l1 and l1 ODR Fitting of Geometric Elements (original) (raw)

l1 and l∞ ODR Fitting of Geometric Elements

msl.uni-bonn.de

We consider the fitting of geometric elements, such as lines, planes, circles, cones, and cylinders, in such a way that the sum of distances or the maximal distance from the element to the data points is minimized. We refer to this kind of distance based fitting as orthogonal distance regression or ODR. We present a separation of variables algorithm for l1 and l∞ ODR fitting of geometric elements. The algorithm is iterative and allows the element to be given in either implicit form f (x, β) = 0 or in parametric form x = g(t, β), where β is the vector of shape parameters, x is a 2-or 3-vector, and s is a vector of location parameters. The algorithm may even be applied in cases, such as with ellipses, in which a closed form expression for the distance is either not available or is difficult to compute. For l1 and l∞ fitting, the norm of the gradient is not available as a stopping criterion, as it is not continuous. We present a stopping criterion that handles both the l1 and the l∞ case, and is based on a suitable characterization of the stationary points.

A simple least squares method for fitting of ellipses and circles depends on border points of a two-tone image and their 3-D extensions

Pattern Recognition Letters, 2010

Fitting circles and ellipses of an object is a problem that arises in many application areas, e.g. target detection, shape analysis and biomedical image analysis. In the past, algorithms have been proposed, which fit circles and ellipses in some least squares sense without minimizing the geometric distance to the given points. In this paper, the problem of fitting circle or ellipse to an object in 2-D as well as the problem of fitting sphere, spheroid or ellipsoid to an object in 3-D have been considered. The proposed algorithm depends on the border points of the object. Here, assume that the center of the ellipse or circle coincides with the centroid of all border points of the object. The major and minor axes of the ellipse are presented by least sum perpendicular distance of all border points of the object. The main concept is that the border points satisfy the equation of conic. On the basis of this concept, all the border points of the object will generate an error function (algebraic function) and the other parameters of the conic are estimated by minimizing this error function. The extension of this idea in 3-D for fitting sphere, spheroid and ellipsoid are proposed.

ElliFit: An unconstrained, non-iterative, least squares based geometric Ellipse Fitting method

Pattern Recognition, 2013

A novel ellipse fitting method which is selective for digital and noisy elliptic curves is proposed in this paper. The method aims at fitting an ellipse only when the data points are highly likely belong to an ellipse. This is achieved using the geometric distances of the ellipse from the data points. The proposed method models the non-linear problem of ellipse fitting as a combination of two operators, with one being linear, numerically stable, and easily invertible, while the other being non-linear but unique and easily invertible operator. As a consequence, the proposed ellipse fitting method has several salient properties like unconstrained, stable, non-iterative, and computationally inexpensive. The efficacy of the method is compared against six contemporary and recent algorithms based on the least squares formulation using five experiments of diverse practical challenges, like digitization, incomplete ellipses, and Gaussian noise (up to 30%). Three of the experiments comprise of a total of 44,400 ellipses (positive test data) while the other two are tested on 320,000 non-elliptic conics (negative test data). The results show that the proposed method is quite selective to elliptic shapes only and provides accurate fitting results, indicating potential application in medical, robotics, object detection, and other image processing industrial applications.

Geometric least-squares fitting of spheres, cylinders, cones and tori

Preprint, 1997

This paper considers a problem arising in the reverse engineering of boundary representation solid models from three-dimensional depth maps of scanned objects. In particular, we wish to identify and fit surfaces of known type wherever these are a good fit, and we briefly outline a segmentation strategy for deciding to which surface type the depth points should be assigned. The particular contributions of this paper are methods for the least-squares fitting of spheres, cylinders, cones and tori to three-dimensional data. While ...

Least Squares Fitting of Ellipsoid Using Orthogonal Distances

Boletim de Ciências Geodésicas, 2015

In this paper, we present techniques for ellipsoid fitting which are based on minimizing the sum of the squares of the geometric distances between the data and the ellipsoid. The literature often uses "orthogonal fitting" in place of "geometric fitting" or "best-fit". For many different purposes, the best-fit ellipsoid fitting to a set of points is required. The problem offitting ellipsoid is encounteredfrequently intheimage processing, face recognition, computer games, geodesy etc. Today, increasing GPS and satellite measurementsprecisionwill allow usto determine amore realistic Earth ellipsoid. Several studies have shown that the Earth, other planets, natural satellites, asteroids and comets can be modeled as triaxial ellipsoids Burša and Šima (1980), Iz et all (2011). Determining the reference ellipsoid for the Earth is an important ellipsoid fitting application, because all geodetic calculations are performed on the reference ellipsoid. Algebraic fit...

Least Squares Association of Geometrical Features by Automatic Differentiation

Key Engineering Materials, 2010

Least squares association of geometrical features plays an important role in geometrical product specification and verification. Most existing algorithms for the least squares association today usually do not give the covariance matrix associated with the parameters of the respective geometrical feature. The reason is that the complexity of these algorithms can be very high, because partial differential quotients are needed. If the necessary partial difference quotients are calculated by hand and subsequently coded into an algorithm, there is a high risk to introduce unwillingly errors. This paper shows how the least squares algorithm can automatically be generated solely from the equation specifying the distance function of the measured points from the geometrical feature.

Geometric matching of circular features by least squares fitting

Pattern Recognition Letters, 2002

Based on the hypothesis that a set of circular arcs is related to a set of circles by translation, rotation and scaling, the paper presents the design of a misalignment cost function, the derivation of an optimal geometric transformation based on least squares fitting to align circular arcs to circles, and computer simulation results to demonstrate the effectiveness of the