Image Processing Subject Priority Stabilization
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Solution Overview
Problem
Conventional image processing techniques face challenges in accurately determining the priority ranking of subjects in images, leading to frequent switching of main subjects during autofocus and exposure control, which affects operational stability and user experience.
Innovation Solution
An image processing apparatus and method that assigns weights based on subject size and position relative to the image center, with adjustments made using previous priority rankings to stabilize subject selection while allowing for timely changes when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If main subject selection is based only on current subject states (distance from image center, subject size), then the appropriate main subject at the specific time can be selected, but frequent switching of the main subject occurs between multiple subjects
Solution Approach 1:
The system performs preliminary action by determining the priority ranking of each subject in advance based on their states in previous images. This pre-established priority information is then used to stabilize main subject selection in the current image, preventing frequent switching while maintaining selection accuracy.
Solution Approach 2:
The system implements feedback by using the priority ranking information from previous images to influence the main subject selection in the current image. This feedback mechanism ensures that subjects with previously high priority are maintained as main subjects unless their current state significantly deteriorates, thereby stabilizing subject selection.
2Stability of the object's composition
If main subject selection considers information from previously selected main subjects, then frequent switching is suppressed, but the system cannot switch to a more appropriate subject when needed
Solution Approach 1:
The system applies dynamics by making the weight of previous priority information adjustable rather than fixed. The determination unit dynamically adjusts the influence of historical priority data based on the current subject states, allowing the system to be stable when subjects remain similar but adaptable when significant changes occur.
Solution Approach 2:
The system changes parameters by adjusting the weight coefficient applied to previous priority information. When subject states change significantly (e.g., distance from image center or subject size changes beyond threshold values), the system reduces the weight of historical priority data, enabling appropriate subject switching while maintaining stability during normal conditions.
3Measurement precision
If priority ranking changes frequently due to slight state variations, then the system responds to current image states, but operational stability is reduced and focusing accuracy is lost
Solution Approach 1:
The system performs preliminary action by pre-determining priority rankings based on previous image data before making main subject selection decisions. This preliminary priority assignment acts as a buffer against frequent changes caused by slight state variations, maintaining operational stability while still detecting significant subject state changes.
Solution Approach 2:
The system applies beforehand cushioning by using previously determined priority rankings to cushion against frequent priority changes. This historical priority information serves as a stabilizing buffer that prevents minor state variations from causing unnecessary main subject switching, thereby maintaining focusing accuracy and operational reliability.
Data Source
AI summary
Face regions are detected from a captured image, and a weight of each detected face region is computed based on a size and/or a position of the detected face region. Then a previous priority ranking weight is computed based on a priority ranking determined in previous processing. A priority of the face region is computed from the weight and the previous priority ranking weight. For example, if the continuous processing number exceeds the threshold the priority ranking weight is reduced. After the processing is completed for all face regions, a priority ranking of each face region is determined based on the priority computed for each face region.