Depth Map Enhancement via Face Detection Segmentation
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Solution Overview
Problem
Current 2D to 3D conversion techniques fail to produce consistent and visually appealing depth maps for stereoscopic entertainment, as they do not effectively target and analyze specific objects like faces, leading to inaccurate depth ranges and poor stereoscopic effects.
Innovation Solution
A method that uses a face detector to analyze 2D images, derive a depth model based on face position and geometry, and combine it with existing depth maps to enhance the depth map, improving the stereoscopic effect by targeting faces and other objects in the scene.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If automated 2D to 3D conversion algorithms operate on general image characteristics without object-specific analysis, then the processing is simpler and faster, but the depth map quality and stereoscopic effect are poor
Solution Approach 1:
The patent segments the image processing task by first detecting specific objects (faces) and then applying specialized depth estimation algorithms to those detected objects. This segmentation allows the system to focus computational resources on critical regions, improving depth map quality without requiring complex processing of the entire image.
Solution Approach 2:
The patent performs preliminary object detection (face detection) before applying depth estimation algorithms. By identifying faces and other objects in advance, the system can prepare appropriate depth models and parameters specific to each object type, thereby improving the overall depth map quality while maintaining a structured processing pipeline.
2Loss of information
If multiple depth fusion techniques are combined, then more depth information is available, but the depth range consistency deteriorates
Solution Approach 1:
The patent applies different depth estimation strategies to different objects based on their local characteristics. For example, faces receive specialized depth modeling that considers facial geometry and typical depth relationships, while other objects receive appropriate treatment based on their specific properties. This local quality approach maintains depth range consistency while preserving complete depth information.
Solution Approach 2:
The patent adjusts depth estimation parameters based on the detected object type. By changing parameters such as depth scaling, offset, and confidence weights according to whether an object is a face, person, or other entity, the system maintains consistent depth ranges across multiple fusion techniques while preserving comprehensive depth information.
3Manufacturing precision
If face detection and depth model derivation are added to the processing pipeline, then the stereoscopic effect is improved, but the processing time and computational requirements increase
Solution Approach 1:
The patent segments the image into regions of interest (detected faces and objects) and applies computationally intensive depth modeling only to these segments. This allows the system to achieve high stereoscopic effect quality for critical elements while keeping the overall processing time manageable by skipping detailed analysis in non-critical regions.
Solution Approach 2:
The patent applies partial action by focusing depth enhancement efforts on detected faces and key objects rather than processing the entire image uniformly. This selective approach delivers improved stereoscopic effect where it matters most while reducing overall computational burden and processing time compared to exhaustive full-image processing.
Data Source
AI summary
A system for enhancing a depth map associated with a 2D image wherein a face detector analyzes the 2D image to determine a position of a face in the 2D image, the system utilizes the position to derive a depth model; and the depth model is combined with the depth map to generate an enhanced depth map.