AI Collage Image Placement for Preserving Regions of Interest
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Solution Overview
Problem
Conventional image editing systems fail to account for subject matter or composition within digital images when placing them in collages, often resulting in suboptimal visual arrangements and requiring manual, time-consuming user interactions to achieve aesthetically pleasing results.
Innovation Solution
An image editing system employs artificial intelligence and machine learning techniques to automatically fit digital images into collage frames by identifying regions of interest and using reference images to optimize visual placement, preserving the integrity of these regions and reducing manual user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image editing systems place digital images into collage frames without considering subject matter or composition, then the placement process is simple and fast, but the visual arrangement quality deteriorates resulting in suboptimal aesthetic results
Solution Approach 1:
The patent introduces reference images as intermediary elements that mediate between the digital image to be placed and the collage frame. These reference images contain pre-analyzed composition data and subject matter information that guide the placement process, enabling the system to achieve high visual arrangement quality without requiring complex real-time analysis of every image-frame combination.
Solution Approach 2:
The system performs preliminary analysis of reference images to extract composition data, subject matter information, and placement guidelines before the actual image placement process. This pre-processing step creates a library of optimized placement parameters that can be quickly applied during collage generation, improving visual quality without adding complexity to the main placement operation.
2Manufacturing precision
If users manually adjust digital images to achieve aesthetically pleasing results in collages, then the visual arrangement quality improves, but the time consumption and user interaction requirements increase
Solution Approach 1:
The patent enables the system to automatically perform the adjustment and optimization functions that would otherwise require manual user intervention. By analyzing reference images and automatically determining optimal placement parameters, the system serves itself in achieving high visual arrangement quality without requiring users to spend time manually adjusting images.
Solution Approach 2:
The system automatically adjusts multiple parameters including image position, size, rotation, and cropping based on analysis of reference images. These parameter changes are computed and applied automatically, reproducing the效果 of manual user adjustments without the time investment required for manual intervention.
3Measurement precision
If the fitting algorithm uses reference images with AI and ML techniques to identify regions of interest and detect visual objects, then the placement accuracy improves, but the computational resources and processing time increase
Solution Approach 1:
The system performs AI and ML analysis on reference images in advance, before the actual image placement operation. This preliminary processing extracts composition data, identifies regions of interest, and detects visual objects once, creating reusable placement guidelines that can be applied to multiple images without repeating the computationally intensive analysis for each one.
Solution Approach 2:
The patent uses reference images as templates or copies that contain pre-analyzed structural and compositional information. Instead of performing full AI/ML analysis on every digital image to be placed, the system copies the placement parameters and composition data from the analyzed reference images, significantly reducing computational resources while maintaining high placement accuracy.
4Ease of operation
If the system automatically adjusts visual properties of digital photos to fit collage frames, then the ease of operation improves by reducing manual interactions, but the device complexity increases due to automated processing requirements
Solution Approach 1:
The system automatically performs all adjustment operations including cropping, resizing, rotating, and positioning without requiring manual user interaction. The automated processing analyzes reference images, determines optimal parameters, and executes adjustments autonomously, greatly improving ease of operation by eliminating the need for users to manually manipulate each image.
Solution Approach 2:
Reference images serve as intermediaries that bridge the gap between simple automated processing and complex manual adjustment requirements. By encoding composition guidelines and placement parameters in reference images, the system achieves sophisticated automated adjustment capabilities without requiring complex real-time processing algorithms.
Data Source
AI summary
Systems and methods are disclosed for reflowing documents to display semantically related content. Embodiments may include receiving a request to view a document that includes body text and one or more images. A trimodal document relationship model identifies relationships between segments of the body text and the one or more images. A linearized view of the document is generated based on the relationships and the linearized view is caused to be displayed on a user device.