Cloud Texturing for Image Stickers via Fractal Brownian Motion
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Solution Overview
Problem
Social networks lack effective methods to enhance user creativity in personalizing images, particularly in modifying content based on specific regions within an image, such as sky or water areas, to provide unique textures and expressions.
Innovation Solution
The implementation of a cloud texturing system that automatically modifies content placed in sky regions by converting it to grayscale, blurring, and combining it with a cloud-like texture generated using fractal Brownian motion, while allowing users to add stickers or graphical resources that can be textured based on their location within the image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If users manually add content to images, then user creativity is enabled, but the process is time-consuming and lacks automation
Solution Approach 1:
The system automatically detects sky regions in images and applies cloud textures to content within those regions without requiring user intervention. The processor independently identifies sky portions and modifies content based on location, enabling self-service automation that reduces manual effort while maintaining creative enhancement.
Solution Approach 2:
The system pre-processes images by automatically detecting and segmenting sky regions before user content addition. By preparing the sky region masks and texture mappings in advance, the system enables rapid automated application of cloud textures when users add stickers or text, reducing the time required for image personalization.
2Adaptability or versatility
If uniform texture is applied to all content, then processing is simple, but location-specific customization is lost
Solution Approach 1:
The system applies different cloud texture treatments to content based on its specific location within sky regions of the image. Rather than uniform processing, the texture application is localized to match the spatial context of each content element, providing adaptability while using efficient processing techniques to manage system complexity.
Solution Approach 2:
The image processing system segments the image into sky and non-sky regions, and further segments sky regions into multiple zones based on content location. This segmentation enables location-specific texture application while organizing the processing complexity into manageable discrete regions that can be handled independently.
3Manufacturing precision
If cloud texture is added to all content, then creativity is enhanced, but content outside sky regions is incorrectly modified
Solution Approach 1:
The system applies cloud textures exclusively to content located within detected sky regions, leaving content in non-sky regions unchanged. This localized quality control ensures precise texture application only where appropriate, maintaining manufacturing precision while preserving the adaptability to handle different content types in different image regions.
Solution Approach 2:
The system uses sky region detection masks as an intermediary layer between the full image and the texture application process. This intermediary selectively identifies which portions of the image should receive cloud texture treatment, ensuring precise application to sky-region content while preventing incorrect modification of content outside these regions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances user creativity by automatically adding engaging textures to image content, allowing for more expressive and personalized image customization, thereby improving user engagement and interaction within social networks.
Implementation Method 1
combining it with a cloud-like texture generated using fractal Brownian motion
Data Source
AI summary
Disclosed are systems, methods, and computer-readable storage media to modify image content. One aspect includes identifying, by one or more electronic hardware processors, an image and content within the image, determining, by the one or more electronic hardware processors, a sky region of the image, determining, by the one or more electronic hardware processors, whether the content within the image is located within the sky region of the image, and in response to the content being within the sky region of the image, modifying, by the one or more electronic hardware processors, the content based on fractal Brownian motion.