Skeleton-Based Recognition of Shapes in Images via Longest Path Matching

Bal, Gulce; Diebold, Julia; Chambers, Erin Wolf; Gasparovic, Ellen; Hu, Ruizhen; Leonard, Kathryn; Shaker, Matineh; Wenk, Carola

doi:10.1007/978-3-319-16348-2_6

Cited by 2 publications

(2 citation statements)

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“…In these cases, the east and west neighbors can be accessed using a single bit-shift without taking into account the adjacent integers. Therefore, (4) and ( 5) simplify to n 2 (i, j) = p(i, j) ≪ 1 (7) and…”

Section: Implementation For Small Images or Blocksmentioning

confidence: 99%

“…In [3,4,5] skeletons are used for automatic chromosome analysis (karyotyping), to identify abnormalities in the morphology. Also for computer vision tasks such as object recognition [6,7] and object tracking [8] skeletonization is an important tool. Furthermore, in [9] it is used to reconstruct the 3D path of interventional devices from two orthogonal projection images.…”

Section: Introductionmentioning

confidence: 99%

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Real-time thinning algorithms for 2D and 3D images using GPU processors

Wagner

2019

J Real-Time Image Proc

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The skeletonization of binary images is a common task in many image processing and machine learning applications. Some of these applications require very fast image processing. We propose novel techniques for efficient 2D and 3D thinning of binary images using GPU processors. The algorithms use bit-encoded binary images to process multiple points simultaneously in each thread. The simpleness of a point is determined based on Boolean algebra using only bitwise logical operators. This avoids computationally expensive decoding and encoding steps and allows for additional parallelization. The 2D algorithm is evaluated using a dataset of handwritten characters images. It required an average computation time of 3.53 ns for 32×32 pixels and 0.25 ms for 1024×1024 pixels. This is 52 to 18,380 times faster than a multi-threaded border-parallel algorithm. The 3D algorithm was evaluated based on clinical images of the human vasculature and required computation times of 0.27 ms for 128×128×128 voxels and 20.32 ms for 512×512×512 voxels, which is 32 to 46 times faster than the compared border-sequential algorithm using the same GPU processor. The proposed techniques enable efficient real-time 2D and 3D skeletonization of binary images, which could improve the performance of many existing machine learning applications.

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Section: Implementation For Small Images or Blocksmentioning

confidence: 99%