Distance transformations in digital images
Computer Vision, Graphics, and Image Processing
An Efficient Uniform Cost Algorithm Applied to Distance Transforms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast raster scan distance propagation on the discrete rectangular lattice
CVGIP: Image Understanding
Neighborhoods for distance transformations using ordered propagation
CVGIP: Image Understanding
Multidimensional digital boundaries
CVGIP: Graphical Models and Image Processing
On digital distance transforms in three dimensions
Computer Vision and Image Understanding
Directional distance transforms and height field preprocessing for efficient ray tracing
Graphical Models and Image Processing
Multidimensional alignment using the Euclidean distance transform
Graphical Models and Image Processing
A List-Processing Approach to Compute Voronoi Diagrams and the Euclidean Distance Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two fast Euclidean distance transformations in Z2 based on sufficient propagation
Computer Vision and Image Understanding
Distance-ordered homotopic thinning: a skeletonization algorithm for 3D digital images
Computer Vision and Image Understanding
Euclidean ordering via Chamfer distance calculations
Computer Vision and Image Understanding
A Method for Obtaining Skeletons Using a Quasi-Euclidean Distance
Journal of the ACM (JACM)
Fast Euclidean distance transformation by propagation using multiple neighborhoods
Computer Vision and Image Understanding
Efficient computation of the Euclidean distance transform
Computer Vision and Image Understanding
Medial axis for chamfer distances: computing look-up tables and neighbuorhoods in 2D or 3D
Pattern Recognition Letters
IEEE Computer Graphics and Applications
Fast Euclidean Distance Transform using a Graph-Search Algorithm
SIBGRAPI '00 Proceedings of the 13th Brazilian Symposium on Computer Graphics and Image Processing
Optimum design of chamfer distance transforms
IEEE Transactions on Image Processing
Fast approximations for global illumination on dynamic scenes
ACM SIGGRAPH 2006 Courses
Calculating accurate volume of spontaneous intracerebral hematoma
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Pattern Recognition Letters
Tensor scale: An analytic approach with efficient computation and applications
Computer Vision and Image Understanding
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Consider a binary image containing one or more objects. A signed distance transform assigns to each pixel (voxel, etc.), both inside and outside of any objects, the minimum distance from that pixel to the nearest pixel on the border of an object. By convention, the sign of the assigned distance value indicates whether or not the point is within some object (positive) or outside of all objects (negative). Over the years, many different algorithms have been proposed to calculate the distance transform of an image. These algorithms often trade accuracy for efficiency, exhibit varying degrees of conceptual complexity, and some require parallel processors. One algorithm in particular, the Chamfer distance [J. ACM 15 (1968) 600, Comput. Vis. Graph. Image Process. 34 (1986) 344], has been analyzed for accuracy, is relatively efficient, requires no special computing hardware, and is conceptually straightforward. It is understandably, therefore, quite popular and widely used. We present a straightforward modification to the Chamfer distance transform algorithm that allows it to produce more accurate results without increasing the window size. We call this new algorithm Dead Reckoning as it is loosely based on the concept of continual measurements and course correction that was employed by ocean going vessel navigation in the past. We compare Dead Reckoning with a wide variety of other distance transform algorithms based on the Chamfer distance algorithm for both accuracy and speed, and demonstrate that Dead Reckoning produces more accurate results with comparable efficiency.