PARÇACIK GÖRÜNTÜLEMELİ HIZ ÖLÇME TEKNİĞİ İÇİN DİNAMİK MASKELEME TEKNİKLERİ

Cisimlere ve yüzeylere Parçacık Görüntülemeli Hız Ölçümü (PIV) imajlarında sıkça rastlanır. Bunlar maskelenmediği sürece, çapraz korelasyon fonksiyonunu etkiler ve PIV analizi sonucu elde edilen hız vektörlerinde faz sınırına yakın yerlerde hatalı sonuçlar oluşturur. Cisimlerin dijital ortamda maskelenmesi, özellikle hareketli cisimlerin dinamik maske kullanılarak imajlardan çıkarılması hesap zincirinin bir parçası olarak literatürde pek çok kez yayınlanmıştır. Bu yazının bir amacı dinamik maskelemede ulaşılan kilometre taşlarının geniş bir uygulama yelpazesini de kapsayan bir özetini vermektir. Bir diğer amaç ise dinamik imaj ve vektör maskelemenin farklarını göstermektir. En son olarak, iki ayrı dinamik maskeleme örneği ayrıntılı olarak anlatılmakta ve karşılaştırılmaktadır. Kullanılan örnekler küçük kanallarda yüzen mikroorganizmalardan seçilmiştir. İlk örnekte histogram eşikleme ile dinamik maskeleme ve ikincide ise yeni bir teknik olan, özellik takibi ile dinamik maskeleme kullanılmıştır. Sonuçta, histogram eşikleme ile maskelemenin yüzme yönü ve şekli rastgele değişen yüzücülerde; özellik takibi ile maskelemenin ise şekli ve yönü pek değişmeyen yüzücülerde daha iyi sonuçlar verdiği ortaya çıkmaktadır. Dinamik maskelemenin yararını göstermek için maskeleme yapılmadan elde edilmiş PIV sonuçları, sadece imaj maskelemesi yapılarak elde edilmiş PIV sonuçları ve hem imaj hem de vektör maskelemesi yapılarak elde edilmiş PIV sonuçları karşılaştırılmıştır. Sonuçta hem imaj hem de vektör maskelemesinin kullanılmasının daha uygun olduğu ortaya çıkmıştır.

DYNAMIC MASKING TECHNIQUES FOR PARTICLE IMAGE VELOCIMETRY

Abstract: Objects and surfaces often appear in Particle Image Velocimetry (PIV) images. Unless masked, thefeatures on these contribute to the cross correlation function and introduce an error in the vectors as a result of thePIV analysis in the vicinity of the phase boundary. Digital masking of objects has appeared numerous times in theliterature as part of the analysis chain, with a growing focus on isolating moving features using dynamic masks. Oneaim of this article is to provide a summary of milestones achieved in dynamic masking covering a wide range ofapplications. Another aim is to show the difference between image masking and vector masking. Finally, twodifferent dynamic masking examples are described in detail and compared. The examples used are selected fromswimming microorganisms in small channels. In the first example, a histogram thresholding-based dynamic maskingis used, while, in the second example, a novel technique employing a feature tracking-based dynamic masking isused. Results show that histogram thresholding-based masking provides better results for swimmers which randomlychange shape and direction; whereas, feature tracking-based masking provides better results for swimmers which donot change shape or direction significantly. In order to show the improvement due to dynamic masking, acomparison is made between PIV results a) with no masking, b) with just image masking and c) with both image andvector masking. Results show that the best approach is to use both image and vector masking.

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