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MULTIMEDIA TIZIMLARDA TASVIRLARGA ISHLOV BERISHNING DASTLABKI USULLARI

Authors: Abdullayva Ozoda; Muhammadjonov Alijon;

MULTIMEDIA TIZIMLARDA TASVIRLARGA ISHLOV BERISHNING DASTLABKI USULLARI

Abstract

Grafik muhandis uchun ma’lumotlarni oldindan qayta ishlash yoki tozalash muhim bosqich hisoblanadi va ko‘pchilik muhandislar model yaratishdan oldin ma’lumotlarni qayta ishlashga ko‘p vaqt sarflashadi. Ma’lumotni oldindan qayta ishlashning ba’zi misollari orasida chegaralarni aniqlash, yetishmayotgan qiymatlarni qayta ishlash va keraksiz yoki shovqinli ma’lumotlarni olib tashlash muammolari yechimi keltirilgan. Tasvirni qayta ishlashning 4 xil usuli keltirilgan: piksel yorqinligini o‘zgartirish/yorqinlikni tuzatish, geometrik o‘zgarishlar, tasvirni filtrlash va segmentlashlash, furye transformatsiyasi va tasvirni tiklash.O‘tkazilgan tadqiqotlar natijasida tinglovchilarning o‘zlashtirishning real ma’lumotlarning termasidagi approksimatsion modellarni qurishga asoslangan multikontentlarning natijaviy sinfini shakllantirish algoritmi taklif etilgan. Taklif etilgan usul asosida regression model, bilimlarning noaniq bazasining multimedia tizimi xamda neyro-noaniq modeli yordamida nochiziqli bog‘liklikni identifikatsiyalashtirishning uchta usullar bo‘yicha istiqbolli reja tuzishning approksimatsion modeli yaratilgan.

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Keywords

gistogrammani tenglashtirish, sigmasimon funktsiya, tasvirni filtrlash va segmentlash, DFT(Discrete Furier Transform), gistogrammani tenglashtirish, sigmasimon funktsiya, tasvirni filtrlash va segmentlash, DFT(Discrete Furier Transform)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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