Fragment Tree Image Segmentation for Boundary Accuracy
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Solution Overview
Problem
Traditional image segmentation technologies face challenges in accurately grouping pixels into objects with poorly defined boundaries, leading to incorrect merging or splitting of pixels, which affects downstream processes and object representation.
Innovation Solution
The method constructs a fragment tree representation of image data by comparing pixel characteristics to establish relationships between fragments, allowing for the identification and grouping of similar pixels, and enables the creation of object fragments with similar characteristics, addressing the merging/splitting issue and object philosophy problem.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image segmentation methods are used to group pixels into objects, then the process can be completed, but the accuracy of segmentation deteriorates due to incorrect merging or splitting of pixels with poorly defined boundaries
Solution Approach 1:
The patent applies segmentation by dividing the image into hierarchical levels of fragments, where each level represents a different degree of pixel grouping. Instead of directly segmenting the entire image into final objects, the method breaks down the segmentation process into multiple hierarchical levels, allowing progressive refinement from fine-grained to coarse-grained fragments, thereby improving accuracy in handling poorly defined boundaries
Solution Approach 2:
The patent introduces a hierarchical dimension to traditional image segmentation by organizing fragments across multiple levels. This transforms the traditional single-level segmentation into a multi-level hierarchical structure, adding a dimensional aspect that enables more nuanced representation of object boundaries and relationships
2Adaptability or versatility
If pixels are grouped into objects using traditional methods, then object identification can be performed, but the complexity of representing complex objects deteriorates due to merging/splitting issues
Solution Approach 1:
The patent segments the object representation into hierarchical levels, where complex objects are represented as compositions of fragments at different hierarchical levels. This allows flexible representation of complex objects by selecting appropriate levels of fragmentation, reducing the complexity of representing objects with poorly defined boundaries
Solution Approach 2:
The patent introduces dynamic adaptability by allowing the hierarchical fragment structure to be adjusted based on the specific segmentation needs. The system can dynamically select which hierarchical levels to use and how to combine fragments, providing flexibility in representing different types of objects and boundaries
Data Source
AI summary
In general, embodiments of the invention comprise systems and methods for performing image segmentation from image data. According to certain aspects, methods of the invention include constructing a fragment tree representation of image data. An image is segmented into regions of pixels having similar image characteristics, called fragments, each fragment (region) is then compared to its neighbors to determine a graph or hierarchical relationship among all regions. Groups of fragments can be selected by an operator or automatically to define a signature of an object. This signature can then be used to search or traverse a fragment tree any image in order to identify similar objects either automatically or manually.