Selective Color Image Generation Using Depth Map Masking
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Solution Overview
Problem
Existing image capturing devices require high user input to accurately generate selective color images, often resulting in improper coloring or rendering due to adverse conditions like reflections, which can lead to inaccuracies in the modified images.
Innovation Solution
An image capturing device method that captures color image data, generates a color mask based on user-selected locations, and combines it with monochromatic data to create a selective color image, optionally using morphological reconstruction to improve image quality by adding unmasked areas, thereby reducing user input and enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If user manually selects portions to colorize, then coloring precision can be improved, but user input complexity increases
Solution Approach 1:
The system performs automatic color mask generation and morphological reconstruction without requiring manual user intervention. The processor automatically identifies portions to colorize using image analysis algorithms, eliminating the need for users to manually select regions while maintaining high coloring precision through automated intelligent detection
Solution Approach 2:
The system performs preliminary morphological reconstruction to identify and correct adverse conditions (reflections, shadows, noise) before the colorization process. This preliminary analysis allows the system to pre-determine accurate color masks without requiring users to account for these conditions manually, thereby improving precision while reducing operational complexity
2Ease of operation
If automatic color mask generation is used, then user input is reduced, but accuracy deteriorates due to adverse conditions like reflections
Solution Approach 1:
The system performs preliminary morphological reconstruction operations before color mask generation to identify and compensate for adverse conditions such as reflections, shadows, and noise. This preliminary analysis enables the automatic system to distinguish between intentional monochrome portions and artifacts caused by adverse conditions, thereby maintaining high accuracy without requiring user input
Solution Approach 2:
The system uses feedback from the morphological reconstruction process to iteratively refine the color mask generation. By analyzing the reconstructed image data and comparing it with the original, the system automatically adjusts the color mask to correct errors caused by reflections and other adverse conditions, improving accuracy while maintaining automation
Data Source
AI summary
A method, a system, and a computer program product for generating a selective color image. The method includes capturing a color image data within a current scene. The method further includes retrieving image color values from the color image data. The method further includes receiving a selection of at least one location within the color image data. The method further includes generating a color mask, including at least one unmasked area identified within a depth map of the current scene, and applying the color mask to the color image data to generate a color masked image data. The method further includes combining the color masked image data with monochromatic image data of the current scene to create a selective color image that includes the monochromatic image data with the at least one portion, and then the method includes providing the selective color image to at least one output device.