Composite Image Generation Using Neural Network Positioning

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Conventional image synthesis techniques require direct user input for the position and size of objects in composite images, hindering efficiency and user convenience.

Innovation Solution

A method using artificial neural networks to generate composite images by extracting feature vectors from foreground images and estimating optimal position and size within background images, allowing for automatic synthesis without user input.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If conventional image synthesis technology is used, then image synthesis can be performed, but the user must directly input the position and size of objects which hinders efficiency and user convenience

Engineering Contradiction:

Improveuser convenienceVSAvoidautomatic position and size determination

Core Design Contradiction:

Ease of operationVSExtent of automation

Solution Approach 1:

The system enables self-service by automatically determining object position and size without requiring user input. The neural network model processes the foreground image and background image to autonomously generate placement information, allowing the system to serve itself rather than requiring manual user specification for each parameter

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical/manual input system with an artificial neural network-based automated system. Instead of requiring users to mechanically input position and size parameters, the system uses deep learning models to automatically extract and determine these parameters from image data, substituting manual operations with intelligent automation

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If conventional image synthesis technology is used, then image synthesis can be performed, but the direct user input requirement impedes the efficiency of the image synthesis task

Engineering Contradiction:

Improveimage synthesis efficiencyVSAvoidtime for user input

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary action by pre-processing the input images through the neural network model to automatically extract position and size information before the actual image synthesis begins. This preliminary automated extraction eliminates the need for users to spend time manually specifying these parameters, thereby reducing overall task time and improving efficiency

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces manual user input mechanisms with automated neural network-based systems that continuously process images and generate synthesis parameters without human intervention, thereby eliminating time loss associated with manual parameter specification and significantly improving synthesis efficiency

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250014148A1Method and system for generating composite image

Publication Date: 2025.01.09 GENGENAI INC

AI summary

The present disclosure relates to a method for generating a composite image, executed by one or more processors. The method for generating a composite image includes receiving a foreground image, receiving a background image, generating information on a position and size within the background image from the foreground image and the background image using a first artificial neural network, and generating a composite image based on the foreground image, the background image, and the information on the position and size within the background image.