Neural Network Imaging Time Estimation for Faded Photographs
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Solution Overview
Problem
Existing technologies cannot accurately determine the imaging time of photographs without an imprinted date, as the relationship between the photograph's features and imaging time is not clearly defined.
Innovation Solution
A machine learning apparatus that uses a neural network to estimate the imaging time based on photographic data, associating the degree of fading with the imaging time, even if the date is not explicitly imprinted, by training on data where dates are known and using the degree of fading as a hidden layer to output the imaging time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to determine imaging time, then imaging time can be accurately specified when an imprint is present, but imaging time cannot be determined for photographs without an imprinted date
Solution Approach 1:
The patent transforms the approach from directly reading imprinted dates to analyzing multiple photographic parameters (color information, luminance distribution, texture patterns, composition features) and using machine learning to infer imaging time from these changed parameters. This allows the system to determine imaging times for both imprinted and non-imprinted photographs.
Solution Approach 2:
The patent introduces machine learning models as an intermediary between the photograph's visual features and the imaging time determination. The learning unit processes photographic data items and outputs estimated imaging times, serving as a mediator that bridges the gap between observable photograph characteristics and the target imaging time information.
2Adaptability or versatility
If machine learning is used to estimate imaging time, then imaging time can be estimated for photographs without imprints, but the relationship between photograph features and imaging time is not clearly defined
Solution Approach 1:
The patent performs preliminary actions by collecting and preparing training data in advance, where photographs with known imaging times (from imprints) are paired with their corresponding photographic data items. This pre-training process establishes the feature-time relationships that the machine learning model will later use to estimate imaging times for photographs without imprints.
Solution Approach 2:
The patent implements feedback mechanisms where the machine learning model's estimates are continuously refined based on comparison with actual imaging times from imprinted photographs. The learning unit adjusts its internal parameters to minimize the difference between estimated and actual imaging times, improving accuracy over time.
3Measurement precision
If manual inspection of photograph features is used, then human judgment can identify some temporal characteristics, but the process is time-consuming and less accurate
Solution Approach 1:
The patent replaces the mechanical system of manual human inspection with an automated machine learning-based system. The learning unit automatically processes photographic data items, extracts features, and estimates imaging times without human intervention, significantly reducing processing time while maintaining or improving accuracy through consistent application of learned patterns.
Data Source
AI summary
A machine learning apparatus includes a learning target obtaining unit that obtains a plurality of photographic data items of scanned photographs and a learning unit that learns imaging times of the photographs in which imaging dates are not imprinted based on the photographic data items.