On GPU Connected Components and Properties : A Systematic Evaluation of Connected Component Labeling Algorithms and Their Extension for Property Extraction

Connected component labeling (CCL) is a fundamental image processing problem that has been studied in many platforms, including GPUs. A common approach to CCL performance analysis is studying the total processing time as a function of abstract image features, like the number of connected components...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 28(2019), 1 vom: 28. Jan., Seite 17-31
1. Verfasser: Asad, Pedro (VerfasserIn)
Weitere Verfasser: Marroquim, Ricardo, Souza, Andrea L E L
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2019
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Journal Article
LEADER 01000naa a22002652 4500
001 NLM286372517
003 DE-627
005 20231225051544.0
007 cr uuu---uuuuu
008 231225s2019 xx |||||o 00| ||eng c
024 7 |a 10.1109/TIP.2018.2851445  |2 doi 
028 5 2 |a pubmed24n0954.xml 
035 |a (DE-627)NLM286372517 
035 |a (NLM)29994708 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Asad, Pedro  |e verfasserin  |4 aut 
245 1 0 |a On GPU Connected Components and Properties  |b A Systematic Evaluation of Connected Component Labeling Algorithms and Their Extension for Property Extraction 
264 1 |c 2019 
336 |a Text  |b txt  |2 rdacontent 
337 |a ƒaComputermedien  |b c  |2 rdamedia 
338 |a ƒa Online-Ressource  |b cr  |2 rdacarrier 
500 |a Date Completed 24.09.2018 
500 |a Date Revised 24.09.2018 
500 |a published: Print-Electronic 
500 |a Citation Status PubMed-not-MEDLINE 
520 |a Connected component labeling (CCL) is a fundamental image processing problem that has been studied in many platforms, including GPUs. A common approach to CCL performance analysis is studying the total processing time as a function of abstract image features, like the number of connected components or the fraction of foreground pixels, and input data usually includes synthetic images and segmented video datasets. In this paper, we develop on these ideas and propose an evaluation methodology for GPU CCL algorithms based on synthetic image patterns, addressing the nonexistence of a standard and reliable benchmark in the literature. Our methodology, applied on two important algorithms from existing literature, uncovers their data dependency with great detail, and allows us to model their processing time in three real-world video data sets as functions of abstract, high-level image concepts. We also apply our methodology for studying the memory and performance requirements of two strategies for computing connected component properties: an existing memory-hungry approach, and a new memory-preserving strategy 
650 4 |a Journal Article 
700 1 |a Marroquim, Ricardo  |e verfasserin  |4 aut 
700 1 |a Souza, Andrea L E L  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society  |d 1992  |g 28(2019), 1 vom: 28. Jan., Seite 17-31  |w (DE-627)NLM09821456X  |x 1941-0042  |7 nnns 
773 1 8 |g volume:28  |g year:2019  |g number:1  |g day:28  |g month:01  |g pages:17-31 
856 4 0 |u http://dx.doi.org/10.1109/TIP.2018.2851445  |3 Volltext 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_NLM 
912 |a GBV_ILN_350 
951 |a AR 
952 |d 28  |j 2019  |e 1  |b 28  |c 01  |h 17-31