SavedAdd your journal

Journals / Bilgisayar Bilimleri / 2016 / Cilt: 1 - Sayı: 1

Comparision of Automatic and Interactive Segmentatition Methods

Otomatik Ve İnteraktif Bölütleme Yöntemlerinin Karşılaştırılması

Serdar Alasu
Muhammed Fatih Talu
Pages
20–28
Publication Date
2016-12-01
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Obtained results demonstrate that interactive segmentatiton is faster and more accure than automatic segmentation.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This paper includes comparision of automatic and interactive segmentation methods.
📊
Key Findings & Data:Both methods are used for color images segmentatiton and based Gaussian Mixture Model.
💡
Conclusion & Impact:Obtained results demonstrate that interactive segmentatiton is faster and more accure than automatic segmentation.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Gauss Karışım Modeli · Otomatik Bölütleme · İnteraktif Bölütleme

Abstract

This paper includes comparision of automatic and interactive segmentation methods. Both methods are used for color images segmentatiton and based Gaussian Mixture Model. In automatic segmentation is segmented image pixels without any prior knowledge provided by the user. Interactive segmentation needs prior knowledge provided by the user and segmentation process are based prior knowledge. Obtained results demonstrate that interactive segmentatiton is faster and more accure than automatic segmentation.

Özet

Bu makale, otomatik ve interaktif bölütleme yöntemlerinin karşılaştırılmasını içermektedir. Her iki yöntem renkli görüntülerin bölütlenmesi için kullanılmakta ve Gauss Karışım Modelini (Gaussian Mixture Model) temel almaktadır. Otomatik bölütlemede, kullanıcıdan her hangi bir önsel bilgi istenmeden görüntü pikselleri bölütlenir. İnteraktif bölütlemede ise kullanıcı tarafından sağlanan önsel bilgiye ihtiyaç vardır ve bölütleme işlemi bu önsel bilgiye göre yapılmaktadır. Elde edilen sonuçlar interaktif bölütlemenin, otomatik bölütlemeden daha hızlı ve doğru olduğunu ortaya koymaktadır.

🔖

Cite This Article

Alasu, S., Talu, M. F. (2016). Comparision of Automatic and Interactive Segmentatition Methods. Bilgisayar Bilimleri, Cilt: 1 - Sayı: 1, 20–28. https://ajindex.com/articles/743250

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (9)

  1. H. Renjini and P. Bhagavathi Sivakumar, “Comparison of Automatic and Interactive Image Segmentation Methods”, International Journal of Engineering Research & Technology (IJERT), vol. 2, no. 6, pp. 3162-3170, 2013
  2. C. M. Smith, et al. Automatic thresholding of three-dimensional microvascular structures from confocal microscopy images. J. Microscopy, 225(3):244–257, 2007
  3. M. Kass, A. Witkin, and D. Terzopoulos. Snakes: Active contour models. International Journal of Computer Vision, 1(4):321–331, 1987. 179
  4. V. Grau, A. U. J. Mewes, M. Alcaniz, R. Kikinis, and S. K. Warfield. Improved watershed transform for medical image segmentation using prior information. IEEE Trans. Med.Imag., 23(4):447–458, 2004
  5. A. Pitiot, A.W. Toga, N. Ayache, and P. Thompson. Texture based MRI segmentation with a two-stage hybrid neural classifier. In Proc.World Congress Computational Intelligence/INNSIEEE Int. Joint Conf. Neural Networks, pages 2053–2058, 2002
  6. Alasu Serdar, and Muhammed Fatih Talu. "Interactive segmentatition implementation." 2015 23nd Signal Processing and Communications Applications Conference (SIU). IEEE, 2015
  7. Boykov Y, JollyM(2001) Interactive graph cuts for optimal boundary & region segmentation of objects in nd images. In: Proceeding of 8th IEEE international conference on computer vision, ICCV 2001, IEEE, vol 1, pp 105–112 [8] Mortensen E, Barrett W (1998) Interactive segmentation with intelligent scissors. Graph Models Image Process 60(5):349–384 [9] Grady L (2006) Random walks for image segmentation. IEEE Trans Pattern Anal Mach Intell 28(11):1768–1783 [10] Bai, X., Sapiro, G, “A geodesic framework for fast interactive image and video segmentation and matting.” In: Proceedings of International Conference on Computer Vision (ICCV), pp. 1–8 (2007
  8. Photoshop / Renk modları, https://helpx.adobe.com/tr/photoshop /using/color-modes.html
  9. Ortalama normalizasyonu, https://en.wikipedia.org/wiki/ Normalization_(statistics)