AI Image Field Extension for Centering Edge-Framed Subjects
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Solution Overview
Problem
Existing image editing systems struggle to maintain aesthetic consistency when a subject is positioned at the edge of a camera's field of view, leading to incomplete frames and uneven presentation in video conferencing scenarios, as adjusting the camera or subject positioning is often impractical.
Innovation Solution
An image field extension system utilizing generative artificial intelligence (AI) algorithms to synthesize plausible image data, filling void areas in crop windows beyond the camera's field of view, creating composite images that center subjects by integrating foreground and synthesized background elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the camera field of view is extended to center subjects at the edge of the frame, then the aesthetic presentation and centering of subjects is improved, but the camera requires physical repositioning or adjustment which is not always available or desirable
Solution Approach 1:
The patent uses generative AI to create a synthesized copy of the background scene that fills the virtual extension of the camera field of view. Instead of physically moving the camera or subject, the system generates a synthetic representation of what would be visible beyond the current frame edges, allowing virtual centering without physical repositioning.
Solution Approach 2:
The patent replaces the mechanical system of physical camera repositioning with a computational system. Instead of moving the camera or subject to achieve better framing, the system uses image processing and generative AI algorithms to computationally extend the field of view and center subjects, eliminating the need for mechanical adjustments.
2Stability of the object's composition
If the camera position is fixed to avoid repeated adjustments, then the stability of the imaging setup is improved, but the ability to capture subjects at the edge of the field of view aesthetically is worsened
Solution Approach 1:
The patent replaces mechanical camera repositioning with computational image processing. The camera remains fixed and stable, while the system uses generative AI algorithms to computationally extend the field of view and create aesthetically pleasing frames by synthesizing background content that would be visible beyond the current frame edges.
Solution Approach 2:
The patent introduces an intermediary computational layer between the fixed camera and the final image output. This intermediary system processes the fixed camera input by generating synthetic background content that bridges the gap between the actual camera field of view and the desired extended frame, allowing aesthetic framing without camera movement.
3Ease of manufacture
If subjects are positioned at the center of the frame for aesthetic presentation, then the visual appeal is improved, but the subjects must move or be repositioned which is not always possible in fixed camera scenarios
Solution Approach 1:
The patent creates a synthesized copy of the background environment that allows virtual repositioning of the frame. Instead of physically moving subjects to the center, the system generates a synthetic background representation that enables the subject to appear centered within an extended field of view, maintaining aesthetic composition without subject movement.
Solution Approach 2:
The patent replaces the mechanical action of moving or repositioning subjects with a computational process. The generative AI system creates a virtual extension of the scene that allows subjects to be framed centrally without requiring them to physically move, substituting mechanical repositioning with computational scene generation.
Data Source
AI summary
An image field extension system and method obtain an input image captured by a camera. The input image depicts an imaged scene. The system and method determine that a crop window, positioned to frame a portion of the input image, extends beyond an edge of the input image and defines a void area within the crop window. The system and method input the input image to a generative artificial intelligence (AI) algorithm. The generative AI algorithm is configured to analyze the input image and generate synthesized image data to fill the void area in the crop window. The generative AI algorithm is configured to generate the synthesized image data based on content in the input image to represent a plausible extension of the imaged scene.