A high-speed algorithm for propagation-type labeling based on block sorting of runs in binary images

SUMMARYThis paper proposes a new first-scan method for two-scan labeling algorithms. In the first scan, our proposed method first scans every fourth image line, and processes the scan line and its two neighbor lines. Then, it processes the remaining lines from top to bottom one by one. Our method decreases the average number of times that must be checked to process a foreground pixel will; thus, the efficiency of labeling can be improved.

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“…These algorithms access an image in an irregular way. There are run-based algorithms [2], [16] and contour-tracing algorithms [3], [14].…”

Section: Introductionmentioning

confidence: 99%

A New First-Scan Method for Two-Scan Labeling Algorithms

Chao

Suzuki

2012

IEICE Trans. Inf. & Syst.

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“…In the example shown in Figure 3, there are 5 such query lines: the first one contains point 4 alone, the second one contains point 8, the third one contains points 11 through 13, the fourth one contains point 15 through 17, and the fifth one contains points 18 through 24. Earlier work demonstrated the effectiveness of this approach with empirical evidences (Shima et al 1990, Wu et al 2003, Sinha et al 2009). Because our approach for finding points naturally groups adjacent points together, we can effectively produce query lines instead of lists of data points.…”

Section: Finding Regions Of Interestmentioning

confidence: 99%

Finding regions of interest on toroidal meshes

Sinha

Jones

et al. 2011

Comput. Sci. Disc.

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Fusion promises to provide clean and safe energy, and a considerable amount of research effort is underway to turn this aspiration into reality. This work focuses on a building block for analyzing data produced from the simulation of microturbulence in magnetic confinement fusion devices: the task of efficiently extracting regions of interest. Like many other simulations where a large amount of data are produced, the careful study of "interesting" parts of the data is critical to gain understanding. In this paper, we present an efficient approach for finding these regions of interest. Our approach takes full advantage of the underlying mesh structure in magnetic coordinates to produce a compact representation of the mesh points inside the regions and an efficient connected component labeling algorithm for constructing regions from points. This approach scales linearly with the surface area of the regions of interest instead of the volume as shown with both computational complexity analysis and experimental measurements. Furthermore, this new approach is 100s of times faster than a recently published method based on Cartesian coordinates.

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“…15 Our version of Chang's algorithm overwrites the input image array with labels and does not use any additional storage. Our block scan algorithm builds blocks from input image and uses the array based Union-Find.…”

Section: Two More Modified Algorithmsmentioning

confidence: 99%

“…We also implemented a labeling algorithm that first connects pixels into blocks. 15 On the set of random images we tested, this block scan approach does not outperform the pixel based scans because there are very few large blocks.…”

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confidence: 99%

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Optimizing connected component labeling algorithms

2005

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This paper presents two new strategies that can be used to greatly improve the speed of connected component labeling algorithms. To assign a label to a new object, most connected component labeling algorithms use a scanning step that examines some of its neighbors. The first strategy exploits the dependencies among them to reduce the number of neighbors examined. When considering 8-connected components in a 2D image, this can reduce the number of neighbors examined from four to one in many cases. The second strategy uses an array to store the equivalence information among the labels. This replaces the pointer based rooted trees used to store the same equivalence information. It reduces the memory required and also produces consecutive final labels. Using an array instead of the pointer based rooted trees speeds up the connected component labeling algorithms by a factor of 5 ∼ 100 in our tests on random binary images.

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A high-speed algorithm for propagation-type labeling based on block sorting of runs in binary images

Cited by 43 publications

References 5 publications

A New First-Scan Method for Two-Scan Labeling Algorithms

A New First-Scan Method for Two-Scan Labeling Algorithms

Finding regions of interest on toroidal meshes

Optimizing connected component labeling algorithms

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