Image Layer Extraction for Occlusion-Aware Regional Annotation
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Solution Overview
Problem
Existing image analysis systems struggle to accurately extract and annotate object-based layers in images, particularly in complex scenes, and often rely on misleading depth estimations that fail to account for occlusion relationships.
Innovation Solution
A multi-stage process involving object detection, box-conditioned segmentation, hierarchical segmentation, and depth estimation is employed to generate ordered segments, which are then annotated with natural language text using a vision-language model, ensuring accurate depth ordering and semantic alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If depth estimation models are used to determine layer ordering, then the process is automated and efficient, but the depth estimations are misleading and fail to account for occlusion relationships
Solution Approach 1:
The patent introduces an intermediary process that uses occlusion boundary detection as a mediator between automated depth estimation and final layer ordering. Instead of relying solely on potentially misleading depth estimation models, the system uses occlusion relationships detected at boundaries as an intermediate step to correct and refine the layer ordering, thereby maintaining automation while improving accuracy.
2Measurement precision
If multiple segmentation models are used to extract object-based layers, then the extraction is comprehensive and accurate, but the system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex task of layer extraction into multiple independent segmentation models that operate on different aspects of the image (e.g., semantic segmentation, instance segmentation). Each model focuses on a specific type of object or property, and their results are combined to achieve comprehensive and accurate layer extraction without requiring a single overly complex system.
3Measurement precision
If occlusion relationships are properly accounted for in layer ordering, then the depth ordering accuracy is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent extracts only the critical occlusion relationship information from the full image data, specifically focusing on occlusion boundaries between objects rather than analyzing all possible pixel relationships. This extraction approach allows the system to account for occlusion relationships and improve depth ordering accuracy while minimizing the additional processing time and computational resources required.
Data Source
AI summary
A system may access an input image. The system may generate a plurality of segments based on one or more segmentation models and the input image, each segment from among the plurality of segments representing a corresponding salient object. The system may generate a depth map based on a depth estimation model. The system may layer the plurality of segments, based on the depth map and border regions between pairs of segments, to generate a plurality of ordered segments. The system may execute a vision-language model to generate a text annotation of the image based on the plurality of ordered segments.