Perceptual grouping of circular arcs under projection (original) (raw)
Related papers
1993
A number of objects in the real world can be described as surfaces of revolution. These are a particular type of generalised cylinder with a straight axis whose 3D shape is formed by rotating a 2D plane about the axis. Examples of such objects are vases, many chess pieces, light bulbs, table lamps etc. This paper describes a number of techniques that can be used to recognise this class of object in a typical cluttered scene under perspective projection. Use is made of the symmetry of the occluding boundary, perceptual grouping of ellipses, 3D models and the hypothesis that an ellipse is a circle in the real world.
Ellipse Detection through Decomposition of Circular Arcs and Line Segments
Lecture Notes in Computer Science, 2011
In this work we propose an efficient and original method for ellipse detection which relies on a recent contour representation based on arcs and line segments [1]. The first step of such a detection is to locate ellipse candidate with a grouping process exploiting geometric properties of adjacent arcs and lines. Then, for each ellipse candidate we extract a compact and significant representation defined from the segment and arc extremities together with the arc middle points. This representation allows then a fast ellipse detection by using a simple least square technique. Finally some first comparisons with other robust approaches are proposed.
A statistically efficient method for ellipse detection
Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348), 1999
In this paper, we introduce a statistically efficient method for detecting ellipses in a n image. Given a set of digital arc segments, we introduce geometric criteria to select possible pairs of arc segments belonging to the same ellipse. The selected arc pairs are subsequently validated or rejected based on certain statistical criteria via hypothesis testing. The advantages of our technique include: 1) the proposed criteria are scaleinvariant; and 2) they can automatically adapt to the noise characteristics of each image and do not need to be adjusted empirically. Performance evaluation of our technique with real images demonstrates its good performance.
Study on perceptually-based fitting elliptic arcs
2015
This Technical Report revisits the problem of fitting the strokes of a sketch into elliptical arcs. Our purpose is to calculate a reasonably good and very fast fit applying a perceptual approach. Hence, the experiments carried out to determine how people perceive elliptical arcs in sketched strokes are described in detail, and the main conclusions are derived.
A real-time ellipse detection based on edge grouping
2009
In this paper, we present a efficient algorithm for real-time ellipse detection. Unlike Hough transform algorithm that is computationally intense and requires a higher dimensional parameter space, our proposed method reduces the computational complexity significantly, and accurately detects ellipses in realtime. We present a new method of detecting arc-segments from the image, based on the properties of ellipse. We then group the arc-segments into elliptical arcs in order to estimate the parameters of the ellipse, which are calculated using the leastsquare method. Our method has been tested and implemented on synthetic and real-world images containing both complete and incomplete ellipses. The performance is compared to existing ellipse detection algorithms, demonstrating the robustness, accuracy and effectiveness of our algorithm.
The use of perceptual organization in the prediction of geometric structures
Pattern Recognition Letters, 1992
A strategy is proposed for detecting geometric structures by perceptual organization, without prior knowledge of the image contents. A saliency criterion has been defined, that involves the length and the luminance contrast of the edges of an image. Starting from the most salient edges, perceptual organization guides the prediction of the geometric structures that are suggested by parallelism and connectivity relationships. This strategy allows an efficient analysis of the large amount of image features, pointing out at first the main structures present in the scene.
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.
Robust detection and ordering of ellipses on a calibration pattern
Pattern Recognition and Image Analysis, 2007
The aim of this work is to accurately estimate from an image the parameters of some ellipses and their relative positions with respect to a given pattern. The process is characterized because it is fully automated and is robust against image noise and occlusions. We have built a calibrator pattern with two planes each containing several ordered circles in known 3D positions. Our method is able to estimate the position of every ellipse and to put them in correspondence with the original calibrator circles.