Autofocus Object Area Identification via Color Boundary Segmentation
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Solution Overview
Problem
Existing autofocus technologies face challenges in accurately focusing on the intended object under variable imaging conditions, often failing to distinguish between main objects and backgrounds, especially when objects have similar colors.
Innovation Solution
An image processing apparatus and method that utilize object area information and designated focus position information to partition images by color boundaries, estimate background areas, correct focus positions, and generate object frames to ensure accurate focusing on main objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If autofocus is executed based on focus evaluation values using conventional techniques, then the focusing process can be automated, but the system fails to correctly identify the main object area under variable imaging conditions
Solution Approach 1:
The image is segmented into multiple candidate areas based on color boundaries and object detection. The area partition block divides the image into distinct regions, and the focus evaluation is performed separately on each candidate area. This segmentation allows the system to evaluate multiple potential main objects simultaneously and select the correct one based on focus metrics, resolving the contradiction between automation and accuracy.
2Measurement precision
If the system processes multiple candidate areas to improve main object identification, then the accuracy of focus positioning improves, but the processing complexity increases
Solution Approach 1:
Different processing strategies are applied to different candidate areas based on their local characteristics. The area partition block identifies regions with different color boundaries, and the focus evaluation is tailored to each region's properties. This local quality approach allows efficient processing of multiple areas without uniformly applying complex algorithms to the entire image, thus improving accuracy while managing complexity.
3Reliability
If the system corrects focus positions that fall on background areas, then the reliability of focusing on main objects improves, but additional processing steps are required
Solution Approach 1:
The system implements a feedback mechanism where focus evaluation results are used to determine whether the detected main object area is correct. If the focus evaluation indicates that the designated focus position falls on a background area rather than a main object, the system automatically corrects the focus position by selecting an alternative candidate area. This feedback loop improves reliability by validating and correcting focus positions based on actual image content analysis.
Data Source
AI summary
The present disclosure relates to an image processing apparatus and method that enables to correctly obtain an area of a main object at the time of auto focusing.A control block selects a local focus area from information of a taken image and supplies designated focus position information indicative of the selected local focus area to an image processing block. The image processing block executes a control operation related with focusing on the basis of object area information indicative of an area corresponding to two or more objects within an image and designated focus position information indicative of a designated focus position in this image. The present disclosure is applicable to an imaging apparatus, for example.