combining automated image capture with computer vision algorithms that automatically detect facial features and calculate measurements, the method transforms a previously expert-dependent process into a self-service operation. Users simply position themselves with the reference object while the system handles all measurement computations automatically.
Solution Approach 2:
The method changes the operational parameters from requiring specialized knowledge and manual measurement techniques to using automated image processing parameters. By relying on algorithmic feature detection and geometric calculations rather than human expertise, the system maintains high measurement precision while dramatically improving ease of operation. The transformation from manual to automated parameter-based measurement is key to resolving this contradiction.
3Measurement precision
If high-quality cameras are used, then measurement precision improves, but device complexity and cost increase
Solution Approach 1:
The method replaces expensive, high-precision camera systems with inexpensive consumer-grade cameras or even smartphone cameras. By introducing a reference object of known size into the scene, the system compensates for the lower inherent precision of cheap cameras through computational geometry. This approach treats the camera as a disposable or low-cost component whose limitations are overcome by the mathematical reference framework rather than requiring expensive hardware upgrades.
Solution Approach 2:
The system changes the measurement approach from relying on camera hardware precision to relying on computational parameters derived from image analysis. By using the known dimensions of the reference object as a scaling parameter, the method transforms the measurement problem into a geometric calculation that is independent of camera quality. This parameter-based approach allows accurate measurements to be obtained from low-resolution or compressed images that would be unusable with traditional methods.
4Measurement precision
If multiple images are required for accurate measurement, then measurement precision improves, but loss of time increases
Solution Approach 1:
The reference object is pre-positioned in the scene before image capture, establishing a known spatial relationship that enables single-image measurements. By preparing the reference framework in advance and incorporating it into the capture scene, the system eliminates the need for multiple sequential images or complex temporal processing. The preliminary placement of the reference object creates all necessary geometric information in a single snapshot, resolving the time-precision tradeoff.
Data Source
Figure 1~2
Figure 3
AI summary
The invention relates to a method for determining at least one ocular measurement (pupillary distance, mono pupillary distance and/or heights) of a user, using a consumer-type digital image sensor. The method uses at least one image of the user's head, acquired by the image sensor and containing an object of known size. The calibration parameters of the camera are unknown or known with little precision.