Tooth Color Matching Using CNN Calibration Across Lighting Angles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for determining tooth color in real-world conditions suffer from inaccuracies due to deviations in lighting and angles, which are not reproducible, leading to a loss of laboratory accuracy.
Innovation Solution
An iterative learning algorithm using a high-quality imaging device captures images of sample teeth under various lighting conditions and angles, creating a database for a CNN to learn and assign corresponding colors, allowing for accurate tooth color determination on-site with a smartphone or professional camera.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a color selection object with known tooth shade is used for calibration, then color determination accuracy is improved in controlled conditions, but accuracy deteriorates in real-world situations with varying lighting and angles
Solution Approach 1:
The system performs preliminary capture and evaluation of images of sample teeth under different lighting conditions and shooting angles before actual use. This preparatory training phase creates a comprehensive database that enables the CNN to adapt to various real-world conditions, resolving the contradiction between calibration accuracy and adaptability to varying environments.
Solution Approach 2:
The system changes multiple parameters simultaneously including lighting conditions (different light sources, brightness levels), shooting angles (5 to 15 different angles in vertical and horizontal directions), and camera types. This creates 100 to 300 different recording situations for each sample tooth, enabling the model to generalize across diverse conditions while maintaining accuracy.
2Measurement precision
If images are captured under multiple lighting conditions and angles in a comprehensive database, then recognition accuracy is improved, but data processing complexity and time increase
Solution Approach 1:
The system performs the computationally intensive image capture and evaluation under multiple conditions during a preliminary training phase. This database is created once and then used for rapid inference in practical applications, shifting the time investment from the usage phase to the preparation phase, thereby resolving the time consumption contradiction.
Solution Approach 2:
The system creates a comprehensive digital copy of sample teeth under various conditions and stores this database in a cloud or accessible storage. This pre-created database can be accessed and reused multiple times without re-capturing images, significantly reducing processing time for subsequent tooth color determinations while maintaining high recognition accuracy.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method for determining tooth color is provided, wherein an evaluation device comprises an iterative learning algorithm using CNNs. In a preparatory step, the algorithm captures and evaluates images under different lighting conditions and angles based on at least one previously known, optionally virtually generated in RGB space, tooth color reference, and learns to match the corresponding tooth color to the relevant reference. In an evaluation step, a recording device is provided with which an image of an auxiliary body with a previously known tooth color is captured together with at least one tooth. The recording device captures at least two images of the combination of the tooth to be determined and the auxiliary body from different angles and transmits them to the evaluation device.Based on the learned assignment to the appropriate sample tooth color, the evaluation device analyzes the captured images and outputs the tooth color of the tooth to be determined according to a reference value, such as a common tooth key, e.g. A1, B2, etc.