Optical Flow Auto Focus for Out-of-Plane Movement
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Solution Overview
Problem
Traditional auto-focusing methods in digital cameras do not effectively utilize optical flow techniques to adjust the camera lens position based on the out-of-plane movement of objects, leading to inaccuracies in maintaining focus as objects move closer or farther from the camera.
Innovation Solution
The method involves tracking features in an image, creating vector fields to determine the magnitude and direction of reference point changes across frames, identifying out-of-plane movement, and adjusting the camera lens position based on the change in distances between reference points to maintain focus on the object.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional auto-focusing methods are used, then the camera can maintain focus for stationary objects, but it fails to accurately track and adjust focus for objects moving out of plane (closer or farther from the camera)
Solution Approach 1:
The patent replaces traditional mechanical or simple optical auto-focusing mechanisms with an optical flow-based computational approach. By using vector field analysis of image sequences to detect out-of-plane movement, the system substitutes complex mechanical sensing with image processing algorithms that can accurately track object distance changes and trigger appropriate lens adjustments.
Solution Approach 2:
The patent introduces vector fields as an intermediary representation between the captured image sequences and the auto-focusing control system. The vector fields serve as a mediator that encodes motion information and out-of-plane movement characteristics, enabling the system to accurately determine focus adjustments without direct mechanical measurement of object distance.
2Measurement precision
If optical flow techniques are implemented to detect out-of-plane movement, then focus tracking accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the image into multiple blocks or regions and computes optical flow independently for each segment. This segmentation approach reduces the overall computational complexity by dividing the large-scale image processing task into smaller, manageable sub-tasks that can be processed in parallel, while still capturing the essential motion information needed for accurate focus tracking.
Solution Approach 2:
The patent computes optical flow only for relevant regions or uses simplified optical flow algorithms that provide sufficient accuracy for auto-focusing purposes without the full computational overhead of complete image sequence analysis. This partial action approach achieves the necessary measurement precision while reducing unnecessary computational burden.
Data Source
AI summary
A method is described that includes identifying a set of features of an object, the features being tracked in an image captured by a camera. The method also includes creating a field of vectors for the reference points. The vectors indicate magnitude and direction of change in position of the reference points across more than one frame of the image. The method further includes identifying existence of out of plane movement of the object's features from same radial orientation of the vectors. The method further includes determining an amount of closer/farther movement of the object's features to/from the camera from change in distances between a plurality of the reference points. The method further includes adjusting a position of camera's lens in view of the amount of closer/farther movement of the object's features to keep the camera focused on the object.