Image Object Segmentation With Preprocessing for Faster Editing
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Solution Overview
Problem
Existing image editing software applications face delays in object segmentation and often inaccurately classify background or other objects as part of the selected object, leading to improper object placement when edits are made.
Innovation Solution
Perform preprocessing on an initial image to identify objects, segments, and determine user intent before receiving input, using convolutional neural networks and diffusion models to enhance segmentation accuracy and reduce processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If object segmentation is performed after user input in existing image editing software, then the system can wait for user selection before processing, but this causes delays in segmentation processing time
Solution Approach 1:
The patent performs object segmentation and identifies selectable objects in advance before the user provides input. The system pre-processes the image to generate segmentation masks and identify objects of interest, so that when the user interacts with the interface, the segmentation is already complete and ready for immediate editing operations.
2Measurement precision
If existing software performs object segmentation, then objects can be selected for editing, but background pixels are often inaccurately classified as part of selected objects leading to improper object placement
Solution Approach 1:
The patent introduces an intermediary processing step that generates preliminary segmentation masks before final object selection. This intermediate segmentation layer acts as a mediator between the raw image and the final selected objects, allowing the system to refine object boundaries and prevent background pixels from being incorrectly classified as part of foreground objects.
Solution Approach 2:
The system employs feedback mechanisms where segmentation results are continuously refined based on object characteristics and user interaction patterns. The segmentation model learns from previous classifications and adjusts its boundaries to improve accuracy, ensuring that background pixels are properly separated from foreground objects.
3Measurement precision
If preprocessing is performed on the initial image to identify objects and segments, then segmentation quality improves, but the device complexity increases due to additional processing steps
Solution Approach 1:
The patent divides the image processing task into multiple independent segmentation stages, each handling specific types of objects or regions. By segmenting the overall processing task into smaller, specialized sub-tasks, the system achieves higher segmentation quality while managing complexity through modular architecture where each segment can be independently optimized and maintained.
Data Source
AI summary
A media application performs object recognition on an initial image to identify a set of objects in the initial image. The media application determines whether the initial image is an outdoor scene. Responsive to the initial image being an outdoor scene, the media application determining a sky segment from the initial image. The media application determines whether the initial image includes a subject that is human or animal. Responsive to the initial image including the subject, the media application determines a subject segment from the initial image. The media application receives at a user interface that includes the initial image, user input corresponding to selection of a selected object from the set of objects. The media application updates the user interface to include an indication that the selected object was selected.