Object-Based Boundary Refinement for Overlapped Image Segmentation
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Solution Overview
Problem
Existing image object segmentation methods struggle with accurately defining boundaries of overlapping objects, leading to incorrect measurements and compromised analysis in applications like cell and tissue analysis, where objects are often overlapped or in aggregates.
Innovation Solution
An object-based boundary refinement method that performs directional edge enhancement, radial detection, and final shaping around the initial object region of interest to generate accurate boundary definitions for overlapped object regions, allowing for specific boundary detection even when shared by multiple objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If general segmentation procedures are applied to images containing overlapping objects, then the segmentation process is simple and fast, but the boundary accuracy of overlapped objects deteriorates and cannot be correctly identified
Solution Approach 1:
The invention segments the image processing task into distinct stages: initial object region detection, refinement zone definition, and boundary refinement. This multi-stage segmentation approach allows the system to first identify approximate object locations quickly, then focus computational resources on refining boundaries only in relevant regions, thus maintaining speed while improving accuracy.
Solution Approach 2:
The invention applies different processing qualities to different regions of the image. Refinement zones are defined around detected object regions where high-quality boundary refinement is applied, while other regions receive minimal processing. This local quality approach ensures accurate boundary detection for overlapped objects without unnecessarily processing the entire image at high resolution.
2Measurement precision
If boundary refinement is applied to accurately define overlapped object regions, then the measurement precision improves, but the device complexity and processing time increase
Solution Approach 1:
The invention performs preliminary object region detection before boundary refinement. By first identifying approximate object locations and defining refinement zones, the system prepares the data structure and processing regions in advance, which simplifies the subsequent boundary refinement process and reduces overall computational complexity.
Solution Approach 2:
The invention transforms the boundary refinement problem from a two-dimensional image space to a polar coordinate system centered on detected object regions. This dimensional transformation simplifies the mathematical operations required for radial boundary detection and makes the processing more efficient despite the increased accuracy requirements.
3Stability of the object's composition
If traditional segmentation rules are enforced to create uniform regions, then region homogeneity improves, but boundary accuracy deteriorates because strict uniformity causes regions to merge and boundaries to be lost
Solution Approach 1:
The invention applies different uniformity requirements to different parts of the segmentation process. Initial object region detection uses relaxed uniformity criteria to identify candidate regions, while boundary refinement applies strict local uniformity only at the boundaries themselves. This allows regions to maintain sufficient homogeneity for identification while preserving accurate boundary separation even for overlapped objects.
Data Source
AI summary
An object based boundary refinement method for object segmentation in digital images receives an image and a single initial object region of interest and performs refinement zone definition using the initial object regions of interest to generate refinement zones output. A directional edge enhancement is performed using the input image and the refinement zones to generate directional enhanced region of interest output. A radial detection is performed using the input image the refinement zones and the directional enhanced region of interest to generate radial detection mask output. In addition, a final shaping is performed using the radial detection mask having single object region output.A directional edge enhancement method determining pixel specific edge contrast enhancement direction according to the object structure direction near the pixel consists receives an image and refinement zones and performs 1D horizontal distance transform and 1D vertical distance transform using the refinement zones to generate horizontal distance map and vertical distance map outputs. A neighboring direction determination is performed using the horizontal distance map and the vertical distance map to generate neighboring image output. In addition, a directional edge contrast calculation using the neighboring image and input image having directional enhanced region of interest output.