Attēlu fona modelēšanas algoritmi

6533b872fe1ef96bd12d3373

RESEARCH PRODUCT

Attēlu fona modelēšanas algoritmi

Krišjānis Balodis

subject

Fona modelēšanas algoritmi Datorzinātne Kompresijas artefakti KNN MOG Datorredze

description

Datorredzes izpētes laukā ir sastopami dažādi izacinājumi, kas atstāj iespaidu uz fona modelēšanas algoritmu darbību. Piemēram, apgaismojuma maiņas, dinamisks fons un ēnas. Pie fona modelēšanas izaicinājumiem var pieskaītīt arī ar video filmēšanas tehniskajām grūtībām radītios izaicinājumus, pie kuriem pieder arī kompresijas artefakti. Taču, ir jāatzīmē, ka kompresijas artefaktu atstātāis efekts uz fona modelēšanas algoritmiem ir maz pētīts. Šī darba ietvaros tika pētīta kompresijas artefaktu atstātā ietekme uz dažiem fona modelēšanas algoritmiem. Izpētei tika izvēlēti divi algoritmi Mixture of Gaussians (MOG) un K-Nearest Neighbours (KNN), kas tika darbināti uz CDNET video datu kopas ar un bez kompresijas artefaktiem. Galvenie šī darba rezultāti ir sekojoši - MOG algoritma rezultāti bija līdzīgi uz visām apskatītajām video kategorijām ar un bez kompresijas artefaktiem. Turpretī KNN algoritma rezultāti nebija tik robusti - izmaiņas algoritma izvadē bija nozīmīgi atšķirīgas starp video ar kompresijas artefaktiem un video bez šādiem artefaktiem.

year	journal	country	edition	language
2019-01-01

https://dspace.lu.lv/dspace/handle/7/47340

PEPhysical Sciences and Engineering
- PE2Fundamental Constituents of Matter
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SHSocial Sciences and Humanities
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