Curvature Detection using Polynomial Fitting on Local Orientation
نویسنده
چکیده
This report describes a technique to detect curvature. The technique uses local polynomial fitting on a local orientation description of an image. The idea is based on the theory of rotational symmetries which describes curvature, circles, starpatterns etc. The local polynomial fitting is shown to be equivalent to calculating partial derivatives on a lowpass version of the local orientation. The new method can therefore be very efficiently implemented both in the singlescale case and in the multiscale case.
منابع مشابه
Multiscale Curvature Detection in Computer Vision
This thesis presents a new method for detection of complex curvatures such as corners, circles, and star patterns. The method is based on a second degree local polynomial model applied to a local orientation description in double angle representation. The theory of rotational symmetries is used to compute curvature responses from the parameters of the polynomial model. The responses are made mo...
متن کاملSurface curvature estimation for automatic colonic polyp detection [5746-43]
Colonic polyps are growths on the inner wall of the colon. They appear like elliptical protrusions which can be detected by curvature-derived shape discriminators. For reasons of computation efficiency, much of the past work in computeraided diagnostic CT colonography adopted kernel-based convolution methods in curvature estimation. However, kernel methods can yield erroneous results at thin st...
متن کاملReal-Time Curvature Defect Detection on Outer Surfaces Using Best-Fit Polynomial Interpolation
This paper presents a novel, real-time defect detection system, based on a best-fit polynomial interpolation, that inspects the conditions of outer surfaces. The defect detection system is an enhanced feature extraction method that employs this technique to inspect the flatness, waviness, blob, and curvature faults of these surfaces. The proposed method has been performed, tested, and validate...
متن کاملUsing Wavelets and Splines to Forecast Non-Stationary Time Series
This paper deals with a short term forecasting non-stationary time series using wavelets and splines. Wavelets can decompose the series as the sum of two low and high frequency components. Aminghafari and Poggi (2007) proposed to predict high frequency component by wavelets and extrapolate low frequency component by local polynomial fitting. We propose to forecast non-stationary process u...
متن کاملThree Dimensional Boundary Detection Using Higher-Order Surface Fitting and Directional Smoothing
The authors propose an algorithm for detection of three-dimensional bundaries in noisy images based on higher-order polynomial surface fitting and directional smoothing. Fitting a polynomial to the local intensities gives the intensity hypersurface. An isointensity surface i s derived from the hyperplane and directional smwthiag is defined as smoothing along this isointensjty surface. The devel...
متن کامل