Automated Image Merging System for Composite Group Photos
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Solution Overview
Problem
Conventional digital image editing systems are inflexible and inefficient in automatically merging people and objects from multiple images into a composite image, requiring significant user input and multiple steps, especially when using handheld devices with limited screen space.
Innovation Solution
An image merging system that automatically detects and segments missing persons or objects from one image and integrates them into another using face detection and segmentation models, determining optimal placement for a natural and realistic composite image, reducing the need for user interaction and interface switching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional systems use manual tools with mouse input to add missing persons to images, then users can control the merging process, but the number of steps and user interactions increases significantly
Solution Approach 1:
The system performs automatic face detection, segmentation, and merging operations without requiring manual user input for each step. The computer vision model independently completes the entire merging process, eliminating the need for users to manually select tools, adjust parameters, or perform multiple manual operations.
Solution Approach 2:
The patent replaces manual mechanical operations (mouse clicking, manual selection, manual positioning) with automated computer vision algorithms. The system uses AI models to detect faces, segment individuals, and determine optimal placement automatically, substituting human manual control with automated computational processes.
2Manufacturing precision
If conventional systems provide manual selection tools for merging people, then users can perform precise selections, but the tools are complex and require high skill and expertise to operate
Solution Approach 1:
The system automatically performs face detection, segmentation, and merging without requiring users to learn or operate complex selection tools. The computer vision model independently identifies faces and determines optimal placement, eliminating the need for users to master complicated manual selection interfaces.
Solution Approach 2:
The patent changes the operational parameters from manual control (mouse coordinates, manual selection) to automated algorithmic parameters. The system uses computational models to automatically determine face locations, segmentation masks, and merging parameters, replacing complex manual parameter adjustment with automated computational parameter selection.
3Adaptability or versatility
If conventional systems require switching between multiple user interfaces to merge people from different images, then users can access different tools, but the number of operations and time required increases
Solution Approach 1:
The patent merges multiple separate operations (face detection, segmentation, merging) into a single integrated workflow. Instead of requiring users to switch between different tools and interfaces for each operation, the system combines all functions into one unified automated process that handles the entire merging task sequentially without interruption.
Solution Approach 2:
The system implements a universal automated merging interface that handles all merging operations through a single computer vision model. This multi-functional approach allows the system to perform face detection, segmentation, and merging within the same interface, eliminating the need for users to switch between specialized tools for different operations.
4Manufacturing precision
If conventional systems require high precision user input for manual merging operations, then users can control the exact placement, but it becomes difficult or impossible to use on handheld devices with limited screen space
Solution Approach 1:
The patent replaces manual mechanical input (finger tapping on small screens, mouse precision) with automated computer vision algorithms. The system uses AI models to automatically detect faces and determine optimal placement without requiring high-precision manual input, making the operation feasible on handheld devices with limited screen real estate.
Solution Approach 2:
The system performs automatic face detection, segmentation, and placement determination without requiring users to manually specify precise locations. The computer vision model independently identifies optimal placement positions and executes the merging operation, eliminating the need for users to provide high-precision input on small handheld displays.
Data Source
AI summary
The present disclosure relates to an image merging system that automatically and seamlessly detects and merges missing people for a set of digital images into a composite group photo. For instance, the image merging system utilizes a number of models and operations to automatically analyze multiple digital images to identify a missing person from a base image, segment the missing person from the second image, and generate a composite group photo by merging the segmented image of the missing person into the base image. In this manner, the image merging system automatically creates merged group photos that appear natural and realistic.