Object Recognition Rate-Based Image Correction Method
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Solution Overview
Problem
Existing image correction methods in electronic devices lack the ability to dynamically adjust and enhance images based on user selection and recognition rates, leading to suboptimal correction quality.
Innovation Solution
An electronic device with a processor that recognizes objects in images, identifies recognition rates and categories, and uses reference images to correct objects or regions based on user input, enhancing the correction process through a cloud environment and user involvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If automatic image correction is performed without user selection, then processing speed is improved, but correction precision deteriorates
Solution Approach 1:
The system dynamically adjusts the correction process by allowing users to select specific objects that need correction. The correction precision is adaptively changed based on user selection and recognition rates, rather than applying a fixed automatic correction to all objects. This resolves the contradiction by making the system flexible enough to provide high precision for selected objects while maintaining efficient processing for non-selected areas.
Solution Approach 2:
The patent applies different correction qualities to different regions of the image based on user selection. Selected objects receive high-precision correction using reference images, while non-selected objects maintain their original quality or receive minimal correction. This local differentiation allows the system to achieve high correction precision for important objects without sacrificing overall processing speed.
2Manufacturing precision
If user selection is required for correction, then correction precision is improved, but device complexity increases
Solution Approach 1:
The system automatically identifies objects and provides correction options to users without requiring complex manual intervention. The object recognition and reference image selection processes are automated, with the system presenting pre-processed options that users can easily select. This reduces the perceived complexity for users while maintaining high correction precision through automated analysis.
Solution Approach 2:
The system performs preliminary object recognition and reference image selection before the actual correction process. By pre-processing and organizing potential correction options, the system reduces the complexity of the main correction operation. Users only need to select from pre-prepared options rather than configuring complex correction parameters from scratch.
3Manufacturing precision
If reference images are used for correction, then correction quality is improved, but information processing volume increases
Solution Approach 1:
The system extracts only the essential features and characteristics from reference images that are needed for correction, rather than processing entire reference images. By extracting key visual features and comparing only relevant portions, the system maintains high correction quality while significantly reducing the volume of data that needs to be processed and compared.
Solution Approach 2:
The system applies reference image correction selectively only to user-selected objects that meet certain recognition rate thresholds, rather than processing all objects in the image. This partial application of correction reduces the overall data processing volume while maintaining high correction quality for the most important objects where it matters most.
Data Source
AI summary
Various embodiments provide an electronic device and a method, the electronic device comprising a communication module, a memory, and a processor, wherein the processor is configured to: recognize at least one object from among one or more objects by using an image containing the one or more objects; identify a recognition rate and a category corresponding to the at least one object at least on the basis of the recognition; obtain at least one reference image corresponding to the object at least on the basis of the category; when the recognition rate satisfies a first specified condition associated with the recognition rate corresponding to the category, correct the at least one object or an area corresponding to the at least one object by using a reference image, which satisfies the first specified condition, from among the at least one reference image; and when the recognition rate satisfies a second specified condition associated with the recognition rate corresponding to the category, correct the at least one object or the area corresponding to the at least one object by using a reference image selected in accordance with an input from among the at least one reference image. In addition, other embodiments are also possible.