Camera Depth of Field Measurement with Automated Collimator Testing
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Solution Overview
Problem
Existing methods for measuring depth of field in cameras are manual, time-consuming, prone to human error, and do not account for sensor size variations and diffraction effects, leading to inconsistent and inaccurate measurements.
Innovation Solution
A system and method using a collimator setup with a housing, image sharpness measurement module, lens parameter module, and automation processing module to automatically measure depth of field by capturing images at minimum and maximum focus lengths, checking sharpness with modulation transfer function, and comparing recorded values against predefined parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual measurement methods are used with skilled technicians adjusting camera settings and taking test shots, then measurement flexibility and adaptability are maintained, but measurement time increases significantly and human error risk increases
Solution Approach 1:
The patent replaces manual mechanical adjustment of camera settings with an automated computer-controlled system. The automation processing module automatically adjusts aperture, focal length, and object distance parameters, eliminating manual intervention while maintaining measurement precision. This substitution of mechanical/manual operations with automated control systems directly reduces measurement time while preserving accuracy.
Solution Approach 2:
The measurement system performs self-calibration and self-measurement through the automation processing module. The system automatically captures test shots, processes images to calculate depth of field, and compares results against reference values without requiring skilled technicians. This self-service capability eliminates human error while maintaining measurement flexibility across different camera models.
2Adaptability or versatility
If manual measurement processes are used, then adaptability to different camera models can be maintained through skilled operator judgment, but measurement consistency and reliability decrease due to human error
Solution Approach 1:
The system automatically adapts to different camera models by adjusting measurement parameters such as aperture values, focal lengths, and object distances based on camera-specific characteristics stored in a database. The automation processing module selects appropriate parameters for each camera model, ensuring consistent and reliable measurements across diverse camera types without requiring manual recalibration by technicians.
Solution Approach 2:
The measurement system incorporates feedback mechanisms where measured depth of field values are compared against reference values from previously measured cameras with known performance. The system uses this feedback to validate measurements and ensure consistency across different camera models, automatically identifying and correcting deviations while maintaining adaptability to various camera specifications.
3Ease of manufacture
If existing measurement systems are used that do not account for sensor size variations, then measurement simplicity is maintained, but measurement accuracy decreases due to unaccounted sensor size effects
Solution Approach 1:
The measurement system incorporates sensor size as a specific local parameter that varies between camera models. The automation processing module retrieves sensor size information for each camera and uses it to adjust depth of field calculations accordingly. This localized consideration of sensor size effects maintains measurement system simplicity while significantly improving accuracy by accounting for previously neglected variations.
4Device complexity
If diffraction effects are not considered in measurements, then measurement process complexity is reduced, but measurement accuracy deteriorates due to unaccounted diffraction on image sharpness
Solution Approach 1:
The system performs preliminary calculations to determine optimal aperture settings that minimize diffraction effects before conducting the actual depth of field measurement. The automation processing module calculates the optimal aperture based on focal length and sensor size, then configures the camera accordingly. This preliminary action accounts for diffraction effects in advance, improving measurement accuracy without significantly increasing overall process complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Ensures accurate and efficient measurement of depth of field, ensuring camera lenses meet performance specifications, reducing human error and inconsistency, and enabling high-quality imaging products.
Implementation Method 1
The collimators arrangement is fixed, and configured such that simulating the images are of a minimum focus length and a maximum focus length. The camera is positioned beneath the collimators arrangement in order to capture the images projected by the collimators arrangement.
Implementation Method 2
The camera is adapted to check image sharpness using a modulation transfer function.
Implementation Method 3
The camera is positioned beneath the collimators arrangement in order to capture the images projected by the collimators arrangement.
Data Source
AI summary
A system for measuring depth of field for a camera is disclosed. The system includes a housing, an image sharpness measurement module, a lens parameter measurement module, and a processing subsystem. The housing includes a collimators arrangement arranged at different focus distances and a camera beneath collimators arrangement in order to capture the images projected by the collimators arrangement. The lens parameter measurement measures a plurality of lens parameters. The automation processing module receives a file including a plurality of first parameters and a plurality of second parameters, receives and reads a camera product code as an input from a user, loads a plurality of optical parameters from the file, records one or more measured values from the camera by the collimator, compares the recorded values with a corresponding predefined values from the file, and automatically decide the acceptance and non-acceptance of the camera product code.