Image Object Extraction Using Depth-of-Field Segmentation Comparison
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Solution Overview
Problem
Existing methods for extracting objects from images with depth of field settings face inconsistencies due to varied device implementations and depth of field interpretations, leading to inaccurate and inconsistent segmentation outcomes.
Innovation Solution
A method involving two segmentation operations with maximum and minimum depth of field settings, followed by a similarity index comparison to determine precise object extraction, with optional user feedback and iterative adjustments to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single segmentation operation is applied to extract objects from images with depth of field settings, then the processing speed is fast, but the segmentation precision and consistency are poor due to varied device implementations and depth of field interpretations
Solution Approach 1:
The patent applies segmentation by dividing the object extraction process into multiple discrete steps: first segmentation operation to generate initial mask, second segmentation operation to refine mask, and final extraction step. This multi-stage segmentation approach improves precision by addressing different aspects of the segmentation challenge at each stage, while managing complexity through systematic breakdown of the process.
Solution Approach 2:
The patent implements preliminary action by performing a first segmentation operation before the second segmentation operation. The first segmentation generates an initial mask that serves as input for the second segmentation, preparing the data in advance to improve the accuracy of the final extraction. This preliminary processing step addresses device variations and depth of field effects before the final refinement stage.
2Measurement precision
If multiple segmentation operations with different depth of field settings are applied, then the object extraction accuracy is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent segments the processing workflow into distinct operations that can be executed efficiently. By dividing the complex task of handling varied depth of field settings into separate segmentation operations, each optimized for specific conditions, the system achieves high accuracy without requiring all operations to run simultaneously, thus managing processing time through sequential execution.
Solution Approach 2:
The patent implements feedback by using the output of the first segmentation operation as input for the second segmentation operation. The system evaluates the results of each segmentation stage and uses this information to guide subsequent processing decisions, allowing it to achieve high accuracy while potentially skipping unnecessary processing steps when the first segmentation already provides sufficient results.
3Adaptability or versatility
If depth of field settings are varied to handle different device implementations, then the adaptability across devices is improved, but the segmentation consistency deteriorates due to confounding effects on object boundaries
Solution Approach 1:
The patent uses segmentation to separate the adaptation to different devices from the actual object extraction. The first segmentation operation handles device-specific variations and depth of field effects, creating a standardized intermediate representation that the second segmentation operation then processes consistently across all devices. This segmented approach maintains both adaptability and consistency.
Solution Approach 2:
The patent introduces an intermediary step in the form of the first segmentation operation that mediates between varied device implementations and the final segmentation process. This intermediary transformation converts diverse input formats and depth of field settings into a standardized intermediate representation, ensuring consistent processing in subsequent stages while maintaining compatibility with different devices.
Data Source
AI summary
Systems and methods are provided for object extraction from images influenced by depth of field settings. Through control circuitry, an image subjected to a prior segmentation operation is acquired. A subsequent segmentation operation is performed, modulating the depth of field setting to its extreme values, producing two distinct segmented images. From these, an in-focus object is derived, forming delineated representations. A similarity index between representations is computed. If this index exceeds a specified threshold, the in-focus object is extracted from the original image using the control circuitry.