Appearance Shock Grammar for Fast Medial Axis Extraction from Real Images
This work addresses the challenge of efficiently and accurately extracting medial axes from real images, which is important for computer vision applications, but it is incremental as it builds on existing shock graph and appearance-based methods.
The paper tackles the problem of medial axis extraction from complex natural scenes by combining shock graph theory with appearance-based methods, resulting in a method that outperforms the state-of-the-art in high-precision regimes, runs an order of magnitude faster, and eliminates the need for post-processing steps.
We combine ideas from shock graph theory with more recent appearance-based methods for medial axis extraction from complex natural scenes, improving upon the present best unsupervised method, in terms of efficiency and performance. We make the following specific contributions: i) we extend the shock graph representation to the domain of real images, by generalizing the shock type definitions using local, appearance-based criteria; ii) we then use the rules of a Shock Grammar to guide our search for medial points, drastically reducing run time when compared to other methods, which exhaustively consider all points in the input image;iii) we remove the need for typical post-processing steps including thinning, non-maximum suppression, and grouping, by adhering to the Shock Grammar rules while deriving the medial axis solution; iv) finally, we raise some fundamental concerns with the evaluation scheme used in previous work and propose a more appropriate alternative for assessing the performance of medial axis extraction from scenes. Our experiments on the BMAX500 and SK-LARGE datasets demonstrate the effectiveness of our approach. We outperform the present state-of-the-art, excelling particularly in the high-precision regime, while running an order of magnitude faster and requiring no post-processing.