Seam Carving Mask Creation and Editing
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Solution Overview
Problem
Current digital image editing tools lack efficient methods for combining and modifying seam carving masks, which are essential for protecting or removing specific pixels during image resizing operations, often resulting in undesirable outcomes due to the lack of content-aware techniques.
Innovation Solution
The system allows users to generate, combine, and modify seam carving masks using graphical user interfaces, incorporating automatic skin tone detection and energy-based prioritization to create custom masks that protect or target specific pixel areas during image editing operations, such as resizing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic mask generation is used, then productivity is improved, but manufacturing precision deteriorates
Solution Approach 1:
The system generates preliminary mask suggestions automatically based on image analysis, which are then refined through user interaction. This preliminary automatic generation speeds up the initial mask creation while allowing subsequent precision adjustments.
Solution Approach 2:
The system incorporates feedback mechanisms where user modifications to automatically generated masks are analyzed and used to improve subsequent automatic generation accuracy. The blend mode operations and user corrections create a feedback loop that enhances both speed and precision over time.
2Manufacturing precision
If manual mask creation is used, then manufacturing precision is improved, but productivity deteriorates
Solution Approach 1:
Instead of requiring complete manual mask creation, the system performs partial automatic generation and applies it selectively. Users only need to modify specific regions that require precision, rather than creating entire masks manually, thus achieving both speed and accuracy.
Solution Approach 2:
The mask creation process is segmented into automatic generation phases and manual refinement phases. Different regions of the mask can be created using different methods, allowing efficient handling of large images while maintaining precision in critical areas.
3Adaptability or versatility
If multiple masks are combined, then adaptability is improved, but device complexity deteriorates
Solution Approach 1:
The system merges multiple masks using blend mode operations (source-over, destination-over, multiply, screen, lighten, darken, difference, exclusion). This combining capability provides adaptability for complex editing tasks while the standardized blend mode operations keep the system complexity manageable.
Solution Approach 2:
The mask combination system uses universal blend mode operations that can be applied to any pair of masks regardless of their origin or purpose. This multi-functional approach allows versatile mask combination without requiring separate handling logic for different mask types.
Data Source
AI summary
Systems and methods for creating and editing seam carving masks may allow a user to combine masks, and/or to modify automatically generated suggested masks, manually created masks, combination masks, or previously stored masks using tools of a graphical user interface in a graphics application (e.g., a mask brush or mask eraser). The method may include accessing data representing an image and automatically generating a suggested mask for the image (e.g., based on a color or color range, a threshold energy value, or input specifying two or more previously stored masks for combination). The method may include displaying the suggested mask as an overlay of the image, highlighting mask pixels using a respective color or pattern. The user may indicate pixels to be added to or removed from the suggested mask to produce a modified mask for application in an image editing operation (e.g., a resizing, filtering, or feature identification operation).