Image Segmentation Morphological Dilation Graph Cut
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Solution Overview
Problem
Existing image segmentation methods are prone to false negatives, especially when dealing with objects that lack spatial coherence or are not homogeneous in color, leading to inaccurate object recognition and potential malfunctions in applications like medical imaging and industrial defect detection.
Innovation Solution
The proposed method involves a morphological dilation phase following a pre-segmentation phase, which increases the information processed by the graph-cut method, taking into account the spatial distribution of image points, thereby reducing false negatives and improving segmentation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If graph-cut method is used directly on pre-segmentation results, then processing speed is maintained, but false negatives increase and segmentation accuracy deteriorates
Solution Approach 1:
The patent applies morphological dilation as a preliminary action before the graph-cut method. By expanding the selected zones in advance, the algorithm ensures that all potential points belonging to the object are included in the input data for graph-cut processing, thereby reducing false negatives and improving segmentation accuracy without significantly increasing overall processing complexity.
2Reliability
If morphological dilation is applied to expand selected zones, then false negatives are reduced and segmentation accuracy improves, but processing time and computational load increase
Solution Approach 1:
The patent applies morphological dilation selectively only to the zones selected during pre-segmentation, rather than processing the entire image. This partial action approach reduces the computational burden compared to full-image processing, while still achieving the goal of reducing false negatives by ensuring complete coverage of object regions.
3Ease of manufacture
If pre-segmentation selects only areas with simple shapes and homogeneous colors, then processing is simplified, but objects with complex spatial distributions are missed
Solution Approach 1:
The patent transforms the spatial distribution parameter by applying morphological dilation, which changes the geometric parameters of selected zones by expanding them. This parameter transformation allows the algorithm to capture objects with complex spatial distributions that would otherwise be missed by simple shape-based selection criteria, while maintaining ease of implementation through standard morphological operations.
Data Source
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AI summary
The present invention relates to an apparatus and a method for image segmentation, which comprises a pre-segmentation phase (P1), wherein a set of points of said image is selected, a morphological expansion phase (P3), wherein the set of points selected during the pre-segmentation phase (P1) is expanded by taking into account the spatial distribution of the points of the selected set in the image, a graph generation phase (SP41), wherein a graph (GR) is generated on the basis of the image and the selected set of points, a graph partitioning phase (SP42), wherein a set of nodes of the graph (GR) is selected on the basis of the characteristics of said graph (GR) and the points corresponding to the selected nodes are added to the selected set of points.