Image Processing Method for Automated Snapshot Selection
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Solution Overview
Problem
Current image capturing technologies in social software platforms, such as WeChat and Twitter, often require manual user intervention and lack automated methods to effectively isolate and enhance the most interesting parts of an image, leading to suboptimal snapshot quality and increased user effort.
Innovation Solution
An image processing method that automatically identifies candidate areas within an image containing a reference target, extracts predetermined characteristics, calculates evaluation values, and selects the best snapshot based on these values, incorporating photographic composition rules and user preferences to ensure accurate and satisfying image capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual user intervention is used for image capturing, then user control over the snapshot is maintained, but user effort and operation time increase
Solution Approach 1:
The system performs self-service by automatically detecting targets, extracting candidate areas, evaluating them based on predetermined characteristics, and selecting snapshots without requiring manual user intervention. The image processing device autonomously completes the entire workflow from image input to snapshot output, eliminating the need for users to manually select or adjust parameters.
Solution Approach 2:
The system performs preliminary actions by pre-defining evaluation characteristics and thresholds before processing. The image processing device prepares candidate areas and evaluation criteria in advance, then automatically executes the selection process. This preliminary setup enables automated decision-making without requiring real-time user input during the snapshot selection process.
2Productivity
If automated methods are used to isolate interesting parts, then user effort is reduced, but accuracy in capturing the most interesting aspects may deteriorate
Solution Approach 1:
The patent replaces manual mechanical selection with automated computational methods. Instead of users manually selecting areas, the system uses image processing algorithms to automatically detect targets, extract candidate regions, and evaluate them based on predetermined characteristics. This substitution maintains accuracy by using sophisticated computational evaluation rather than human judgment.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously evaluating candidate areas against predetermined characteristics and adjusting selections accordingly. The image processing device receives the original image, processes it through multiple evaluation stages, and selects snapshots based on feedback from the evaluation results. This iterative feedback process ensures accurate identification of the most interesting aspects.
3Manufacturing precision
If multiple candidate areas are evaluated, then snapshot quality improves, but processing time and computational complexity increase
Solution Approach 1:
The system applies segmentation by dividing the image into multiple candidate areas and evaluating them independently based on predetermined characteristics. The image processing device segments the original image into distinct regions of interest, then processes each segment separately through evaluation functions. This segmentation enables parallel processing and reduces overall computational time while maintaining high snapshot quality through comprehensive evaluation of multiple candidates.
Data Source
AI summary
An image processing method, device and medium are provided. The method includes: acquiring candidate areas from an image to be processed, each of the candidate areas including a reference target; extracting a predetermined characteristic of each of the candidate areas; calculating an evaluation value of each of the candidate areas according to the predetermined characteristic; and acquiring a snapshot of the image to be processed according to the evaluation values.