Saliency Improvement in Feature-Poor Surgical Environments Using Local Laplacian of Specified Histograms

Navigation in endoscopic environments requires an accurate and robust localisation system. A key challenge in such environments is the paucity of visual features that hinders accurate tracking. This article examines the performance of three image enhancement techniques for tracking under such featur...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Artur Banach [verfasserIn] Mario Strydom [verfasserIn] Anjali Jaiprakash [verfasserIn] Gustavo Carneiro [verfasserIn] Cameron Brown [verfasserIn] Ross Crawford [verfasserIn] Aaron Mcfadyen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Image enhancement histogram specification local Laplacian filtering minimally invasive surgery

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 213378-213388
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:213378-213388

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2020.3040187

Katalog-ID:	DOAJ06243649X

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spelling	10.1109/ACCESS.2020.3040187 doi (DE-627)DOAJ06243649X (DE-599)DOAJ0f1950428ee94b2ba72b7f5ff6a2b811 DE-627 ger DE-627 rakwb eng TK1-9971 Artur Banach verfasserin aut Saliency Improvement in Feature-Poor Surgical Environments Using Local Laplacian of Specified Histograms 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Navigation in endoscopic environments requires an accurate and robust localisation system. A key challenge in such environments is the paucity of visual features that hinders accurate tracking. This article examines the performance of three image enhancement techniques for tracking under such feature-poor conditions including Contrast Limited Adaptive Histogram Specification (CLAHS), Fast Local Laplacian Filtering (LLAP) and a new combination of the two coined Local Laplacian of Specified Histograms (LLSH). Two cadaveric knee arthroscopic datasets and an underwater seabed inspection dataset are used for the analysis, where results are interpreted by defining visual saliency as the number of correctly matched key-point (SIFT and SURF) features. Experimental results show a significant improvement in contrast quality and feature matching performance when image enhancement techniques are used. Results also demonstrate the LLSHs ability to vastly improve SURF tracking performance indicating more than 87% of successfully matched frames. A comparative analysis provides some important insights useful in the design of vision-based navigation for autonomous agents in feature-poor environments. Image enhancement histogram specification local Laplacian filtering minimally invasive surgery Electrical engineering. Electronics. Nuclear engineering Mario Strydom verfasserin aut Anjali Jaiprakash verfasserin aut Gustavo Carneiro verfasserin aut Cameron Brown verfasserin aut Ross Crawford verfasserin aut Aaron Mcfadyen verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 213378-213388 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:213378-213388 https://doi.org/10.1109/ACCESS.2020.3040187 kostenfrei https://doaj.org/article/0f1950428ee94b2ba72b7f5ff6a2b811 kostenfrei https://ieeexplore.ieee.org/document/9269332/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 213378-213388
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allfieldsGer	10.1109/ACCESS.2020.3040187 doi (DE-627)DOAJ06243649X (DE-599)DOAJ0f1950428ee94b2ba72b7f5ff6a2b811 DE-627 ger DE-627 rakwb eng TK1-9971 Artur Banach verfasserin aut Saliency Improvement in Feature-Poor Surgical Environments Using Local Laplacian of Specified Histograms 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Navigation in endoscopic environments requires an accurate and robust localisation system. A key challenge in such environments is the paucity of visual features that hinders accurate tracking. This article examines the performance of three image enhancement techniques for tracking under such feature-poor conditions including Contrast Limited Adaptive Histogram Specification (CLAHS), Fast Local Laplacian Filtering (LLAP) and a new combination of the two coined Local Laplacian of Specified Histograms (LLSH). Two cadaveric knee arthroscopic datasets and an underwater seabed inspection dataset are used for the analysis, where results are interpreted by defining visual saliency as the number of correctly matched key-point (SIFT and SURF) features. Experimental results show a significant improvement in contrast quality and feature matching performance when image enhancement techniques are used. Results also demonstrate the LLSHs ability to vastly improve SURF tracking performance indicating more than 87% of successfully matched frames. A comparative analysis provides some important insights useful in the design of vision-based navigation for autonomous agents in feature-poor environments. Image enhancement histogram specification local Laplacian filtering minimally invasive surgery Electrical engineering. Electronics. Nuclear engineering Mario Strydom verfasserin aut Anjali Jaiprakash verfasserin aut Gustavo Carneiro verfasserin aut Cameron Brown verfasserin aut Ross Crawford verfasserin aut Aaron Mcfadyen verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 213378-213388 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:213378-213388 https://doi.org/10.1109/ACCESS.2020.3040187 kostenfrei https://doaj.org/article/0f1950428ee94b2ba72b7f5ff6a2b811 kostenfrei https://ieeexplore.ieee.org/document/9269332/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 213378-213388
allfieldsSound	10.1109/ACCESS.2020.3040187 doi (DE-627)DOAJ06243649X (DE-599)DOAJ0f1950428ee94b2ba72b7f5ff6a2b811 DE-627 ger DE-627 rakwb eng TK1-9971 Artur Banach verfasserin aut Saliency Improvement in Feature-Poor Surgical Environments Using Local Laplacian of Specified Histograms 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Navigation in endoscopic environments requires an accurate and robust localisation system. A key challenge in such environments is the paucity of visual features that hinders accurate tracking. This article examines the performance of three image enhancement techniques for tracking under such feature-poor conditions including Contrast Limited Adaptive Histogram Specification (CLAHS), Fast Local Laplacian Filtering (LLAP) and a new combination of the two coined Local Laplacian of Specified Histograms (LLSH). Two cadaveric knee arthroscopic datasets and an underwater seabed inspection dataset are used for the analysis, where results are interpreted by defining visual saliency as the number of correctly matched key-point (SIFT and SURF) features. Experimental results show a significant improvement in contrast quality and feature matching performance when image enhancement techniques are used. Results also demonstrate the LLSHs ability to vastly improve SURF tracking performance indicating more than 87% of successfully matched frames. A comparative analysis provides some important insights useful in the design of vision-based navigation for autonomous agents in feature-poor environments. Image enhancement histogram specification local Laplacian filtering minimally invasive surgery Electrical engineering. Electronics. Nuclear engineering Mario Strydom verfasserin aut Anjali Jaiprakash verfasserin aut Gustavo Carneiro verfasserin aut Cameron Brown verfasserin aut Ross Crawford verfasserin aut Aaron Mcfadyen verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 213378-213388 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:213378-213388 https://doi.org/10.1109/ACCESS.2020.3040187 kostenfrei https://doaj.org/article/0f1950428ee94b2ba72b7f5ff6a2b811 kostenfrei https://ieeexplore.ieee.org/document/9269332/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 213378-213388
language	English
source	In IEEE Access 8(2020), Seite 213378-213388 volume:8 year:2020 pages:213378-213388
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topic_facet	Image enhancement histogram specification local Laplacian filtering minimally invasive surgery Electrical engineering. Electronics. Nuclear engineering
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authorswithroles_txt_mv	Artur Banach @@aut@@ Mario Strydom @@aut@@ Anjali Jaiprakash @@aut@@ Gustavo Carneiro @@aut@@ Cameron Brown @@aut@@ Ross Crawford @@aut@@ Aaron Mcfadyen @@aut@@
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callnumber-first	T - Technology
author	Artur Banach
spellingShingle	Artur Banach misc TK1-9971 misc Image enhancement misc histogram specification misc local Laplacian filtering misc minimally invasive surgery misc Electrical engineering. Electronics. Nuclear engineering Saliency Improvement in Feature-Poor Surgical Environments Using Local Laplacian of Specified Histograms
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topic_title	TK1-9971 Saliency Improvement in Feature-Poor Surgical Environments Using Local Laplacian of Specified Histograms Image enhancement histogram specification local Laplacian filtering minimally invasive surgery
topic	misc TK1-9971 misc Image enhancement misc histogram specification misc local Laplacian filtering misc minimally invasive surgery misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Image enhancement misc histogram specification misc local Laplacian filtering misc minimally invasive surgery misc Electrical engineering. Electronics. Nuclear engineering
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title	Saliency Improvement in Feature-Poor Surgical Environments Using Local Laplacian of Specified Histograms
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title_sort	saliency improvement in feature-poor surgical environments using local laplacian of specified histograms
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title_auth	Saliency Improvement in Feature-Poor Surgical Environments Using Local Laplacian of Specified Histograms
abstract	Navigation in endoscopic environments requires an accurate and robust localisation system. A key challenge in such environments is the paucity of visual features that hinders accurate tracking. This article examines the performance of three image enhancement techniques for tracking under such feature-poor conditions including Contrast Limited Adaptive Histogram Specification (CLAHS), Fast Local Laplacian Filtering (LLAP) and a new combination of the two coined Local Laplacian of Specified Histograms (LLSH). Two cadaveric knee arthroscopic datasets and an underwater seabed inspection dataset are used for the analysis, where results are interpreted by defining visual saliency as the number of correctly matched key-point (SIFT and SURF) features. Experimental results show a significant improvement in contrast quality and feature matching performance when image enhancement techniques are used. Results also demonstrate the LLSHs ability to vastly improve SURF tracking performance indicating more than 87% of successfully matched frames. A comparative analysis provides some important insights useful in the design of vision-based navigation for autonomous agents in feature-poor environments.
abstractGer	Navigation in endoscopic environments requires an accurate and robust localisation system. A key challenge in such environments is the paucity of visual features that hinders accurate tracking. This article examines the performance of three image enhancement techniques for tracking under such feature-poor conditions including Contrast Limited Adaptive Histogram Specification (CLAHS), Fast Local Laplacian Filtering (LLAP) and a new combination of the two coined Local Laplacian of Specified Histograms (LLSH). Two cadaveric knee arthroscopic datasets and an underwater seabed inspection dataset are used for the analysis, where results are interpreted by defining visual saliency as the number of correctly matched key-point (SIFT and SURF) features. Experimental results show a significant improvement in contrast quality and feature matching performance when image enhancement techniques are used. Results also demonstrate the LLSHs ability to vastly improve SURF tracking performance indicating more than 87% of successfully matched frames. A comparative analysis provides some important insights useful in the design of vision-based navigation for autonomous agents in feature-poor environments.
abstract_unstemmed	Navigation in endoscopic environments requires an accurate and robust localisation system. A key challenge in such environments is the paucity of visual features that hinders accurate tracking. This article examines the performance of three image enhancement techniques for tracking under such feature-poor conditions including Contrast Limited Adaptive Histogram Specification (CLAHS), Fast Local Laplacian Filtering (LLAP) and a new combination of the two coined Local Laplacian of Specified Histograms (LLSH). Two cadaveric knee arthroscopic datasets and an underwater seabed inspection dataset are used for the analysis, where results are interpreted by defining visual saliency as the number of correctly matched key-point (SIFT and SURF) features. Experimental results show a significant improvement in contrast quality and feature matching performance when image enhancement techniques are used. Results also demonstrate the LLSHs ability to vastly improve SURF tracking performance indicating more than 87% of successfully matched frames. A comparative analysis provides some important insights useful in the design of vision-based navigation for autonomous agents in feature-poor environments.
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title_short	Saliency Improvement in Feature-Poor Surgical Environments Using Local Laplacian of Specified Histograms
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