Adaptive Autofocus Window Segmentation for Small Object Detection
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Solution Overview
Problem
Conventional autofocus systems in digital cameras, particularly in camera phones, face challenges in accurately focusing on small objects and maintaining focus in low-light conditions and during camera motion, leading to inaccuracies and false peak detections.
Innovation Solution
The implementation of a novel adaptive configuration scheme for windows of interest in multispot autofocus, where sub-windows of interest are logically separated and assigned focus value masks, with overlapping sub-windows to reduce the effects of camera shaking and zooming, and increasing the number of sub-windows in the middle row to improve focus detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional autofocus algorithms use standard window configurations, then the system is simple to implement, but focus detection accuracy deteriorates for small objects and in low-light conditions
Solution Approach 1:
The autofocus window is divided into multiple sub-windows arranged in a grid pattern, with each sub-window further segmented into regions with different focus value masks. This segmentation allows the system to detect focus more accurately for small objects by examining multiple smaller regions rather than one large window, while the standardized grid configuration maintains implementation simplicity.
Solution Approach 2:
Different regions within sub-windows are assigned different focus value masks (e.g., center region vs. corner regions), allowing the algorithm to weight different areas differently based on their importance for focus detection. This local differentiation improves accuracy for small objects located in specific regions without requiring complete algorithm redesign.
2Reliability
If the autofocus system uses a single large window of interest, then the algorithm is computationally efficient, but reliability deteriorates during camera motion and in low-light conditions
Solution Approach 1:
The single large window is segmented into multiple smaller sub-windows that can be independently evaluated. This segmentation provides redundancy - if camera motion affects one sub-window, other sub-windows can still provide reliable focus information. The multiple sub-windows improve reliability during motion and in low-light conditions while maintaining computational efficiency through parallel processing.
Solution Approach 2:
The system pre-configures multiple sub-windows with different focus value masks to cushion against potential failures in any single window evaluation. By having multiple predetermined evaluation regions, the system is prepared in advance to handle camera motion and low-light conditions without requiring complex real-time adjustments.
3Measurement precision
If standard grid formation is used for sub-windows, then the configuration is simple, but detection precision deteriorates for small objects in the middle row
Solution Approach 1:
The patent introduces asymmetry into the otherwise symmetric grid formation by modifying the middle row to have additional or differently configured sub-windows. This asymmetric configuration specifically targets the detection of small objects in the middle row, improving precision for that critical region while maintaining simple symmetric configurations in other rows. The focused modification adds minimal complexity only where needed.
Data Source
AI summary
In accordance with the exemplary embodiments of the invention there is at least a method, executable computer program, and apparatus to provide operations including logically separating into a plurality of parts at least one sub-window of interest of a plurality of sub-windows of interest arranged in a grid formation in an autofocus window of interest, assigning a focus value mask to each of the plurality of parts of the at least one sub-window, and executing an autofocus algorithm using the assigned focus value masks.