Computation of geometric properties from the medial axis transform in O (n log n) time
Computer Vision, Graphics, and Image Processing
Distance transformations in digital images
Computer Vision, Graphics, and Image Processing
Parallel computation of geometric properties from the medial axis transform
Computer Vision, Graphics, and Image Processing
Optimal algorithms for rectangle problems on a mesh-connected computer
Journal of Parallel and Distributed Computing
A note on “distance transformations in arbitrary dimensions"
Computer Vision, Graphics, and Image Processing
Another comment on “a note on distance transformation in digital images”
CVGIP: Image Understanding
Ridge points in Euclidean distance maps
Pattern Recognition Letters
Serial and Parallel Algorithms for the Medial Axis Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast algorithm for Euclidean distance maps of a 2-D binary image
Information Processing Letters
Digital Picture Processing
A Parallel Algorithm for Weighted Distance Transforms
IPPS '97 Proceedings of the 11th International Symposium on Parallel Processing
Parallel Computation of the Euclidean Distance Transform on a Three-Dimensional Image Array
IEEE Transactions on Parallel and Distributed Systems
3D Block-Based Medial Axis Transform and Chessboard Distance Transform on the CREW PRAM
ICA3PP '08 Proceedings of the 8th international conference on Algorithms and Architectures for Parallel Processing
A parallel algorithm for medial axis transformation
ISPA'03 Proceedings of the 2003 international conference on Parallel and distributed processing and applications
3D block-based medial axis transform and chessboard distance transform based on dominance
Image and Vision Computing
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The distance transform (DT) and the medial axis transform (MAT) are two image computation tools used to extract information about the shape and position of foreground pixels relative to each other. Extensively applications of these two transforms are used in the fields of computer vision and image processing, such as expanding/shrinking, thinning, computing the shape factor, etc. There are many different DTs based on different distance metrics. Finding the DT with respect to the Euclidean distance metric is easier to use, but rather time-consuming, so many approximate Euclidean DTs (EDTs) are also widely used in the computer vision and image processing fields. The chessboard DT (CDT) is one kind of DT, which converts an image based on the chessboard distance metric. Traditionally, the MAT and the CDT have usually been viewed as two completely different image computation problems. In this paper, we first point out that the processes to find the CDT and the MAT are almost identical, i.e. the two transforms are interchangeable through the proposed algorithms, so that a MAT can be found by utilizing a CDT algorithm and vice versa.