Object-Specific Preset Edits for Precise Image Layout Adaptation
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Solution Overview
Problem
Conventional image editing systems lack flexibility and accuracy in applying preset edits, particularly for specific objects within digital images, leading to inefficiencies and resource waste due to manual editing and inaccurate application of fixed-area presets.
Innovation Solution
An object-specific-preset-edit system that identifies and generates edits tailored to specific objects, storing object-specific-preset edits for application to similar objects in other images, using machine learning to detect and apply transformed-positioning parameters for precise editing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If global preset edits are applied to digital images, then editing speed is improved, but editing precision deteriorates because edits are applied indiscriminately to all objects
Solution Approach 1:
The patent segments the image into multiple objects using object detection technology, then applies preset edits to each object individually rather than globally. The system identifies bounding boxes for different objects (faces, vehicles, animals) and applies edits selectively to each detected object, resolving the contradiction between speed and precision by automating object-level editing.
Solution Approach 2:
The patent implements local quality by allowing different preset edits to be applied to different objects within the same image. Each detected object can receive customized editing based on its type and characteristics, rather than applying a uniform edit across the entire image. This enables precise control over which objects receive which edits while maintaining automated processing.
2Manufacturing precision
If manual editing is performed on particular objects, then editing precision is improved, but user interaction intensity increases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically detect objects, select appropriate preset edits, and apply them without requiring manual user intervention. The object detection and edit application processes occur automatically based on user selection of preset edits, eliminating the need for users to manually select and edit each object individually while maintaining high precision.
Solution Approach 2:
The patent applies preliminary action by pre-detecting and categorizing objects in the image before editing begins. The system performs object detection and classification in advance, preparing bounding boxes and object identifiers that enable subsequent automated edit application. This preliminary processing eliminates the need for manual object selection during the editing phase.
3Manufacturing precision
If fixed-area preset edits are applied to particular sections, then editing precision is improved, but adaptability deteriorates when image layouts differ
Solution Approach 1:
The patent implements dynamics by making the edit application process adaptive to different image layouts through object detection. Rather than using fixed coordinate-based areas, the system dynamically identifies object locations and adjusts edit application accordingly. When the same preset edit is applied to different images, the system automatically adapts to the new object positions and layouts while maintaining consistent editing precision.
Solution Approach 2:
The patent applies parameter changes by using object detection parameters (bounding boxes, object identifiers, confidence scores) to control edit application. Instead of fixed spatial parameters, the system uses object-based parameters that automatically adjust to different layouts. This allows the same preset edit to be accurately applied regardless of object position, size, or image composition variations.
Data Source
AI summary
The present disclosure describes systems, non-transitory computer-readable media, and methods for generating object-specific-preset edits to be later applied to other digital images depicting a same object type or applying a previously generated object-specific-preset edit to an object of the same object type within a target digital image. For example, in some cases, the disclosed systems generate an object-specific-preset edit by determining a region of a particular localized edit in an edited digital image, identifying an edited object corresponding to the localized edit, and storing in a digital-image-editing document an object tag for the edited object and instructions for the localized edit. In certain implementations, the disclosed systems further apply such an object-specific-preset edit to a target object in a target digital image by determining transformed-positioning parameters for a localized edit from the object-specific-preset edit to the target object.