Authors:
Matthew van der Zwan
1
;
Yuri Meiburg
1
and
Alexandru Telea
2
Affiliations:
1
University of Groningen, Netherlands
;
2
University of Groningen and University of Medicine and Pharmacy Carol Davila, Netherlands
Keyword(s):
Medial Axes, Image Segmentation, Shape Analysis.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Segmentation and Grouping
;
Shape Representation and Matching
;
Visual Attention and Image Saliency
Abstract:
We present dense medial descriptors, a new technique which generalizes the well-known medial axes to encode
and manipulate whole 2D grayvalue images, rather than binary shapes. To compute our descriptors, we
first reduce an image to a set of threshold-sets in luminance space. Next, we compute a simplified representation
of each threshold-set using a noise-resistant medial axis transform. Finally, we use these medial axis
transforms to perform a range of operations on the input image, from perfect reconstruction to segmentation,
simplification, and artistic effects. Our pipeline can robustly handle any 2D grayscale image, is easy to use,
and allows an efficient CPU or GPU-based implementation. We demonstrate our dense medial descriptors
with several image-processing applications.