Topological Derivative Image Segmentation Using Heterogeneous Features
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Solution Overview
Problem
Current TD-based image segmentation methods rely solely on image intensity data, limiting their robustness and effectiveness, especially in noisy environments.
Innovation Solution
The method utilizes heterogeneous image features data, such as brightness, color, edge, gradient, and eigen-vectors, to calculate Topological Derivatives, which are then weighted and summed to segment images into regions, with iterative updates until a cost threshold is met, enhancing segmentation performance and noise robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If only image intensity data is used for TD-based segmentation, then the method is simple to implement, but the segmentation robustness and performance deteriorate in noisy environments
Solution Approach 1:
The patent combines multiple heterogeneous image features (intensity, color, edge, gradient, eigen-vectors) into a unified segmentation framework. Each feature type provides complementary information that enhances robustness against noise and improves segmentation accuracy, directly resolving the contradiction between reliability and complexity by merging multiple data sources.
Solution Approach 2:
The patent creates a composite feature representation by integrating multiple types of image features (intensity, color, edge, gradient, eigen-vectors) analogous to composite materials. This composite approach leverages the strengths of each individual feature type to achieve superior segmentation robustness while maintaining a systematic processing framework.
2Measurement precision
If multiple heterogeneous image features are used, then segmentation accuracy and noise robustness improve, but computational complexity increases
Solution Approach 1:
The patent segments the image analysis process into distinct feature extraction stages (intensity, color, edge, gradient, eigen-vectors) that can be computed independently and then integrated. This segmentation of the processing pipeline allows for optimized computation of each feature type while maintaining overall segmentation accuracy.
Solution Approach 2:
The patent employs parameter changes by applying different mathematical operations and transformations to extract various feature types from the input image. Each feature type uses specific parameters and transformations optimized for that particular feature, allowing accurate extraction while managing computational complexity through targeted processing.
3Manufacturing precision
If iterative updates are performed until cost threshold is met, then segmentation precision improves, but processing time increases
Solution Approach 1:
The patent implements a feedback mechanism through iterative updates where segmentation results are evaluated against a cost function, and the process continues until the cost threshold is met or convergence is achieved. This feedback loop ensures high segmentation precision while providing a clear stopping criterion to manage processing time.
Solution Approach 2:
The patent allows for partial iteration by performing updates until a cost threshold is satisfied rather than requiring a fixed number of iterations. This approach achieves sufficient segmentation precision without unnecessary excessive computing, balancing accuracy and processing time by stopping when adequate results are obtained.
Data Source
AI summary
Provided herein is a topological derivatives (TDs)-based image segmentation method and system using heterogeneous image features data. The image segmentation method according to an embodiment of the present disclosure involves calculating TDs having each of the heterogeneous image features data as an input value, and segmenting an image into a plurality of regions using the calculated TDs. Accordingly, performance may be improved, and robustness against noise may be further improved.