Appliance Cavity Image Processing for Support Member Positioning
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Solution Overview
Problem
Existing image processing systems for kitchen appliances lack the ability to efficiently identify and normalize the position of support members within appliance cavities, leading to inconsistent and obstructed views of food or dishes, which complicates cooking, cleaning, and storage operations.
Innovation Solution
An image processing method and system that captures images of appliance cavities using an imaging device, identifies support members via a processor, calculates a histogram of gradients to determine their position, and applies transformation matrices to generate standardized views, allowing for accurate food recognition, doneness detection, and inventory management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used without support member identification, then the imaging system is simpler, but the view of food or dishes becomes inconsistent and obstructed
Solution Approach 1:
The system performs preliminary identification of support members (racks, shelves) before conducting the main imaging task. By detecting and localizing support members first using histogram of gradients and transformation matrices, the system prepares the image data in advance, enabling subsequent food recognition and analysis to proceed with consistent, unobstructed views.
2Reliability
If support members are identified and positions are normalized, then consistent views are achieved, but the image processing time increases
Solution Approach 1:
The system replaces complex mechanical or manual adjustment mechanisms with computational image processing. Instead of physically adjusting camera positions or angles to achieve consistent views, the system uses histogram of gradients analysis and transformation matrices to computationally normalize support member positions, achieving view consistency through software rather than mechanical means.
3Difficulty of detecting and measuring
If histogram of gradients is calculated for support member detection, then detection accuracy improves, but computational load increases
Solution Approach 1:
The system applies histogram of gradients analysis selectively to regions where support members are likely to be located, rather than processing the entire image uniformly. By focusing computational resources on specific areas of interest and using transformation matrices to concentrate on relevant portions, the system achieves accurate detection while reducing overall computational load and energy consumption.
Data Source
AI summary
A method for processing images of an appliance includes capturing, via an imaging device, an image of a cavity of the appliance. The method further includes identifying a support member in the cavity based on the image via a processor in communication with the imaging device. The support member is configured to support an object thereon. The method further includes calculating a histogram of gradients based on the image via the processor. The method further includes receiving dimensional data of the appliance. The method further includes comparing the histogram of gradients to the dimensional data. The method further includes determining a position of the support member based on the comparison of the histogram of gradients to the dimensional data.