Sequential Operations in Digital Picture Processing

Rosenfeld, Azriel; Pfaltz, John L.

doi:10.1145/321356.321357

Cited by 1,422 publications

(432 citation statements)

References 3 publications

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“…25.7. In this the connected components labelling operation is expanded out, using a standard two-pass labelling algorithm [33]. In the first pass, as the image is scanned, the previously processed neighbourhood of a pixel is examined.…”

Section: Transforming An Algorithm For Hardware Implementationmentioning

confidence: 99%

Implementing Machine Vision Systems Using FPGAs

Bailey¹

2012

Machine Vision Handbook

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Section: Transforming An Algorithm For Hardware Implementationmentioning

confidence: 99%

Implementing Machine Vision Systems Using FPGAs

Bailey¹

2012

Machine Vision Handbook

View full text Add to dashboard Cite

“…For simple distances d 4 and d 8 , the medial axis extraction can be performed by the detection of local maxima in the distance map [1]. Chamfer distances raise a first complication even for small masks as soon as the weights are not unitary.…”

Section: Chamfer Medial Axismentioning

confidence: 99%

“…Algorithms for computing the distance transform are known for various discrete distances [1][2][3][4][5]. In this paper, we will focus on chamfer (or weighted) distances which are defined by a set of weighted vectors described by a mask, P R E P R I N T called the chamfer mask.…”

Section: Introductionmentioning

confidence: 99%

“…It is thus mandatory to describe the covering relation of disks, or at least the transitive reduction of this relation. For simple distances, this knowledge is summarized in a local maximum criterion [1] but a most general method uses lookup tables for that purpose [6].…”

Section: Introductionmentioning

confidence: 99%

“…Distance functions differ by the neighborhoods used to build paths and by the way path lengths are measured. For the simple distance d 4 (denoted by d in [1]), defined in the square grid Z 2 , each pixel has four neighbors located at its top, left, bottom and right edges. Similarly, for distance d 8 …”

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confidence: 99%

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Medial axis lookup table and test neighborhood computation for 3D chamfer norms

Normand

Évenou

2009

Pattern Recognition

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Abstract. Chamfer distances are discrete distances based on the propagation of local distances, or weights, defined in a mask. The medial axis, i.e. the centers of maximal balls (balls which are not contained in any other ball), is a powerful tool for shape representation and analysis. The extraction of maximal disks is performed in the general case by testing the inclusion of a ball in a local neighborhood with covering relations usually represented by lookup tables. The proposed method determines if a mask induces a norm and in this case, computes the lookup tables and the test neighborhood based on geometric properties of the balls of chamfer norms, represented as H-polytopes. The method does not need to repeatedly scan the image space, and improves the computation time of both the test neighborhood detection and the lookup AbstractChamfer distances are discrete distances based on the propagation of local distances, or weights, defined in a mask. The medial axis, i.e. the centers of maximal balls (balls which are not contained in any other ball), is a powerful tool for shape representation and analysis. The extraction of maximal disks is performed in the general case by testing the inclusion of a ball in a local neighborhood with covering relations usually represented by lookup tables. The proposed method determines if a mask induces a norm and in this case, computes the lookup tables and the test neighborhood based on geometric properties of the balls of chamfer norms, represented as H-polytopes. The method does not need to repeatedly scan the image space, and improves the computation time of both the test neighborhood detection and the lookup table computation.

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Volume rendering using seed-filling acceleration: Supporting cut planes by fast reseeding

Oikarinen

Hietala

Jyrkinen

1999

Comput. Aided Surg.

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Objective: Seed filling in view lattice is a method for accelerating volume rendering. Our previously published results on using seed filling had the limitation that they did not support cut planes. Cut planes are one of the main advantages of using volume rendering over surface rendering. In this article we describe the seed-filling acceleration technique and propose two fast reseeding processes that may be performed for each frame. Materials and Methods: The reseeding process performs a connected component search on the cut plane within the volume data set. The amount of processing done by a connected component search algorithm is proportional to N Z , assuming the data set size is N 3. We implemented two connected component search algorithms: one for orthogonal cut planes and another for oblique cut planes. We measured the time taken by the cutting and estimated the effect on the rendering performance when the cut plane was moved interactively. Results: Our measurements show that rendering rates of several frames per second can be achieved with a 266-MHz Pentium II PC, even when the object is interactively modified with cut planes during the rendering. Conclusions: We have shown that the seed-filling algorithm is also applicable in situations where the displayed data is modified interactively using cut planes or objects. Applications in this regard include, for example, computer-aided surgeq systems, where the instrument may be used to interactively perform the simulated cut in the data set. Cornp Aid Surg 4169-181 (1999). 01999 Wiley-Liss, h c .

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Sequential Operations in Digital Picture Processing

Cited by 1,422 publications

References 3 publications

Implementing Machine Vision Systems Using FPGAs

Implementing Machine Vision Systems Using FPGAs

Medial axis lookup table and test neighborhood computation for 3D chamfer norms

Volume rendering using seed-filling acceleration: Supporting cut planes by fast reseeding

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