Method for determining the cooking end time of food, and household cooking appliance
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Solution Overview
Problem
Existing cooking appliances face challenges in reliably determining the cooking end time with minimal computational effort, especially when the food has a similar color to its surroundings, such as light brown food and baking parchment, making it difficult to accurately compare the actual degree of browning to a target degree.
Innovation Solution
A method involving the creation of a lightness value-separable image of the cooking chamber, using cluster analysis for segmentation to distinguish food pixels from surrounding pixels, allowing for the comparison of actual browning to a target browning level during the cooking process, and adjusting cooking time accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed threshold values for color coordinates are used for segmentation, then the segmentation process is simple and fast, but the distinction between food pixels and surrounding pixels is poor when colors are similar
Solution Approach 1:
The patent transforms the image from RGB color space to L*a*b* color space, where the lightness coordinate L* is separated from chromatic coordinates a* and b*. This parameter transformation allows the segmentation to use lightness value as the primary discrimination criterion, which significantly improves the ability to distinguish food from surrounding objects even when their chromatic colors are similar. The lightness coordinate provides a unified scale for brightness comparison that is independent of color hue.
Solution Approach 2:
The patent applies segmentation based on lightness value thresholds in the L*a*b* color space to separate food pixels from surrounding pixels. By dividing the image into different lightness regions, the method achieves reliable food identification without requiring complex computational algorithms, thus maintaining processing efficiency while improving accuracy.
2Measurement precision
If cluster analysis is used for segmentation, then the distinction between food pixels and surrounding pixels is significantly improved, but the computational effort increases
Solution Approach 1:
The patent reduces computational complexity by transforming the segmentation problem from a multi-dimensional color space clustering task to a one-dimensional lightness value thresholding task. By using only the L* coordinate from L*a*b* color space, the method achieves effective food-surrounding separation with simple threshold comparisons, avoiding the need for computationally intensive cluster analysis algorithms while maintaining high segmentation accuracy.
3Productivity
If the actual degree of browning is compared with target degree using simple methods, then the computational effort is low, but the determination of cooking end time is not reliable
Solution Approach 1:
The patent improves the reliability of cooking end time determination by using the lightness coordinate L* from L*a*b* color space as the measurement parameter for browning degree. The lightness value provides a perceptually uniform and physically meaningful metric that correlates well with the visual appearance of cooked food. By comparing the lightness values of segmented food pixels against target lightness values, the system achieves accurate cooking status assessment with simple computational operations, maintaining both speed and reliability.
Data Source
AI summary
In a method for determining a cooking end time of food located in a cooking chamber of a household cooking appliance, at the beginning of a cooking process a lightness value-separable image of the cooking chamber is created and segmented based of color coordinates thereof by way of cluster analysis to produce food pixels associated with the food and surrounding pixels associated with a surrounding area of the food. A user is offered an opportunity to enter a target degree of browning. During the cooking process images of the cooking chamber at intervals are recorded and a respective actual degree of browning is computed in these images based on the food pixels. The actual degree of browning is compared with the target degree of browning and the food is treated in the cooking chamber until the actual degree of browning has at least approximately reached the target degree of browning.