Neural Network Focusing Point Selection for Camera Viewfinders
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Solution Overview
Problem
Users of digital cameras and portable smart devices face challenges in achieving accurate focusing, particularly when inexperienced or dealing with moving objects or distant subjects, due to the need for manual selection of focusing points, which can lead to low-quality images and increased operational complexity.
Innovation Solution
A focusing point determining method and apparatus that utilizes neural networks to identify significance areas in view-finding images and extract focusing points, allowing for automatic focusing point selection and reduction of user error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the focusing point is specified in advance by the user depending on experience, then the focusing operation can be performed, but the quality of the photographed image becomes low when the user is not sufficiently experienced
Solution Approach 1:
The system performs self-service by automatically determining the focusing point through neural network analysis of the view-finding image. The apparatus identifies significance areas and extracts focusing points without requiring user intervention, thereby eliminating the dependency on user experience while maintaining high focusing accuracy.
2Adaptability or versatility
If different compositions require different focusing points, then the focusing can be adapted to various compositions, but the user needs to switch among different focusing settings which influences the user's operations
Solution Approach 1:
The system dynamically determines focusing points based on the actual content of each view-finding image using neural network analysis. Instead of providing static focusing settings for different compositions, the apparatus automatically adapts to various compositions by identifying significance areas in real-time, thereby achieving composition adaptability without requiring users to switch among multiple focusing settings.
3Productivity
If the user snaps an object which is moving quickly, then the photographing can be performed, but it is very hard for the user to finish the operation of focusing within a very short time
Solution Approach 1:
The system performs preliminary action by continuously analyzing the view-finding image and determining the focusing point in advance before the user needs to capture the image. The neural network automatically identifies significance areas and extracts focusing points, so when the user snaps the photograph, the focusing operation has already been prepared, eliminating the time delay that would otherwise be required for manual focusing adjustments.
4Measurement precision
If the user focuses a further and smaller object, then the photographing can be performed, but the circumstance that the focusing point specified by the user is inaccurate would occur easily
Solution Approach 1:
The system replaces the mechanical/manual method of specifying focusing points with an automated neural network-based image analysis system. The apparatus processes the view-finding image through neural networks to automatically identify significance areas and extract precise focusing points, thereby overcoming the difficulty of manually detecting and measuring focusing points for further and smaller objects.
Data Source
AI summary
There are provided a focusing point determining method and apparatus. The focusing point determining method comprises: obtaining a view-finding image within a view-finding coverage; identifying a significance area in the view-finding image; and extracting at least one focusing point from the identified significance area. By identifying the significance area in the view-finding image and extracting at least one focusing point from the identified significance area, the focusing point determining method and apparatus can ensure accuracy of a selected focusing point to a certain extent, so as to ensure accuracy of focusing.