Depth-of-Field Object Extraction for Device-Consistent Segmentation
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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 generate alternative mask, similarity comparison step, and selective extraction step. This multi-stage segmentation approach improves precision by verifying results through multiple operations while maintaining manageable process complexity through systematic organization.
Solution Approach 2:
The patent utilizes parameter changes by varying depth of field settings between the first and second segmentation operations. By changing the depth of field parameter to maximum in one operation and to minimum in another, the system generates different segmentation results that can be compared for consistency, thereby improving segmentation precision across varied device implementations.
2Reliability
If multiple segmentation operations with different depth of field settings are applied, then the segmentation consistency is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by performing the first segmentation operation with maximum depth of field setting before the second operation. This preliminary segmentation generates an initial mask that serves as a reference for subsequent comparison, allowing the system to quickly identify consistent objects across different depth of field settings and reduce overall processing time.
Solution Approach 2:
The patent implements feedback through the similarity determination step where the system compares masks generated from different segmentation operations. This feedback mechanism identifies consistent objects across varying depth of field settings, allowing the system to confirm reliable segmentations quickly and reduce processing time by avoiding redundant computations on inconsistent regions.
3Measurement precision
If depth of field setting is adjusted to maximum for segmentation, then the object boundaries are clearer, but the background detail is lost; if adjusted to minimum, then background detail is preserved, but object boundaries become obscured
Solution Approach 1:
The patent resolves this contradiction by segmenting the depth of field parameter space into distinct operations: one at maximum setting for clear object boundaries and another at minimum setting for background detail preservation. By performing segmentation at multiple parameter points and comparing results, the system recovers both object boundary clarity and background information that would be lost in a single operation.
Solution Approach 2:
The patent applies parameter changes by systematically varying the depth of field setting between maximum and minimum values across different segmentation operations. This parameter exploration allows the system to capture complementary information: clear boundaries from maximum depth of field and background details from minimum depth of field, then integrate both through similarity comparison.
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.