Object-Level Image Editing Automation
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Solution Overview
Problem
Current image editing tools lack efficiency and user-friendly functionality, requiring complex operations and manual intervention for tasks like object manipulation and image enhancement, which limits user capability and aesthetic quality.
Innovation Solution
The use of object-level information, such as depth ordering and class identification, to automate image editing processes like smart tiling, copy-paste, digital tapestry, super resolution, auto-cropping, and color balancing, enabling single-user-action object selection and replacement, and improving user interfaces to provide class-specific editing options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional image editing tools are used, then basic editing functions are available, but the operations are complex and require manual intervention
Solution Approach 1:
The system automatically segments objects in images and performs editing operations without requiring manual user intervention. The computer executes algorithms that autonomously identify objects, determine their properties, and apply editing functions, making the system self-sufficient and eliminating complex manual operations.
Solution Approach 2:
The system changes the state of image data by automatically detecting object parameters such as position, size, shape, and depth ordering. By utilizing these detected parameters, the system performs intelligent editing operations that adapt to the specific characteristics of each object, simplifying the user interface while maintaining sophisticated functionality.
2Productivity
If traditional image editing tools are used, then basic editing functions are available, but productivity is reduced due to manual effort
Solution Approach 1:
The system performs preliminary object segmentation and property detection automatically before editing operations are needed. By pre-processing the image to identify objects and their characteristics, the system prepares the data structure in advance, enabling rapid execution of editing commands without time-consuming manual analysis.
Solution Approach 2:
The system replaces manual mechanical editing operations with automated computer-based algorithms. Instead of requiring users to manually select and manipulate image regions, the system uses computational algorithms to automatically identify objects and apply editing transformations, dramatically improving productivity and reducing time loss.
3Extent of automation
If object-level information is used, then automated editing is achieved, but the system requires advanced image analysis capabilities
Solution Approach 1:
The system segments the image into distinct object regions and analyzes properties for each segment independently. By dividing the complex image analysis task into smaller, manageable segments corresponding to individual objects, the system achieves high-level automation while managing computational complexity through modular processing.
Solution Approach 2:
The system applies different analysis and editing operations to different regions of the image based on local object characteristics. By tailoring the processing to the specific properties of each object region (such as depth ordering, occlusion relationships, and object class), the system achieves sophisticated automation without requiring uniformly complex processing across the entire image.
Data Source
AI summary
Systems and methods for editing digital images using information about objects in those images are described. For example, the information about objects comprises depth ordering information and/or information about the class each object is a member of. Examples of classes include sky, building, aeroplane, grass and person. This object-level information is used to provide new and/or improved editing functions such as cut and paste, filling-in image regions using tiles or patchworks, digital tapestry, alpha matte generation, super resolution, auto cropping, auto color balance, object selection, depth of field manipulation, and object replacement. In addition improvements to user interfaces for image editing systems are described which use object-level information.