Ridge Extraction via Depth-First Traversal for Uniform Image Skeletons
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Solution Overview
Problem
Existing image skeletonization systems face challenges in accurately and efficiently extracting precise ridges or skeletons from digital images due to inefficiencies and inaccuracies, such as non-uniform width, gaps, holes, and computational inefficiencies.
Innovation Solution
A ridge extraction process utilizing a depth-first traversal algorithm, including region segmentation, depth transform, and flare suppression, to generate continuous and uniform ridges from digital images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative morphological thinning and successive erosion are used for skeletonization, then some skeletonization functionality is provided, but speed and accuracy in extracting precise ridges deteriorates
Solution Approach 1:
The patent segments the image processing task into distinct functional stages: (1) converting the image to a depth map where ridge pixels have higher depth values, (2) extracting candidate ridge pixels through depth-based criteria, (3) connecting candidates into continuous ridges using graph-based pathfinding, and (4) cleaning up the ridges by removing spurious pixels. This segmentation allows each stage to be optimized independently, achieving both high accuracy and efficiency.
Solution Approach 2:
The patent replaces traditional iterative morphological operations with a depth-map based approach. Instead of repeatedly applying erosion and dilation operations, the system computes a single depth map and extracts ridges through depth-based pixel selection and connection algorithms, significantly reducing computational complexity and improving speed while maintaining accuracy.
2Reliability
If existing skeletonization tools are used, then limited skeletonization functionality is provided, but ridge continuity and uniformity deteriorates
Solution Approach 1:
The patent implements feedback mechanisms at multiple stages: (1) the depth map computation provides feedback about pixel depths to guide ridge extraction, (2) the graph-based connection algorithm uses feedback from depth comparisons to select optimal path candidates, and (3) the final cleaning stage uses feedback from connectivity analysis to remove spurious pixels. This feedback-driven approach ensures continuous and uniform ridges while maintaining reliability.
Solution Approach 2:
The patent changes the fundamental parameter used for ridge extraction from traditional binary image data to depth values. By computing a depth map where each pixel's value represents its distance from the foreground, the system can extract ridges based on depth gradients, ensuring uniform width and continuity. The depth parameter naturally guides the ridge extraction process to produce geometrically accurate results.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for detecting and tracing ridges in digital images using a ridge detection process that includes a depth-first traversal algorithm. For example, the disclosed systems determine, for a digital image, a distance transform defining distances of foreground pixels from background pixels depicted in the digital image. In some embodiments, the disclosed systems generate a preliminary ridge for the digital image by using a depth-first traversal starting at an initial pixel indicated by the distance transform and growing based on local distance transform comparisons among the foreground pixels of the digital image. In certain embodiments, the disclosed systems also generate a final ridge from the preliminary ridge by removing spurious pixels included in ridge flares.