Flood Fill Pixel Classification for Hair Extraction
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Solution Overview
Problem
Existing technologies face challenges in accurately and precisely extracting a user's persona, particularly the hair, from a video feed, which is crucial for multimedia communications like online meetings, due to difficulties in classifying pixels as foreground or background.
Innovation Solution
The implementation of a flood fill technique that assigns distance-cost values to pixels based on position-space and color-space cost values, using depth information and seed pixels selected from the video data, to classify pixels as foreground or background, thereby enhancing the precision of user extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pixel classification methods are used to extract user persona, then the extraction process is simpler, but the accuracy of hair detection and foreground-background classification deteriorates
Solution Approach 1:
The patent segments the pixel classification problem into multiple cost components: position-space cost (spatial distance from seed pixels) and color-space cost (color similarity to user persona). This segmentation allows each component to be optimized independently, improving hair detection accuracy while keeping the overall process manageable through modular computation.
Solution Approach 2:
The patent introduces a cost-value dimension to pixel classification, transforming simple binary classification into a multi-dimensional evaluation system. Each pixel is assigned distance-cost values that combine spatial and color information, enabling more accurate hair detection by considering multiple attributes simultaneously rather than relying on single-threshold methods.
2Manufacturing precision
If flood fill technique with distance-cost values is implemented, then pixel classification accuracy improves, but computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by selecting seed pixels from known user persona regions before executing the flood fill algorithm. These seed pixels serve as starting points with pre-assigned cost values, allowing the algorithm to propagate classification information efficiently through the image while maintaining precision in hair detection without requiring exhaustive search of all pixels.
Solution Approach 2:
The flood fill algorithm implements feedback mechanisms where each processed pixel's cost values influence the classification of neighboring pixels. The algorithm continuously refines pixel classifications by comparing accumulated cost values against threshold criteria, creating a self-correcting system that improves user extraction precision through iterative refinement rather than single-pass processing.
Data Source
AI summary
Disclosed herein are methods and systems for assigning pixels distance-cost values using a flood fill technique. One embodiment takes the form of a process that includes obtaining video data depicting a head of a user, obtaining depth data associated with the video data, and selecting seed pixels for a flood fill at least in part by using the depth information. The process also includes performing the flood fill from the selected seed pixels. The flood fill assigns respective distance-cost values to pixels of the video data based on position-space cost values and color-space cost values. In some embodiments, the process also includes classifying pixels of the video data as foreground based at least in part on the assigned distance-cost values. In some other embodiments, the process also includes assigning pixels of the video data foreground-likelihood values based at least in part on the assigned distance-cost values.