Local Color Model Opacity Masks for Edge Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing techniques fail to accurately generate opacity masks that capture both hard and soft edges of objects in images, often resulting in uniformly softened edges that do not reflect the features of the image, and may not completely include the desired areas.
Innovation Solution
The method involves determining a local color model of opacity for each pixel based on its neighborhood, using a function that de-emphasizes the contribution of pixels near the border, and updating the image mask with user input to improve opacity values, allowing for the creation of a coherent opacity mask with both hard and soft edges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a global color model of opacity is used to select areas of an image, then the entire image can be processed uniformly, but the resulting opacity mask does not completely include the desired areas and fails to capture local edge features
Solution Approach 1:
The patent applies local color models of opacity at different spatial locations throughout the image. Each local color model is determined from the colors of pixels in a local neighborhood, allowing the opacity mask to adapt to local edge features and color variations. This resolves the contradiction by maintaining global processing capability while introducing local adaptability to capture precise edge information that a global model would miss.
2Ease of operation
If artificial feathering or softening is applied to generate an opacity mask from a binary mask, then soft edges can be created, but the edges become uniformly softened and do not reflect the actual features (sharp edges) depicted in the image
Solution Approach 1:
The patent determines opacity values for boundary pixels using local color models that analyze color variations in the local neighborhood of each boundary pixel. This allows the system to preserve sharp edges where the image features demand it, while still providing soft transitions where appropriate. The local analysis enables the opacity mask to reflect actual image features rather than applying uniform softening, resolving the contradiction between ease of softening operation and fidelity to original edge sharpness.
3Productivity
If existing image processing techniques are used to generate opacity masks, then processing can be performed, but the masks fail to accurately capture both hard and soft edges of objects
Solution Approach 1:
The patent segments the image into different regions based on opacity values and color characteristics. Boundary pixels are identified and processed separately from non-boundary pixels using local color models. This segmentation allows the system to efficiently process only the critical boundary regions with computationally intensive local analysis, while maintaining overall processing speed. The segmentation strategy resolves the contradiction by enabling accurate edge capture without requiring the entire image to be processed at maximum detail level.
4Measurement precision
If more user input is required to improve image mask accuracy, then better selection can be achieved, but the selection process becomes more time-consuming and less efficient
Solution Approach 1:
The patent employs local color models that automatically determine opacity values for boundary pixels based on the colors of pixels in their local neighborhoods. This self-service mechanism reduces the need for extensive user input by having the system autonomously analyze color variations and generate appropriate opacity masks. The automatic local analysis provides accurate edge capture with minimal user intervention, resolving the contradiction between achieving high accuracy and minimizing time loss to user input.
Data Source
AI summary
Methods, systems, and apparatus, including computer program products, feature receiving an image mask where the image mask identifies a first opacity value of pixels in a raster image. The first opacity value ranges between a value representing foreground and a value representing background. Each pixel in the image has a color. A first boundary pixel is identified in the image. A second opacity value for the first boundary pixel is determined using a first local color model of opacity. The first local color model of opacity is determined based on the colors of the pixels in the image within a local neighborhood of the first boundary pixel.