دانلود مقاله ISI انگلیسی شماره 53041
عنوان فارسی مقاله

فیلتر کالمن بر اساس برآورد حدس اولیه برای همبستگی تصویر دیجیتال

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
53041 2015 9 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Kalman filter based initial guess estimation for digital image correlation
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Optics and Lasers in Engineering, Volume 73, October 2015, Pages 80–88

کلمات کلیدی
همبستگی تصویر دیجیتال - فیلتر کالمن - برآورد حدس اولیه
پیش نمایش مقاله
پیش نمایش مقاله فیلتر کالمن بر اساس برآورد حدس اولیه برای همبستگی تصویر دیجیتال

چکیده انگلیسی

In applications digital image correlation based algorithms often present a basis for analysis of movement/deformation of bodies. The sequence of the obtained images is analyzed for this purpose. Especially, in cases when the body׳s movement/deformation between two successive images is significant, the initial guess can have a major influence on the execution speed of the algorithm. In the worst case it can even cause the divergence of the algorithm. This was the inspiration to develop a new and unique approach for an accurate and reliable determination of an initial guess for each image pixel. Kalman filter has been used for this purpose. It uses past measurements of observed variable(s) for calculations. Beside that it also incorporates state space model of the actual system. This is one of the most important advantages provided by Kalman filter. The determined initial guess by the proposed method is actually close to the true one and it enables fast convergence. Even more important property of this approach is the fact that it is not path-dependant because each image pixel, which is defined in ROI, is tracked through the sequence of images based on its own past measurements and general state space model. Consequently, the proposed method can be used to analyze tasks where discontinuities between image pixels are present. The applied method can be used to predict an initial guess where reference and deformed subsets are related by translational and rotational motion. The advantages mentioned above are verified with numerical and real experiments. The experimental validations are performed by NR (Newton–Raphson) approach which is the most widely used. Beside NR method the presented algorithm is applicable for other registration methods as well. It is used as an addition for calculation of initial guesses in a sequence of deformed images.

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