A Euclidean distance transform using grayscale morphology decomposition

Huang, Chao; Mitchell, O. Robert

doi:10.1109/34.277600

Cited by 95 publications

(45 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Another kind of exact DT algorithm repeatedly applies a mask on every pixel until no pixel has changed its value [Yamada 1984;Shih and Mitchell 1992;Huang and Mitchell 1994]. This kind of algorithm may be executed on all the pixels in parallel, but is not computationally efficient.…”

Section: Distance Transformsmentioning

confidence: 99%

Jump flooding in GPU with applications to Voronoi diagram and distance transform

Rong

Tan

2006

Proceedings of the 2006 Symposium on Interactive 3D Graphics and Games - SI3D '06

171

154

View full text Add to dashboard Cite

Figure 1: With the eight seeds (highlighted and shown in different colors) in a 64×64 grid on the leftmost picture, the progress of each round of the jump flooding algorithm is shown in the other six pictures, with the rightmost being the computed Voronoi diagram. AbstractThis paper studies jump flooding as an algorithmic paradigm in the general purpose computation with GPU. As an example application of jump flooding, the paper discusses a constant time algorithm on GPU to compute an approximation to the Voronoi diagram of a given set of seeds in a 2D grid. The errors due to the differences between the approximation and the actual Voronoi diagram are hardly noticeable to the naked eye in all our experiments. The same approach can also compute in constant time an approximation to the distance transform of a set of seeds in a 2D grid. In practice, such constant time algorithm is useful to many interactive applications involving, for example, rendering and image processing. Besides the experimental evidences, this paper also confirms quantitatively the effectiveness of jump flooding by analyzing the occurrences of errors. The analysis is a showcase of insights to the jump flooding paradigm, and may be of independent interests to other applications of jump flooding.

show abstract

Section: Distance Transformsmentioning

confidence: 99%

Jump flooding in GPU with applications to Voronoi diagram and distance transform

Rong

Tan

2006

Proceedings of the 2006 Symposium on Interactive 3D Graphics and Games - SI3D '06

171

154

View full text Add to dashboard Cite

show abstract

“…They can be divided into three categories, according to the order used to scan the pixels. First, parallel algorithms were presented by Yamada [4], Mitchell [16,18], or Embrechts [20], but they cannot be efficiently implemented on a general-purpose computer. Second, raster scanning algorithms were proposed by Mullikin [12] or Saito [19].…”

Section: D( P) = Min{dist( P Q) Q ∈ O}mentioning

confidence: 99%

Fast Euclidean Distance Transformation by Propagation Using Multiple Neighborhoods

Cuisenaire

Macq

1999

Computer Vision and Image Understanding

127

108

View full text Add to dashboard Cite

We propose a new exact Euclidean distance transformation (DT) by propagation, using bucket sorting. A fast but approximate DT is first computed using a coarse neighborhood. A sequence of larger neighborhoods is then used to gradually improve this approximation. Computations are kept short by restricting the use of these large neighborhoods to the tile borders in the Voronoi diagram of the image. We assess the computational cost of this new algorithm and show that it is both smaller and less image-dependent than all other DTs recently proposed. Contrary to all other propagation DTs, it appears to remain o(n 2 ) even in the worst-case scenario.

show abstract

“…[32], the author gives a DT fast computation for the euclidean distance based on masks. More recently, several methods have been presented to give an exact and fast computation of the euclidean DT in a linear time [33,34]. The case of the chessboard distance has also been studied in Ref.…”

Section: Definition 16 (Dt) Letmentioning

confidence: 99%

Binary-image comparison with local-dissimilarity quantification

Baudrier

Nicolier

Millon

et al. 2008

Pattern Recognition

View full text Add to dashboard Cite

In this paper, we present a method for binary image comparison. For binary images, intensity information is poor and shape extraction is often difficult. Therefore binary images have to be compared without using feature extraction. Due to the fact that different scene patterns can be present in the images, we propose a modified Hausdorff distance (HD) locally measured in an adaptive way. The resulting set of measures is richer than a single global measure. The local HD measures result in a local-dissimilarity map (LDMap) including the dissimilarity spatial layout. A classification of the images in function of their similarity is carried out on the LDMaps using a support vector machine. The proposed method is tested on a medieval illustration database and compared with other methods to show its efficiency. For the first approach, conspicuous features must be captured in the signature of each image in order to be as discriminating as possible in some user defined way [6]. The choice of the signature attributes is not always easy and depends on the processed images [5,1]. In case that there are several patterns in the images, a segmentation is often necessary to compare locally the attributes. For binary images, object shapes cannot always be precisely identified, so it is difficult to find the features related to shapes. Moreover, the texture attribute is also difficult to extract (binary images are not always * Corresponding author. textured), and the color attribute is poor (only black and white colors). Thus, the second approach, a straight image comparison, seems adapted in the case of binary images. In our work, the measure is windowed and the window size is adjusted so as to measure exclusively the local dissimilarity. The obtained result is composed of a set of local measures covering the image. The following state of the art gives a general presentation of dissimilarity measures and tends to choose the Hausdorff distance (HD). The computation of windowed measure all over the images is time consuming, nevertheless, for the HD, the algorithm comes down to a formula based on the distance transform (DT) of each image which accelerates the computation.The paper is organized as follows: firstly we present an overview of the image retrieval in Section 1.1 and then focus on the HD in Section 1.2. Secondly the notion of local HD is introduced and its properties are exposed in Sections 2 and 3. This allows us to determine automatically the size of the local HD's window in function of the dissimilarity, as shown in Section 4. Then it is shown how the algorithm may be reduced to a formula based on the DT. The map of local HD defined without parameters is presented in Sections 4 and 5. Finally, qualitative results and an application to image classification are presented in Section 6 before concluding.Please cite this article as:Ḃaudrier, et al., Binary-image comparison with local-dissimilarity quantification, Pattern Recognition (2007),

show abstract

A Euclidean distance transform using grayscale morphology decomposition

Cited by 95 publications

References 13 publications

Jump flooding in GPU with applications to Voronoi diagram and distance transform

Jump flooding in GPU with applications to Voronoi diagram and distance transform

Fast Euclidean Distance Transformation by Propagation Using Multiple Neighborhoods

Binary-image comparison with local-dissimilarity quantification

Contact Info

Product

Resources

About