- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Transmission Characteristics and Fano-like Lineshape in Coupled-slotted Microresonators
摘要： A new unique class of foldable distance transforms of digital images (DT) is introduced, baptized: Fast exact euclidean distance (FEED) transforms. FEED class algorithms calculate the DT starting directly from the de?nition or rather its inverse. The principle of FEED class algorithms is introduced, followed by strategies for their ef?cient implementation. It is shown that FEED class algorithms unite properties of ordered propagation, raster scanning, and independent scanning DT. Moreover, FEED class algorithms shown to have a unique property: they can be tailored to the images under investigation. Benchmarks are conducted on both the Fabbri et al. data set and on a newly developed data set. Three baseline, three approximate, and three state-of-the-art DT algorithms were included, in addition to two implementations of FEED class algorithms. It illustrates that FEED class algorithms i) provide truly exact Euclidean DT; ii) do no suffer from disconnected Voronoi tiles, which is a unique feature for non-parallel but fast DT; iii) outperform any other approximate and exact Euclidean DT with its time complexity OeNT, even after their optimization; and iv) are unequaled in that they can be adapted to the characteristics of the image class at hand.
关键词： Fast exact euclidean distance (FEED),computational complexity,distance transformation,distance transform,benchmark,adaptive,Voronoi
更新于2025-09-23 15:19:57
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Multi-Pulse Fitting of Transition Edge Sensor Signals from a Near-Infrared Continuous-Wave Source
摘要： A new unique class of foldable distance transforms of digital images (DT) is introduced, baptized: Fast exact euclidean distance (FEED) transforms. FEED class algorithms calculate the DT starting directly from the de?nition or rather its inverse. The principle of FEED class algorithms is introduced, followed by strategies for their ef?cient implementation. It is shown that FEED class algorithms unite properties of ordered propagation, raster scanning, and independent scanning DT. Moreover, FEED class algorithms shown to have a unique property: they can be tailored to the images under investigation. Benchmarks are conducted on both the Fabbri et al. data set and on a newly developed data set. Three baseline, three approximate, and three state-of-the-art DT algorithms were included, in addition to two implementations of FEED class algorithms. It illustrates that FEED class algorithms i) provide truly exact Euclidean DT; ii) do no suffer from disconnected Voronoi tiles, which is a unique feature for non-parallel but fast DT; iii) outperform any other approximate and exact Euclidean DT with its time complexity OeNT, even after their optimization; and iv) are unequaled in that they can be adapted to the characteristics of the image class at hand.
关键词： benchmark,computational complexity,Fast exact euclidean distance (FEED),distance transform,Voronoi,distance transformation,adaptive
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Investigation of Radiative Coupling from InGaAsP Quantum Wells for Improving End-of-Life (EOL) Efficiency in Multijunction Solar Cells
摘要： A new unique class of foldable distance transforms of digital images (DT) is introduced, baptized: Fast exact euclidean distance (FEED) transforms. FEED class algorithms calculate the DT starting directly from the de?nition or rather its inverse. The principle of FEED class algorithms is introduced, followed by strategies for their ef?cient implementation. It is shown that FEED class algorithms unite properties of ordered propagation, raster scanning, and independent scanning DT. Moreover, FEED class algorithms shown to have a unique property: they can be tailored to the images under investigation. Benchmarks are conducted on both the Fabbri et al. data set and on a newly developed data set. Three baseline, three approximate, and three state-of-the-art DT algorithms were included, in addition to two implementations of FEED class algorithms. It illustrates that FEED class algorithms i) provide truly exact Euclidean DT; ii) do no suffer from disconnected Voronoi tiles, which is a unique feature for non-parallel but fast DT; iii) outperform any other approximate and exact Euclidean DT with its time complexity OeNT, even after their optimization; and iv) are unequaled in that they can be adapted to the characteristics of the image class at hand.
关键词： Fast exact euclidean distance (FEED),computational complexity,distance transformation,distance transform,benchmark,adaptive,Voronoi
更新于2025-09-19 17:13:59
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Room-temperature power-stabilized narrow-linewidth tunable erbium-doped fiber ring laser based on cascaded Mach-Zehnder interferometers with different free spectral range for strain sensing
摘要： A new unique class of foldable distance transforms of digital images (DT) is introduced, baptized: Fast exact euclidean distance (FEED) transforms. FEED class algorithms calculate the DT starting directly from the de?nition or rather its inverse. The principle of FEED class algorithms is introduced, followed by strategies for their ef?cient implementation. It is shown that FEED class algorithms unite properties of ordered propagation, raster scanning, and independent scanning DT. Moreover, FEED class algorithms shown to have a unique property: they can be tailored to the images under investigation. Benchmarks are conducted on both the Fabbri et al. data set and on a newly developed data set. Three baseline, three approximate, and three state-of-the-art DT algorithms were included, in addition to two implementations of FEED class algorithms. It illustrates that FEED class algorithms i) provide truly exact Euclidean DT; ii) do no suffer from disconnected Voronoi tiles, which is a unique feature for non-parallel but fast DT; iii) outperform any other approximate and exact Euclidean DT with its time complexity OeNT, even after their optimization; and iv) are unequaled in that they can be adapted to the characteristics of the image class at hand.
关键词： benchmark,computational complexity,Fast exact euclidean distance (FEED),distance transform,Voronoi,distance transformation,adaptive
更新于2025-09-19 17:13:59
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Tris-(8-hydroxyquinoline) aluminium thin film as saturable absorber for passively Q-switched erbium-doped fibre laser
摘要： A new unique class of foldable distance transforms of digital images (DT) is introduced, baptized: Fast exact euclidean distance (FEED) transforms. FEED class algorithms calculate the DT starting directly from the de?nition or rather its inverse. The principle of FEED class algorithms is introduced, followed by strategies for their ef?cient implementation. It is shown that FEED class algorithms unite properties of ordered propagation, raster scanning, and independent scanning DT. Moreover, FEED class algorithms shown to have a unique property: they can be tailored to the images under investigation. Benchmarks are conducted on both the Fabbri et al. data set and on a newly developed data set. Three baseline, three approximate, and three state-of-the-art DT algorithms were included, in addition to two implementations of FEED class algorithms. It illustrates that FEED class algorithms i) provide truly exact Euclidean DT; ii) do no suffer from disconnected Voronoi tiles, which is a unique feature for non-parallel but fast DT; iii) outperform any other approximate and exact Euclidean DT with its time complexity OeNT, even after their optimization; and iv) are unequaled in that they can be adapted to the characteristics of the image class at hand.
关键词： Fast exact euclidean distance (FEED),benchmark,distance transform,distance transformation,computational complexity,Voronoi,adaptive
更新于2025-09-19 17:13:59