Efficient Euclidean distance transform using perpendicular bisector segmentation

Wang, Jun; Tan, Ying

doi:10.1109/cvpr.2011.5995644

Cited by 8 publications

(2 citation statements)

References 17 publications

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“…Later, Maurer et al [22] proposed a linear time sequential algorithm for a binary image in arbitrary dimension using dimensionality reduction strategy. Recently, Wang and Tan [23] proposed a method in which perpendicular bisector line is used to improve the locality of computation. They also extended it to an arbitrary dimension [24].…”

Section: A Exact Edtmentioning

confidence: 99%

A Work Efficient Parallel Algorithm for Exact Euclidean Distance Transform

Manduhu

Jones

2019

IEEE Trans. on Image Process.

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A fully-parallelized work-time optimal algorithm is presented for computing the exact Euclidean Distance Transform (EDT) of a 2D binary image with the size of n × n. Unlike existing PRAM and other algorithms, this algorithm is suitable for implementation on modern SIMD architectures such as GPUs. As a fundamental operation of 2D EDT, 1D EDT is efficiently parallelized first. Specifically, the GPU algorithm for the 1D EDT, which uses CUDA binary functions such as ballot(), ffs(), clz() and shfl(), runs in O(log32n) time and performs O(n) work. Using the 1D EDT as a fundamental operation, the fullyparallelized work-time optimal 2D EDT algorithm is designed. This algorithm consists of three steps. Step 1 of the algorithm runs in O(log32n) time and performs O(N) (N =n 2) of total work on GPU. Step 2 performs O(N) of total work and has an expected time complexity of O(logn) on GPU. Step 3 runs in O(log32n) time and performs O(N) of total work on GPU. As far as we know, this algorithm is the first fully-parallelized and realized work-time optimal algorithm for GPUs. Experimental results show that this algorithm outperforms prior state-of-theart GPU algorithms. Index Terms-work-time optimal parallel algorithm, SIMD architecture, 1D EDT, 2D EDT, binary operations on GPU.

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Section: A Exact Edtmentioning

confidence: 99%

A Work Efficient Parallel Algorithm for Exact Euclidean Distance Transform

Manduhu

Jones

2019

IEEE Trans. on Image Process.

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“…Therefore, DBTAs decompose the thinning in two main steps in which the EDT is the most time-consuming. Several researchers have put their efforts to improve the performance of EDT algorithms [8], for the thinning problem as well as for other applications. Some examples of DBTAs can be found in [9] and [10].…”

Section: Related Workmentioning

confidence: 99%

Hardware accelerator for fast image/video thinning

Davalle

Carnevale

Saponara

et al. 2014

2014 IEEE International Conference on Imaging Systems and Techniques (IST) Proceedings

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Image thinning algorithms are widely used in image processing to simplify elaboration preserving geometrical features. Standard approaches are based on iterative methods and on distance transforms. Both techniques are well known to be computationally intensive. In this work we propose a parallel, fast and flexible hardware architecture for image thinning to achieve real-time performance. The test case is the 720 × 576 PAL standard video at 25 frame per second (fps). Synthesis was performed for a Stratix II FPGA EP2S30 and for a standard cell 65nm CMOS technology. The former showed a usage of 4%slices and 1% registers, the latter gave an occupation of 5 kgates for the processing core. The execution time for one frame was 0.03 s on the FPGA and 0.009 s on the 65 nm, resulting in a maximum throughput of 33 fps and 111 fps, respectively

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