Hybrid Level Set Algorithm for Accurate Image Matting
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Solution Overview
Problem
Conventional image editing software struggles with accurately selecting complex objects, such as hair or feathers, due to limitations in edge-detecting algorithms, which often result in incomplete or inaccurate selections, requiring manual refinement and the use of separate tools for soft selection.
Innovation Solution
A hybrid algorithm that combines a conventional level set algorithm for hard selection with a tri-regional level set algorithm to define a matting region, allowing for real-time refinement of selection edges and automatic specification of complex edges, enabling a unified tool for both hard and soft selections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a conventional level set algorithm is used for hard selection, then the selection process is simple and fast, but the selection accuracy for complex edges (hair, feathers) is poor
Solution Approach 1:
The patent divides the selection process into two distinct phases: hard selection using a conventional level set algorithm for initial selection, and soft selection using a tri-regional level set algorithm for refining complex edges. This segmentation allows each algorithm to specialize in its strength while working together to achieve overall accuracy.
Solution Approach 2:
The patent merges the conventional level set algorithm and the tri-regional level set algorithm into a hybrid system. The hard selection mask from the first algorithm serves as input to the second algorithm, combining their capabilities to achieve both speed and accuracy in selection.
2Measurement precision
If manual refinement is used to improve selection accuracy for complex objects, then selection precision improves, but the time and effort required increases significantly
Solution Approach 1:
The system performs automatic refinement through the tri-regional level set algorithm that detects and processes complex edges without requiring manual user intervention. The algorithm autonomously identifies hair, feathers, and other complex boundaries and refines the selection mask accordingly, eliminating the need for time-consuming manual adjustments.
3Measurement precision
If separate tools are used for hard selection and soft selection, then each tool can be optimized for its specific function, but the overall workflow complexity increases
Solution Approach 1:
The patent creates a unified hybrid selection tool that performs both hard selection and soft selection functions in a single integrated operation. The system automatically transitions from hard to soft selection based on the detected complexity of edges, providing a universal solution that handles both simple and complex selection scenarios without requiring separate tools or complex workflow switching.
Data Source
AI summary
In various implementations, methods and systems are disclosed for accurately selecting a targeted portion of a digital image. In one embodiment, a selection cursor having a central and a peripheral region is provided. The central region is used to force a selection or a deselection and therefore moving the central region over a portion of the image causes that portion of the image to be selected or deselected, respectively. The peripheral region of the cursor surrounds the central region and defines an area where a hybrid level set algorithm for both boundary detection and region definition, particularly a matting region, is performed. This provides highly accurate boundary detection and matting region selection within a narrowly-focused peripheral region and eliminates the need to subsequently designate a matting region and apply a matting algorithm to complex portions of an object selection. Thus moving the peripheral region of the selection cursor over a boundary of the targeted portion of the image applies the hybrid algorithm in that boundary region, increasing the likelihood that the boundary will be detected accurately, and further defining at least a matting region along the detected boundary for an even more refined selection.