Topic
Distance transform
About: Distance transform is a research topic. Over the lifetime, 2886 publications have been published within this topic receiving 59481 citations.
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01 Jun 2016TL;DR: This work proposes a more general multi-object segmentation approach that has significantly more applications than standard single star-convex segmentation, e.g. in medical data the authors can separate multiple non-star organs with similar appearances and weak edges, and modified -expansion moves shown to be submodular for multi-hedgehog shapes.
Abstract: Star-convexity prior is popular for interactive single object segmentation due to its simplicity and amenability to binary graph cut optimization. We propose a more general multi-object segmentation approach. Moreover, each object can be constrained by a more descriptive shape prior, "hedgehog". Each hedgehog shape has its surface normals locally constrained by an arbitrary given vector field, e.g. gradient of the user-scribble distance transform. In contrast to star-convexity, the tightness of our normal constraint can be changed giving better control over allowed shapes. For example, looser constraints, i.e. wider cones of allowed normals, give more relaxed hedgehog shapes. On the other hand, the tightest constraint enforces skeleton consistency with the scribbles. In general, hedgehog shapes are more descriptive than a star, which is only a special case corresponding to a radial vector field and weakest tightness. Our approach has significantly more applications than standard single star-convex segmentation, e.g. in medical data we can separate multiple non-star organs with similar appearances and weak edges. Optimization is done by our modified -expansion moves shown to be submodular for multi-hedgehog shapes.
29 citations
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TL;DR: It is shown that the d-MAT approach provides the potential to sculpt/control the MAT form for specialized solution purposes and provides better accuracy than pure thinning methods.
29 citations
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TL;DR: Height distributional distance transform (HDDT) methods are introduced as a new class of methods for height field ray tracing that trace rays through empty cone-like volumes instead of through successive height field cells.
Abstract: Height distributional distance transform (HDDT) methods are introduced as a new class of methods for height field ray tracing. HDDT methods utilize results of height field preprocessing. The preprocessing involves computing a height field transform representing an array of cone-like volumes of empty space above the height field surface that are as wide as possible. There is one cone-like volume balanced on its apex centered above each height field cell. Various height field transforms of this type are developed. Each is based on distance transforms of height field horizontal cross-sections. HDDT methods trace rays through empty cone-like volumes instead of through successive height field cells. The performance of HDDT methods is evaluated experimentally against existing height field ray tracing methods.
29 citations
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05 Dec 1985
TL;DR: In this paper, a gray scale image of a fingerprint composed of a field of pixels is converted to a binary image composed of pixels by a technique which takes into account the directivity of ridge and valley structure.
Abstract: A gray scale image of a fingerprint composed of a field of
pixels is converted to a binary image composed of a field of
pixels by a technique which takes into account the directivity of
the ridge and valley structure. Three intermediate binary im
ages are developed, one by the use of a vertical filter, one by
the use of a horizontal filter and a reference image by the use of
a filter which is not directionally biased. Corresponding sub
fields around each pixel in each of the three images are com
pared. If the subfield for the vertically derived image is closer to
that of the reference image then is the subfield for the horizon
tally derived image, then the binary value for the pixel from the
vertically derived image is used in the final image; and vice
versa. In this fashion, a fourth a final binary image is derived
from a combination of the vertically derived image and horizon
tally derived image which includes the best imagery from each
of those two intermediate images.
29 citations
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TL;DR: A modified distance measure is presented for use with distance transforms of anti-aliased, area sampled grayscale images of arbitrary binary contours and achieves an accuracy comparable to a binary transform on a supersampled image with 16x16 higher resolution, which would require 256 times more computations and memory.
29 citations