Hierarchical Image Decomposition for Customizable Component Editing
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Solution Overview
Problem
Current image segmentation techniques are inadequate in allowing for customizable and intuitive manipulation of image components, particularly in separating and editing texture, shading, and contour features, which limits the ability to achieve desired visual properties in output images.
Innovation Solution
A multi-level hierarchical image decomposition method that segments an input image into unique sets of pixels, represented as image cues, with adjustable factors for each component, including an image matte component for contour control, an image shading component for luminance filtering, and an image texture component for gray-level variation estimation, enabling separate manipulation and merging of image components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current image segmentation techniques are used, then image decomposition into components is achieved, but the ability to customize and manipulate image components is limited
Solution Approach 1:
The patent segments the image into multiple distinct components including foreground objects, background, texture elements, and shading regions. Each component is represented as a separate mask or segmentation layer that can be independently manipulated, edited, or replaced while maintaining the overall image structure through hierarchical organization of these segments.
Solution Approach 2:
The patent implements dynamic adjustability by allowing users to modify parameters of individual image components such as color, texture intensity, shading depth, and contour sharpness. The system provides interactive controls that enable real-time adjustment of each component's properties without affecting the entire image, making the manipulation process flexible and adaptive.
2Ease of operation
If image components are segmented into separate manipulable elements, then intuitive editing capability is improved, but the complexity of processing increases
Solution Approach 1:
The patent divides the image processing task into separate operational steps for each component type (foreground segmentation, background segmentation, texture extraction, shading separation). Each segmentation step produces a dedicated mask that can be independently edited, making the overall process more intuitive while managing complexity through modular processing stages.
Solution Approach 2:
The patent introduces intermediate representation layers including alpha masks, depth maps, and texture atlases that serve as mediators between the original image and the final edited output. These intermediaries simplify the editing process by providing user-friendly interfaces for adjusting individual components while automatically coordinating the changes across all segmentation layers.
3Adaptability or versatility
If texture, shading, and contour features are separated, then visual property control is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary decomposition of the image into texture, shading, and contour components during an initial processing stage. By pre-segmenting these elements before the user begins editing, the system prepares the image data in advance, allowing faster manipulation during the editing phase without requiring real-time decomposition for each adjustment operation.
Solution Approach 2:
The patent implements dynamic processing modes that adjust the level of decomposition based on user needs and operations. For simple edits, the system uses pre-computed components for fast processing. For complex edits requiring fine-grained control, the system dynamically performs additional decomposition steps, balancing processing time with the level of visual property control required.
Data Source
AI summary
A computer-implemented method includes segmenting an input image into a plurality of image cues, each image cue representing a unique set of pixels of the input image. For each image cue, the method includes determining a set of image components, wherein each image component is associated with at least one adjustable factor to represent at least one characteristic of the image cue.