Splitting touching cells based on concave points and ellipse fitting

Bai, Xiangzhi; Sun, Changming; Zhou, Fugen

doi:10.1016/j.patcog.2009.04.003

Cited by 180 publications

(138 citation statements)

References 18 publications

Supporting

Mentioning

137

Contrasting

Order By: Relevance

“…For example, ellipse fitting is used in the calibration of catadioptric cameras [9], segmentation of touching cells [3,28] or grains [30], in strain analysis [20], and in the study of galaxies in astrophysics [26]. Given the rich field of applications, it is not surprising that numerous ellipse fitting algorithms have been reported in the literature [1,[10][11][12]16,21,24,25,27,29,31].…”

Section: Introductionmentioning

confidence: 99%

Guaranteed Ellipse Fitting with the Sampson Distance

Szpak

Chojnacki

Hengel

2012

Computer Vision – ECCV 2012

View full text Add to dashboard Cite

Abstract. When faced with an ellipse fitting problem, practitioners frequently resort to algebraic ellipse fitting methods due to their simplicity and efficiency. Currently, practitioners must choose between algebraic methods that guarantee an ellipse fit but exhibit high bias, and geometric methods that are less biased but may no longer guarantee an ellipse solution. We address this limitation by proposing a method that strikes a balance between these two objectives. Specifically, we propose a fast stable algorithm for fitting a guaranteed ellipse to data using the Sampson distance as a data-parameter discrepancy measure. We validate the stability, accuracy, and efficiency of our method on both real and synthetic data. Experimental results show that our algorithm is a fast and accurate approximation of the computationally more expensive orthogonal-distance-based ellipse fitting method. In view of these qualities, our method may be of interest to practitioners who require accurate and guaranteed ellipse estimates.

show abstract

Section: Introductionmentioning

confidence: 99%

Guaranteed Ellipse Fitting with the Sampson Distance

Szpak

Chojnacki

Hengel

2012

Computer Vision – ECCV 2012

View full text Add to dashboard Cite

show abstract

“…Another possibility is to analyse the concavity of the contours of the grain clusters (Baia et al, 2009), but because of noise or of the image grid along contours, it is usual to detect false concave regions, and to obtain an overestimation of the number of concave points.…”

Section: Concavities Locationmentioning

confidence: 99%

“…, but those methods are inefficient or difficult to use when fused objects are numerous, not perfect disks, or when contact zones are important. It is also possible to use methods based on ellipse fitting (Talbot and Appleton, 2002;Evans et al, 2004;Zhang et al, 2005), which appear efficient as long as clusters contain only a few grains, or whose performance depends on the fit of the contour to an ideal ellipse (Baia et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Touching Grain Kernels Separation by Gap-Filling

Faessel¹,

Courtois²

2011

Image Anal Stereol

View full text Add to dashboard Cite

Separation of touching grain kernels is a recurring problem in image analysis. Morphological methods to separate merged objects in binary images are generally based on the watershed transform applied to the inverse of the distance function. This method is efficient with roughly circular objects, but cannot separate objects beyond a certain elliptic shape nor when the contact zones are too numerous or too large. This paper presents a gap-filling method applied to the skeleton of the image background as an alternative technique to go further in the fused objects separation process. Open lines resulting from skeletonization are prolonged according to their direction from corresponding end points. If the distance between two lines is smaller than a certain value, their respective end points are connected. Results of combined use of watershed and gap-filling based methods are presented on sample binary images. An example of its use on an particularly complex image containing rice grains shows that it allows to segment up to 90% of the grains when classical watershed methods allow only to segment 25% of the grains. An application to breakage and cracks assessing of parboiled rice kernels is presented.

show abstract

“…2. We first apply canny operator [2] to the in-put image. The proposed method is based on a modified RANSAC [7], which constructs ellipse models by randomly choosing five boundary (edge) pixels.…”

Section: Basic Ideamentioning

confidence: 99%

“…As computing hardware and theories have improved, in recent years a wide range of ellipseestimation applications, including defect detection for industrial products [24], ellipse extraction from remote sensing images [5], [14], [39], biological and biomedical analysis [2], [12], [18], intelligent transportation systems [25], [32], human pupil monitoring [17], human face recognition and tracking [7], [10], [22], [38], [40], video surveillance [27], [35], [36], and others [37], has been achieved. In addition, since the ellipse is a fundamental shape primitive, ellipse detection can facilitate more advanced object recognition tasks by providing significant appearance features.…”

Section: Introductionmentioning

confidence: 99%

Elliptical Object Detection by a Modified RANSAC with Sampling Constraint from Boundary Curves' Clustering

Xie

Ohya

2010

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYThis paper proposes a method for detecting ellipses from an image despite (1) multiple colors within the ellipses, (2) partially occluded ellipses' boundaries, (3) noisy, locally deformed boundaries of ellipses, (4) presence of multiple objects other than the ellipses in the image, and (5) combinations of (1) through (4). After boundary curves are obtained by edge detection, by utilizing the first-order difference curves of the edge orientation of each pixel in the boundary curves, a segment-reconnect method obtains boundary clusters. Then, a modified RANSAC detects ellipses by choosing five pixels randomly from the boundary clusters, where overlapped ellipses are merged. Experimental results using synthesized images and real images demonstrate the effectiveness of the proposed method together with comparison with the Randomized Hough Transform, a wellknown conventional method.

show abstract

Splitting touching cells based on concave points and ellipse fitting

Cited by 180 publications

References 18 publications

Guaranteed Ellipse Fitting with the Sampson Distance

Guaranteed Ellipse Fitting with the Sampson Distance

Touching Grain Kernels Separation by Gap-Filling

Elliptical Object Detection by a Modified RANSAC with Sampling Constraint from Boundary Curves' Clustering

Contact Info

Product

Resources

About