Shadow Detection Neural Networks for Object-Aware Image Editing
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Solution Overview
Problem
Conventional image editing systems are inflexible and inefficient, requiring significant user interaction to perform edits at the pixel level and failing to anticipate and prepare for object-level modifications.
Innovation Solution
A scene-based image editing system that utilizes machine learning models to pre-process digital images, segmenting objects, generating object masks and content fills, and creating semantic scene graphs to facilitate intuitive and efficient object-aware editing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image editing systems perform pixel-level edits, then editing precision is maintained, but user interaction complexity increases significantly
Solution Approach 1:
The system segments the image into multiple objects using machine learning models, allowing users to edit at the object level rather than pixel level. Each object is identified and separated, enabling cohesive unit editing while maintaining precision through automated object boundary detection.
Solution Approach 2:
The system introduces an intermediary object mask between the user input and the actual editing operation. The mask serves as a mediator that translates user intent into precise editing boundaries, reducing direct user interaction complexity while maintaining editing precision through automated mask generation.
2Ease of operation
If conventional systems require significant user interaction for editing, then editing control is maintained, but editing efficiency decreases
Solution Approach 1:
The system performs preliminary actions by pre-processing the image to identify objects, generate masks, and prepare edit candidates before the user actually initiates editing. This anticipatory processing maintains user control over the final edit decision while significantly improving efficiency by eliminating preparatory steps.
Solution Approach 2:
The system enables self-service editing by automatically performing object identification, mask generation, and edit application based on user intent. The editing system serves itself by autonomously completing tasks that would otherwise require manual user intervention, thereby maintaining control while boosting productivity.
3Manufacturing precision
If the system processes images at pixel level, then detail accuracy is preserved, but processing time increases
Solution Approach 1:
The system divides the image into segmented objects rather than processing all pixels uniformly. This segmentation allows the system to focus computational resources only on relevant object regions, preserving detail accuracy within objects while reducing overall processing time by avoiding unnecessary pixel-level operations in background areas.
Solution Approach 2:
The system applies local quality processing by treating different regions of the image differently based on their importance. Objects identified by the machine learning model receive detailed processing to maintain accuracy, while other regions receive minimal processing, thereby optimizing the balance between detail accuracy and processing time.
4Extent of automation
If the system uses machine learning models for pre-processing, then automation level increases, but system complexity increases
Solution Approach 1:
The system employs a universal machine learning model that performs multiple functions including object identification, mask generation, and edit candidate selection. This multi-functional approach increases automation level while managing complexity by using a single versatile model rather than multiple specialized components.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For instance, in one or more embodiments, the disclosed systems receive a digital image from a client device. The disclosed systems detect, utilizing a shadow detection neural network, an object portrayed in the digital image. The disclosed systems detect, utilizing the shadow detection neural network, a shadow portrayed in the digital image. The disclosed systems generate, utilizing the shadow detection neural network, an object-shadow pair prediction that associates the shadow with the object.