Guided Generative Model Masking for Precise Background Separation
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Solution Overview
Problem
Conventional image processing methods struggle to accurately separate foreground objects, particularly text characters, from the background, leading to unsatisfactory results with overlapping boundaries and inconsistent edges, requiring manual editing by users.
Innovation Solution
An image processing apparatus that combines an unconditional mask and a conditional mask, along with a distance transformation map and a color map, to generate a precise foreground mask, enabling seamless integration of characters into new backgrounds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing methods are used to separate foreground from background, then the processing is simple, but the separation accuracy is poor with overlapping boundaries and inconsistent edges
Solution Approach 1:
The patent divides the mask generation task into two separate networks: an unconditional mask network that processes only the input image, and a conditional mask network that processes both the input image and approximate mask. This segmentation allows each network to specialize in specific aspects of foreground separation, improving overall accuracy without requiring a single overly complex system.
Solution Approach 2:
The patent introduces an approximate mask as an intermediary element that guides the conditional mask network. This intermediary provides prior information about the foreground region, helping the network achieve more accurate separation boundaries without requiring the network to process all information from scratch, thus balancing complexity and precision.
2Manufacturing precision
If a single mask network is used, then the system is simple, but the foreground mask accuracy is insufficient for seamless character integration
Solution Approach 1:
The patent combines the outputs of two separate mask networks (unconditional and conditional) to produce the final foreground mask. This merging approach leverages the strengths of both networks: the unconditional network captures general foreground patterns, while the conditional network refines boundaries using approximate mask guidance, achieving high precision through combination.
Solution Approach 2:
The patent employs two separate mask generation processes instead of one, where each network performs a partial function. The unconditional network handles baseline mask generation, while the conditional network provides refinement. This partial action approach distributes the computational workload and improves precision without requiring one excessively complex network.
3Productivity
If manual editing is required to fix boundary issues, then the mask generation is simple, but the overall process efficiency is reduced
Solution Approach 1:
The patent enables the mask network system to automatically correct its own output by using the conditional mask network to refine boundaries based on approximate mask guidance. This self-service capability eliminates the need for manual editing to fix boundary issues, significantly improving productivity while the automated refinement process handles the complexity internally.
Data Source
AI summary
Embodiments of the present disclosure include obtaining an input image and an approximate mask that approximately indicates a foreground region of the input image. Some embodiments generate an unconditional mask of the foreground region based on the input image. A conditional mask of the foreground region is generated based on the input image and the approximate mask. Then, an output image is generated based on the unconditional mask and the conditional mask. In some cases, the output image includes the foreground region of the input image.