Image Matte Generation Using Burst Feature Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional image processing applications face challenges in accurately segmenting foreground objects from backgrounds, especially in regions with complex boundaries or edges like hair or fur, due to the lack of effective methods for automatic detection and boundary determination.
Innovation Solution
An image processing system that leverages information from an image burst to generate a matte by aligning features between reference and burst images using machine learning models, background reconstruction, or foreground modeling, thereby improving the accuracy of foreground and background contribution determination at pixel boundaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing methods are used for segmentation, then the process is simple and fast, but the segmentation accuracy is poor in boundary regions
Solution Approach 1:
The patent segments the image into multiple individual images captured in rapid succession (image burst), then processes each image to extract foreground and background information. By dividing the complex segmentation problem into multiple simpler sub-problems across different time instances, the system achieves better boundary region accuracy while managing computational complexity through parallel processing of individual frames.
Solution Approach 2:
The patent transitions from processing a single static image to processing a sequence of images across the time dimension. By leveraging temporal information from multiple frames showing relative movement between foreground and background, the system resolves ambiguous boundary pixels through temporal consistency analysis, effectively adding a time dimension to the segmentation problem.
2Measurement precision
If more information from multiple images is used, then the segmentation accuracy improves, but the processing time and computational load increase
Solution Approach 1:
The patent performs preliminary processing by capturing an image burst in rapid succession before any detailed analysis. The sequential images are pre-aligned and organized, with motion estimation performed upfront to identify regions of interest. This preliminary organization of temporal data enables more efficient subsequent processing while maximizing the use of available temporal information for accurate matte generation.
3Measurement precision
If manual boundary drawing is used, then the segmentation can be accurate for simple cases, but it is time-consuming and impractical for complex boundaries
Solution Approach 1:
The patent enables the system to automatically detect and segment foreground objects without manual intervention. By analyzing temporal consistency and motion patterns across the image burst, the system self-determines boundary regions and generates mattes autonomously. This automated approach handles complex boundaries (such as hair or fur) that would be difficult to manually segment, significantly improving processing efficiency while maintaining accuracy.
Data Source
AI summary
Systems and methods perform image matte generation using image bursts. In accordance with some aspects, an image burst comprising a set of images is received. Features of a reference image from the set of images is aligned with features of other images from the set of images. A matte for the reference image is generated using the aligned features.