Clustering of ellipses based on their distinctiveness: An aid to ellipse detection algorithms

Prasad, Dilip K.; Leung, M.K.H.

doi:10.1109/iccsit.2010.5564932

Cited by 17 publications

(12 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ellipse detection can be established using reduced quantization-free parameters space [4] or by analyzing of principal components [5]. Basically, three stable points are needed to fit an ellipse on the object contour [6][7][8]. Meanwhile the accuracy of ellipse detection highly depends on accurate detection of the object contour which is a difficult task in many cases dealing with embryo images.…”

Section: Computer Vision Methodsmentioning

confidence: 99%

Application of Numerical Intelligence Methods for the Automatic Quality Grading of an Embryo Development

Jonaitis¹,

Raudonis²,

Lipnickas³

2016

IJC

View full text Add to dashboard Cite

In vitro fertilization – a procedure which aims to get the embryo to adapt the methods of "oocyte" fertilized sperm outside the human body. At the end of this procedure there are several embryos. This paper represents overview of tracking-free and tracking-based methods for detection of important embryo development stages. Tracking-based method represents well known classical object tracking techniques. For tracking-free method were selected statistical feature extraction techniques and classification methods: Classification with training and classification without training. For the feature extraction proposed statistical methods: entropy, invariant moments and principal components analyses. For the classification are used neural networks, support vector machine and K-nearest neighbor method. Data collected consist of 500 images for each class. 70 percent of images are dedicated for training, and 30 percent for testing. The proposed method is checked by experiment. It is expected that this method will work well in video sequences.

show abstract

Section: Computer Vision Methodsmentioning

confidence: 99%

Application of Numerical Intelligence Methods for the Automatic Quality Grading of an Embryo Development

Jonaitis¹,

Raudonis²,

Lipnickas³

2016

IJC

View full text Add to dashboard Cite

show abstract

“…Thus, after a brief review of existing techniques, a novel method is proposed. There exist various ad hoc methods based on the explicit ellipse parameters (a, b, x c , y c , and ψ) [30,113] or by measuring the distance only at one specific point (e.g., via the Hausdorff distance [132,136]), though none of these are similarity invariants. Moreover, these methods have changing geometric meaning as the two ellipses change shape and relative orientation.…”

Section: Similarity Invariance: D(a I mentioning

confidence: 99%

Lunar Crater Identification in Digital Images

2021

View full text Add to dashboard Cite

It is often necessary to identify a pattern of observed craters in a single image of the lunar surface and without any prior knowledge of the camera’s location. This so-called “lost-in-space” crater identification problem is common in both crater-based terrain relative navigation (TRN) and in automatic registration of scientific imagery. Past work on crater identification has largely been based on heuristic schemes, with poor performance outside of a narrowly defined operating regime (e.g., nadir pointing images, small search areas). This work provides the first mathematically rigorous treatment of the general crater identification problem. It is shown when it is (and when it is not) possible to recognize a pattern of elliptical crater rims in an image formed by perspective projection. For the cases when it is possible to recognize a pattern, descriptors are developed using invariant theory that provably capture all of the viewpoint invariant information. These descriptors may be pre-computed for known crater patterns and placed in a searchable index for fast recognition. New techniques are also developed for computing pose from crater rim observations and for evaluating crater rim correspondences. These techniques are demonstrated on both synthetic and real images.

show abstract

“…Moreover, Qi et al method [14] is able to consume quite small running time. The reasons behind this are mainly due to its usage of projective invariant for effectively pruning straight edges, fast arc selection strategy, and simple clustering [14], [36]. In contrast to Qi et al method accelerating detection speed at the risk of generating duplicates surrounding a common ground truth, our method employs a more useful and yet relatively more time-consuming hierarchical clustering method for ellipse candidates, which could reduce the false positives significantly.…”

Section: B Experiments On Real-world Datasetsmentioning

confidence: 99%

Arc-Support Line Segments Revisited: An Efficient High-Quality Ellipse Detection

Xia

Shao

et al. 2020

IEEE Trans. on Image Process.

117

View full text Add to dashboard Cite

Over the years many ellipse detection algorithms spring up and are studied broadly, while the critical issue of detecting ellipses accurately and efficiently in real-world images remains a challenge. In this paper, we propose a valuable industry-oriented ellipse detector by arc-support line segments, which simultaneously reaches high detection accuracy and efficiency. To simplify the complicated curves in an image while retaining the general properties including convexity and polarity, the arc-support line segments are extracted, which grounds the successful detection of ellipses. The arc-support groups are formed by iteratively and robustly linking the arc-support line segments that latently belong to a common ellipse. Afterward, two complementary approaches, namely, locally selecting the arcsupport group with higher saliency and globally searching all the valid paired groups, are adopted to fit the initial ellipses in a fast way. Then, the ellipse candidate set can be formulated by hierarchical clustering of 5D parameter space of initial ellipses. Finally, the salient ellipse candidates are selected and refined as detections subject to the stringent and effective verification. Extensive experiments on three public datasets are implemented and our method achieves the best F-measure scores compared to the state-of-the-art methods. The source code is available at https://github.com/AlanLuSun/High-quality-ellipse-detection.

show abstract

Clustering of ellipses based on their distinctiveness: An aid to ellipse detection algorithms

Cited by 17 publications

References 10 publications

Application of Numerical Intelligence Methods for the Automatic Quality Grading of an Embryo Development

Application of Numerical Intelligence Methods for the Automatic Quality Grading of an Embryo Development

Lunar Crater Identification in Digital Images

Arc-Support Line Segments Revisited: An Efficient High-Quality Ellipse Detection

Contact Info

Product

Resources

About