{"id":1020,"date":"2023-03-25T13:33:07","date_gmt":"2023-03-25T10:33:07","guid":{"rendered":"https:\/\/legendhomework.com\/tr\/?p=1020"},"modified":"2023-03-25T13:33:07","modified_gmt":"2023-03-25T10:33:07","slug":"grafikler-multimedya-bolumu-multimedya-bolumu-odevleri-multimedya-bolumu-tez-yaptirma-multimedya-bolumu-odev-ucretleri","status":"publish","type":"post","link":"https:\/\/legendhomework.com\/tr\/grafikler-multimedya-bolumu-multimedya-bolumu-odevleri-multimedya-bolumu-tez-yaptirma-multimedya-bolumu-odev-ucretleri\/","title":{"rendered":"Grafikler \u2013 Multimedya B\u00f6l\u00fcm\u00fc \u2013 Multimedya B\u00f6l\u00fcm\u00fc \u00d6devleri \u2013 Multimedya B\u00f6l\u00fcm\u00fc Tez Yapt\u0131rma \u2013Multimedya B\u00f6l\u00fcm\u00fc \u00d6dev \u00dccretleri"},"content":{"rendered":"

Multimedya Sunumu i\u00e7in G\u00f6r\u00fcnt\u00fc Analizi ve Grafikler<\/span><\/strong><\/h3>\n

Multimedya uygulamalar\u0131n\u0131n ba\u015far\u0131s\u0131, art\u0131k \u00e7e\u015fitli bi\u00e7imlerde gelen verilerden ilgilenilen bilgilerin etkili bir \u015fekilde temsil edilmesine b\u00fcy\u00fck \u00f6l\u00e7\u00fcde ba\u011fl\u0131d\u0131r.<\/span><\/p>\n

Bilgisayarla yeniden olu\u015fturulmu\u015f g\u00f6r\u00fcnt\u00fclerin etkin kullan\u0131m\u0131 i\u00e7in iki ad\u0131m \u00f6nemlidir: g\u00f6r\u00fcnt\u00fcn\u00fcn temel \u00f6zelliklerinin \u00e7\u0131kar\u0131lmas\u0131 yoluyla g\u00f6r\u00fcnt\u00fclerin analizi ve bu \u00f6zelliklerin eldeki uygulamaya uygun bir \u015fekilde g\u00f6rselle\u015ftirilmesi.<\/span><\/p>\n

Model tabanl\u0131 g\u00f6r\u00fcnt\u00fc analizi, g\u00f6r\u00fcnt\u00fclerin i\u00e7sel karakterini birka\u00e7 parametre ile yakalamay\u0131 ama\u00e7lar ve ayn\u0131 zamanda g\u00f6r\u00fcnt\u00fcleme s\u00fcrecinin do\u011fas\u0131n\u0131 anlamaya yard\u0131mc\u0131 olur. G\u00f6r\u00fcnt\u00fc analizindeki temel konular aras\u0131nda model se\u00e7imi, parametre tahmini, g\u00f6r\u00fcnt\u00fcleme fizi\u011fi ve g\u00f6r\u00fcnt\u00fcn\u00fcn g\u00f6revle ili\u015fkisi (g\u00f6r\u00fcnt\u00fcn\u00fcn nas\u0131l kullan\u0131laca\u011f\u0131) yer al\u0131r.<\/span><\/p>\n

Stokastik model tabanl\u0131 g\u00f6r\u00fcnt\u00fc analizi, model tabanl\u0131 g\u00f6r\u00fcnt\u00fc analizi y\u00f6ntemleri aras\u0131nda en pop\u00fcler olan\u0131d\u0131r \u00e7\u00fcnk\u00fc \u00e7o\u011fu zaman g\u00f6r\u00fcnt\u00fcleme fizi\u011fi, stokastik bir modelle etkili bir \u015fekilde modellenebilir. \u00d6rne\u011fin, standart sonlu normal kar\u0131\u015f\u0131m modellerinin uygunlu\u011fu, bir dizi t\u0131bbi g\u00f6r\u00fcnt\u00fcleme y\u00f6ntemi i\u00e7in do\u011frulanm\u0131\u015ft\u0131r.<\/span><\/p>\n

B\u00f6l\u00fcm\u00fcn ilk b\u00f6l\u00fcm\u00fcnde, model ve model s\u0131ras\u0131 se\u00e7imi, parametre tahmini ve son b\u00f6l\u00fcmlendirmeyi i\u00e7eren stokastik model tabanl\u0131 g\u00f6r\u00fcnt\u00fc analizinin eksiksiz bir \u015fekilde ele al\u0131nmas\u0131n\u0131 tart\u0131\u015f\u0131yoruz. Sonlu normal kar\u0131\u015f\u0131mlar\u0131 kullanan modellere odaklan\u0131yoruz ve t\u0131bbi g\u00f6r\u00fcnt\u00fc b\u00f6l\u00fctleme ve bilgisayar destekli te\u015fhis \u00f6rneklerini g\u00f6steriyoruz.<\/span><\/p>\n

Bilgisayar grafikleri, multimedyan\u0131n zorluklar\u0131n\u0131 a\u015fmas\u0131na yard\u0131mc\u0131 olmada merkezi bir rol oynayabilir. G\u00f6r\u00fcnt\u00fcleri alg\u0131sal yeteneklerimize (\u00e7o\u011funlukla g\u00f6rsel) ve bir sorunun \u00f6zel gereksinimlerine uygun bir bi\u00e7imde temsil etmek, bilgi edinme ve onu sindirme s\u00fcrecini daha kolay ve etkili hale getirir. <\/span><\/p>\n

Daha spesifik olarak, multimedya ortam\u0131nda g\u00f6rselle\u015ftirme ve bilgisayar grafiklerinin iyi kullan\u0131lmas\u0131, a\u015fa\u011f\u0131dakiler gibi bir dizi \u00f6nemli g\u00f6revi daha kolay ve etkili hale getirebilir:<\/span><\/p>\n

1. G\u00f6r\u00fcnt\u00fclerle ilgili bilgileri analiz etme<\/span>
\n2. G\u00f6r\u00fcnt\u00fc i\u00e7eri\u011fini ve de\u011fi\u015fiklikleri izleme<\/span>
\n3. G\u00f6r\u00fcnt\u00fc veritabanlar\u0131yla etkile\u015fim<\/span>
\n4. Di\u011fer siteler\/gruplarla i\u015fbirli\u011fi yapmak<\/span>
\n5. Video e-postalar\u0131n\u0131 i\u015flemek veya Web’de gezinmek<\/span><\/p>\n

Bu b\u00f6l\u00fcm\u00fcn ikinci b\u00f6l\u00fcm\u00fcnde, bu g\u00f6revi ba\u015farmak i\u00e7in grafik modelleme ve g\u00f6rselle\u015ftirme teknolojilerinin nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 tart\u0131\u015faca\u011f\u0131z. Grafik modelleme ve yeniden olu\u015fturma y\u00f6ntemlerini ele al\u0131yoruz ve deforme olabilen y\u00fczey-omurga modellerini tan\u0131t\u0131yoruz. Sentetik ve aral\u0131kl\u0131 veri k\u00fcmelerinin ve 3B cerrahi prostat modellerinin yeniden yap\u0131land\u0131r\u0131lmas\u0131ndaki uygulamalar\u0131 tart\u0131\u015f\u0131yoruz.<\/span><\/p>\n

G\u00f6r\u00fcnt\u00fc Analizi<\/span><\/strong><\/h3>\n

Stokastik model tabanl\u0131 g\u00f6r\u00fcnt\u00fc analizi, hem gri d\u00fczey hem de ba\u011flam g\u00f6r\u00fcnt\u00fclerinin istatistiksel \u00f6zelliklerine dayal\u0131 olarak bir g\u00f6r\u00fcnt\u00fcy\u00fc belirgin anlaml\u0131 b\u00f6lgelere b\u00f6lmek i\u00e7in kullan\u0131lan bir tekniktir. \u0130yi bir b\u00f6l\u00fctleme sonucu verilen g\u00f6r\u00fcnt\u00fc i\u00e7in uygun model se\u00e7imine ba\u011fl\u0131d\u0131r.<\/span><\/p>\n

Manyetik rezonans (MR), pozitron emisyon tomografisi (PET) ve radyografik g\u00f6r\u00fcnt\u00fcler gibi t\u0131bbi g\u00f6r\u00fcnt\u00fcler i\u00e7in model se\u00e7imi, g\u00f6r\u00fcnt\u00fcleme fizi\u011fi a\u00e7\u0131s\u0131ndan gerek\u00e7elendirilebilir veya alternatif olarak, g\u00f6r\u00fcnt\u00fcleme fizi\u011finin daha iyi anla\u015f\u0131lmas\u0131, bir model se\u00e7mek i\u00e7in kullan\u0131labilir. belirli bir g\u00f6r\u00fcnt\u00fcleme y\u00f6ntemi i\u00e7in uygun model. Model se\u00e7imi, hem her b\u00f6lgenin yerel istatistiksel da\u011f\u0131l\u0131mlar\u0131n\u0131n hem de g\u00f6r\u00fcnt\u00fc b\u00f6lgelerinin say\u0131s\u0131n\u0131n belirlenmesi anlam\u0131na gelir.<\/span><\/p>\n

\n

Grafik t\u00fcrleri<\/span>
\ngrafikler <\/span><\/a>
\nGrafik t\u00fcrleri ve kullan\u0131m alanlar\u0131<\/span>
\nHistogram grafik<\/span>
\nGrafik t\u00fcrleri ve \u00f6zellikleri<\/span>
\nGrafik \u00d6rnekleri<\/span>
\nGrafik t\u00fcrleri ve \u00d6rnekleri<\/span>
\nPasta grafik<\/span><\/p>\n

\n

G\u00f6r\u00fcnt\u00fc analizinde, her pikselin bir piksel g\u00f6r\u00fcnt\u00fcs\u00fcne ve bir ba\u011flam g\u00f6r\u00fcnt\u00fcs\u00fcne ayr\u0131\u015ft\u0131r\u0131labilece\u011fini varsayarak, piksel ve ba\u011flam modellemesini ayr\u0131 ayr\u0131 ele alabiliriz. Piksel g\u00f6r\u00fcnt\u00fc, pikselle ili\u015fkili g\u00f6zlenen gri seviye olarak tan\u0131mlan\u0131r ve sonlu kar\u0131\u015f\u0131m modelleri, en pop\u00fcler piksel g\u00f6r\u00fcnt\u00fc modelleri olmu\u015ftur.<\/span><\/p>\n

\u00d6zellikle, standart sonlu normal kar\u0131\u015f\u0131mlar (SFNM’ler) istatistiksel g\u00f6r\u00fcnt\u00fc analizinde \u00e7ok yayg\u0131n olarak kullan\u0131lmaktad\u0131r ve modelin parametrelerini hesaplamak i\u00e7in verimli algoritmalar mevcuttur.<\/span><\/p>\n

Ayr\u0131ca, ba\u011flam g\u00f6r\u00fcnt\u00fcs\u00fcn\u00fcn farkl\u0131 b\u00f6lgelerle ili\u015fkili piksel \u00fcyeli\u011fi olarak tan\u0131mland\u0131\u011f\u0131 ba\u011flam g\u00f6r\u00fcnt\u00fclerinin istatistiksel \u00f6zelliklerini dahil ederek, yerelle\u015ftirilmi\u015f bir SFNM form\u00fclasyonu, bir stokastik d\u00fczenlile\u015ftirme \u015femas\u0131 a\u00e7\u0131s\u0131ndan ba\u011flam g\u00f6r\u00fcnt\u00fclerine yerel tutarl\u0131l\u0131k k\u0131s\u0131tlamalar\u0131 uygulamak i\u00e7in kullan\u0131labilir.<\/span><\/p>\n

Bir sonraki b\u00f6l\u00fcm, sonlu kar\u0131\u015f\u0131mlar modelini a\u00e7\u0131klar ve modelin tan\u0131mlanmas\u0131n\u0131 ele al\u0131r (yani, modelin parametrelerinin tahmini ve model s\u0131ra se\u00e7imi). \u0130\u00e7inde, modelleme ba\u011flam\u0131na y\u00f6nelik yakla\u015f\u0131mlar\u0131 tart\u0131\u015f\u0131yoruz.<\/span><\/p>\n

Ayr\u0131ca, insanlar taraf\u0131ndan g\u00f6r\u00fcnt\u00fclerin alg\u0131lanmas\u0131nda doku \u00f6nemli bir \u00f6zellik olsa da, onu tan\u0131mlaman\u0131n tipik olarak zor oldu\u011funa dikkat etmek \u00f6nemlidir. Be\u015f alg\u0131sal boyut a\u00e7\u0131s\u0131ndan tan\u0131mlanabilir: kabal\u0131k, kontrast, y\u00f6nl\u00fcl\u00fck, \u00e7izgi benzeri d\u00fczenlilik ve p\u00fcr\u00fczl\u00fcl\u00fck ve tart\u0131\u015f\u0131lan grafik g\u00f6sterime dahil edilebilir.<\/span><\/p>\n

Piksel Modelleme<\/span><\/strong><\/h3>\n

N \u2261 N1 \u00d7 N2 pikselden olu\u015fan bir dijital g\u00f6r\u00fcnt\u00fc verildi\u011finde, bu g\u00f6r\u00fcnt\u00fcn\u00fcn K b\u00f6lge i\u00e7erdi\u011fini ve her pikselin x piksel g\u00f6r\u00fcnt\u00fcs\u00fcne ve l ba\u011flam g\u00f6r\u00fcnt\u00fcs\u00fcne ayr\u0131\u015ft\u0131r\u0131ld\u0131\u011f\u0131n\u0131 varsayal\u0131m. Piksellerin uzamsal s\u0131ralamas\u0131na ili\u015fkin bilgileri g\u00f6z ard\u0131 ederek, ba\u011flam g\u00f6r\u00fcnt\u00fclerini (yani piksel etiketleri) rastgele de\u011fi\u015fkenler olarak ele alabilir ve bunlar\u0131 bilinmeyen parametre \u03c0k ile \u00e7ok terimli bir da\u011f\u0131l\u0131m kullanarak tan\u0131mlayabiliriz.<\/span><\/p>\n

Bu parametre, her b\u00f6lgedeki toplam piksel say\u0131s\u0131n\u0131n da\u011f\u0131l\u0131m\u0131n\u0131 yans\u0131tt\u0131\u011f\u0131ndan, \u03c0k, genel ba\u011flam bilgisi taraf\u0131ndan belirlenen piksel etiketlerinin \u00f6nceki olas\u0131l\u0131\u011f\u0131 olarak yorumlanabilir.<\/span><\/p>\n

Dolay\u0131s\u0131yla ilgili (yeterli) istatistikler, her bile\u015fen kar\u0131\u015f\u0131m\u0131 i\u00e7in piksel g\u00f6r\u00fcnt\u00fc istatistikleri ve her bile\u015fenin piksel say\u0131s\u0131d\u0131r.\u00a0<\/span>Herhangi bir piksel g\u00f6r\u00fcnt\u00fcs\u00fc i\u00e7in marjinal olas\u0131l\u0131k \u00f6l\u00e7\u00fcs\u00fc (yani, sonlu kar\u0131\u015f\u0131mlar da\u011f\u0131l\u0131m\u0131), x ve l’nin ortak olas\u0131l\u0131k yo\u011funlu\u011funu yaz\u0131p ard\u0131ndan ortak yo\u011funlu\u011fu toplayarak elde edilebilir.<\/span><\/p>\n

l’nin t\u00fcm olas\u0131 sonu\u00e7lar\u0131 \u00fczerinden (yani, p(xi) p(xi,l) hesaplayarak), \u03c0k a\u011f\u0131rl\u0131k fakt\u00f6r\u00fc \u03c0k, \u03c0k > 0 ve Kk=1 \u03c0k’yi kar\u015f\u0131layan bir l (pdfs) toplam\u0131 ile sonu\u00e7lan\u0131r = 1. Genelle\u015ftirilmi\u015f Gauss pdf verilen b\u00f6lge k tan\u0131mlan\u0131r.<\/span><\/p>\n

\u03b1 \u226b 1 oldu\u011funda, da\u011f\u0131l\u0131m d\u00fczg\u00fcn bir pdf olma e\u011filimindedir; \u03b1 < 1 i\u00e7in pdf keskinle\u015fir; \u03b1 = 2.0 i\u00e7in Gauss pdf elde edilir; ve \u03b1 = 1.0 i\u00e7in Laplace pdf mevcuttur. Bu nedenle, genelle\u015ftirilmi\u015f Gauss modeli, \u00e7ekirdek \u015fekli farkl\u0131 \u03b1 de\u011ferleri se\u00e7ilerek kontrol edilebildi\u011finden, istatistiksel \u00f6zellikleri bilinmeyen g\u00f6r\u00fcnt\u00fclerin histogram da\u011f\u0131l\u0131m\u0131na uymas\u0131 i\u00e7in uygun bir modeldir.<\/span><\/p>\n

\u03b1 = 2 i\u00e7in sonlu Gauss kar\u0131\u015f\u0131m modeli (FGGM), genellikle standart sonlu normal kar\u0131\u015f\u0131m modeli olarak an\u0131l\u0131r ve en s\u0131k kullan\u0131lan bi\u00e7im olmu\u015ftur.<\/span><\/p>\n

T\u00fcm g\u00f6r\u00fcnt\u00fc, ba\u011f\u0131ms\u0131z ve ayn\u0131 \u015fekilde da\u011f\u0131t\u0131lm\u0131\u015f bir X rastgele alan\u0131 taraf\u0131ndan iyi bir \u015fekilde yakla\u015f\u0131klanabilir. Burada x = [x1, x2, . . . , xN ] ve x \u2208 X. Piksel g\u00f6r\u00fcnt\u00fclerinin ortak olas\u0131l\u0131k \u00f6l\u00e7\u00fcs\u00fcne ba\u011fl\u0131 olarak, sonlu kar\u0131\u015f\u0131m modellemesi alt\u0131ndaki olabilirlik fonksiyonu L(r) = Ni=1 pr(xi) olarak ifade edilebilir, burada r : {K,\u03b1,\u03c0k ,\u03bck,\u03c3k,k = 1,…,K} model parametre setini belirtir.<\/span><\/p>\n

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