Ellipse Detection Method Research Articles

A high-precision ellipse detection method based on quadrant representation and top-down fitting

Ellipse detection is a basic task in many computer-vision related problems. While widely studied in recent years, accurate and efficient detection in real-world images is still a challenge. In this paper, a novel ellipse detector, with high accuracy and efficiency, is proposed. The detector models edge by block sequences, and extracts a set of elliptical arcs, which are classified into four sets. Then top-down ellipse fitting strategy that also makes the method able to detect small and flat ellipses is designed. A two-level validation process is used to select highly probable potential ellipses, especially for fragmented ellipses. Experiments on four synthetic datasets show that the proposed method performs far better than existing methods. In images with severe cluttering and occlusion, the F-measure can still be around 0.9. On four real image datasets the proposed method achieves better F-measure scores with competitive speed than state-of-the-art techniques.

Circular-Feature-Based Pose Estimation of Noncooperative Satellite Using Time-of-Flight Sensor

This paper develops an end-to-end circular-feature-based pose estimation using the time-of-flight (TOF) camera for capturing serviced satellite. First, the docking ring is chosen as the recognized object for pose measurement. Second, a new ellipse detection method is proposed to detect the docking ring. Third, the circular feature is used to estimate the six-degrees-of-freedom pose with the aid of a reflective block. Combined with the range at each pixel location from TOF, the pose duality can be eliminated, and the roll angle is recovered through the geometric constraint. Ground physical experiments validate the effectiveness and efficiency of the proposed method. The on-orbit application demonstrates that the proposed algorithm can accurately and robustly estimate the relative pose of the noncooperative satellite, providing reliable navigation information for the chaser spacecraft with the relative pose estimation errors less than 2 deg and 5 cm.

Anisotropic Scale-Invariant Ellipse Detection.

Detecting ellipses poses a challenging low-level task indispensable to many image analysis applications. Existing ellipse detection methods commonly encounter two fundamental issues. First, inferior detection accuracy could be incurred on a small ellipse than that on a large one; this introduces the scale issue. Second, inferior detection accuracy could be yielded along the minor axis than along the major one of the same ellipse; this leads to the anisotropy issue. To address these issues simultaneously, a novel anisotropic scale-invariant (ASI) ellipse detection methodology is proposed. Our basic idea is to perform ellipse detection in a transformed image space referred to as the ellipse normalization (EN) space, in which the desired ellipse from the original image is 'normalized' to the unit circle. With the establishment of the EN-space, an analytical ellipse fitting scheme and a set of distance measures are developed. Theoretical justifications are then derived to prove that both our ellipse fitting scheme and distance measures are invariant to anisotropic scaling, and thus each ellipse can be detected with the same accuracy regardless of its size and ellipticity. By incorporating these components into two recent state-of-the-art algorithms, two ASI ellipse detectors are finally developed and exploited to verify the effectiveness of our proposed methodology.

Ellipse Detection with Applications of Convolutional Neural Network in Industrial Images

Ellipse detection has a very wide range of applications in the field of industrial production, especially in the geometric detection of metallurgical hinge pins. However, the factors in industrial images, such as small object size and incomplete ellipse in the image boundary, bring challenges to ellipse detection, which cannot be solved by existing methods. This paper proposes a method for ellipse detection in industrial images, which utilizes the extended proposal operation to prevent the loss of ellipse rotation angle features during ellipse regression. Moreover, the Gaussian angle distance conforming to the ellipse axioms is adopted and combined with smooth L1 loss as the ellipse regression loss function to enhance the prediction accuracy of the ellipse rotation angle. The effectiveness of the proposed method is demonstrated on the hinge pins dataset, with experiment results showing an AP* of 80.93% and indicating superior detection performance compared to other methods. It is thus suitable for engineering applications and can provide visual guidance for the precise measurement of ellipse-like mechanical parts.

A Method for Measuring Parameters of Defective Ellipse Based on Vision.

Ellipse detection has a very wide range of applications in the field of object detection, especially in the geometric size detection of inclined microporous parts. However, due to the processing methods applied to the parts, there are certain defects in the features. The existing ellipse detection methods do not meet the needs of rapid detection due to the problems of false detection and time consumption. This article proposes a method of quickly obtaining defective ellipse parameters based on vision. It mainly uses the approximation principle of circles to repair defective circles, then combines this with morphological processing to obtain effective edge points, and finally uses the least squares method to obtain elliptical parameters. By simulating the computer-generated images, the results demonstrate that the center fitting error of the simulated defect ellipses with major and minor axes of 600 and 400 pixels is less than 1 pixel, the major and minor axis fitting error is less than 3 pixels, and the tilt angle fitting error is less than 0.1°. Further, experimental verification was conducted on the engine injection hole. The measurement results show that the surface size deviation was less than 0.01 mm and the angle error was less than 0.15°, which means the parameters of defective ellipses can obtained quickly and effectively. It is thus suitable for engineering applications, and can provide visual guidance for the precise measurement of fiber probes.

AI-based analysis of 3D position and orientation of red blood cells using a digital in-line holographic microscopy.

The morphological and mechanical characteristics of red blood cells (RBCs) largely vary depending on the occurrence of hematologic disorders. Variations in the rheological properties of RBCs affect the dynamic motions of RBCs, especially their rotational behavior. However, conventional techniques for measuring the orientation of biconcave-shaped RBCs still have some technical limitations, including complicated optical setups, complex post data processing, and low throughput. In this study, we propose a novel image-based technique for measuring 3D position and orientation of normal RBCs using digital in-line holographic microscopy (DIHM) and artificial intelligence (AI). Formaldehyde-fixed RBCs are immobilized in coagulated polydimethylsiloxane (PDMS). Holographic images of RBCs positioned at various out-of-plane angles are acquired by precisely manipulating the PDMS-trapped RBC sample attached to a 4-axis optical stage. With the aid of deep learning algorithms for data augmentation and regression analysis, the out-of-plane angle of RBCs is directly predicted from the captured holographic images. The 3D position and in-plane angle of RBCs are acquired by employing numerical reconstruction and ellipse detection methods. Combining these digital image processing techniques, the 3D positional and orientational information of each RBC recorded in a single holographic image is measured within 23.5 and 3.07s, respectively. The proposed AI-based DIHM technique that can extract the 3D position, orientation, and morphology of individual RBCs would be utilized to analyze the dynamic translational and rotational motions of abnormal RBCs with hematologic disorders in shear flows through further research.

A Fast and High-Precision Ellipse Detection Method Based on the Candy’s Theorem

A Fast and High-Precision Ellipse Detection Method Based on the Candy’s Theorem

Characteristic Mapping for Ellipse Detection Acceleration

It is challenging to characterize the intrinsic geometry of high-degree algebraic curves with lower-degree algebraic curves. The reduction in the curve's degree implies lower computation costs, which is crucial for various practical computer vision systems. In this paper, we develop a characteristic mapping (CM) to recursively degenerate 3n points on a planar curve of n th order to 3(n-1) points on a curve of (n-1) th order. The proposed characteristic mapping enables curve grouping on a line, a curve of the lowest order, that preserves the intrinsic geometric properties of a higher-order curve (ellipse). We prove a necessary condition and derive an efficient arc grouping module that finds valid elliptical arc segments by determining whether the mapped three points are colinear, invoking minimal computation. We embed the module into two latest arc-based ellipse detection methods, which reduces their running time by 25% and 50% on average over five widely used data sets. This yields faster detection than the state-of-the-art algorithms while keeping their precision comparable or even higher. Two CM embedded methods also significantly surpass a deep learning method on all evaluation metrics.

A Novel SVM Network Using HOG Feature for Prohibition Traffic Sign Recognition

To recognize prohibition traffic sign, this paper proposes a novel method that is trained by a small number of samples and uses the feature of histogram of oriented gradient (HOG) and support vector machine (SVM) network. The recognition method is mainly divided into three stages. The first stage is image preprocessing, which includes image interception based on ellipse detection, image resizing, and Gamma correction. In the part of image interception, a new ellipse detection method called RHT_MCN is proposed based on RHT, which uses the maximum coincidence number (MCN) of image edge points and detected ellipse edge to choose the final ellipse for image interception. The second stage is the feature extraction of HOG. The third stage is the prohibition traffic sign recognition (PTSR) based on SVM network. In the design and implementation of the PTSR model, a new single-layer SVM network is proposed. The ascending spiral training method of the recognition model is introduced in detail. Finally, the data from GTSRB is used to test and analyze the prohibition traffic sign recognition method. The method is proven to have good applicability.

Vision-based trajectory monitoring for assembly alignment of precast concrete bridge components

The prefabricated structures are popularly used because of their high efficiency and it is important to ensure their installation quality. The current installation is guided by visual observation and total station, which is inaccurate and time-consuming. This study presents a stereo-vision-based pose measurement technique for guiding the installation to address the problems. The contributions are as follows: 1) A framework of a vision-based 6-DOF pose monitoring system is designed to track the precast member continuously; 2) A two-level adjustment strategy is suggested by transferring the relative pose measurement between the precast member and existing structure to the process to determine the relative relations of two coordinate systems; 3) A robust deep-learning-based ellipse detection method is proposed to identify and classify the artificial targets accurately. The proposed technique is evaluated in both indoor experiments and the construction of an expressway viaduct to demonstrate its accuracy and effectiveness.

Real-Time Ellipse Detection for Robotics Applications

We propose a new algorithm for real-time detection and tracking of elliptic patterns suitable for real-world robotics applications. The method fits ellipses to each contour in the image frame and rejects ellipses that do not yield a good fit. The resulting detection and tracking method is lightweight enough to be used on robots' resource-limited onboard computers, can deal with lighting variations and detect the pattern even when the view is partial. The method is tested on an example application of an autonomous UAV landing on a fast-moving vehicle to show its performance indoors, outdoors, and in simulation on a real-world robotics task. The comparison with other well-known ellipse detection methods shows that our proposed algorithm outperforms other methods with the F1 score of 0.981 on a dataset with over 1500 frames. The videos of experiments, the source codes, and the collected dataset are provided with the paper at https://theairlab.org/landing-on-vehicle .

Fast high-precision ellipse detection method

Obtaining an optimal tradeoff between accuracy and efficiency in ellipse detection is a significant challenge. In this paper, we propose a fast, high-precision ellipse detection method that utilizes arc selection and grouping strategies to significantly reduce the computation amount. A fast corner detection algorithm is also proposed. In the proposed method, to generate ellipse candidates comprehensively, both grouped and ungrouped-salient arcs are fitted. Further, the salient ellipse candidates are selected as final detections that are subject to the selection strategy, which realizes both validation and de-redundancy (clustering) functions. A complexity analysis of the method revealed that the detection time is linearly related to the number of edge points. The results of extensive experiments conducted on three public datasets demonstrate that the proposed method is approximately 75% faster than state-of-the-art methods with comparable or higher precision, and its detection time is less than 30 ms in most cases.

A real-time and precise ellipse detector via edge screening and aggregation

A fast and precise method for ellipse detection is proposed in this paper. The method aims at clearly removing the lines and curves which are not ellipse edges to improve the ellipse fitting. In arc extraction, the arcs are divided into four categories according to the gradient, and the size constraint is exploited to remove the interference lines. Then, the arc relative position constraints and the tangent lines constraint are employed to exactly group the arcs that belong to the same ellipse into a set. Finally, a post-processing approach is developed to remove the invalid ellipses. Due to the effective removal of the interference edges and the designed geometric multi-constraint, the computational costs of arc grouping and parameter estimation are dramatically reduced, and the fitting results are finely agreeable to the actual ellipse contours. The performance is evaluated with 3600 synthetic images and 1517 real images, and the experimental results demonstrate that the proposed method runs much faster than the current speed leading methods with the comparable or higher F-measure.

Arc Adjacency Matrix-Based Fast Ellipse Detection.

Fast and accurate ellipse detection is critical in certain computer vision tasks. In this paper, we propose an arc adjacency matrix-based ellipse detection (AAMED) method to fulfill this requirement. At first, after segmenting the edges into elliptic arcs, the digraph-based arc adjacency matrix (AAM) is constructed to describe their triple sequential adjacency states. Curvature and region constraints are employed to make the AAM sparse. Secondly, through bidirectionally searching the AAM, we can get all arc combinations which are probably true ellipse candidates. The cumulative-factor (CF) based cumulative matrices (CM) are worked out simultaneously. CF is irrelative to the image context and can be pre-calculated. CM is related to the arcs or arc combinations and can be calculated by the addition or subtraction of CF. Then the ellipses are efficiently fitted from these candidates through twice eigendecomposition of CM using Jacobi method. Finally, a comprehensive validation score is proposed to eliminate false ellipses effectively. The score is mainly influenced by the constraints about adaptive shape, tangent similarity, distribution compensation. Experiments show that our method outperforms the 12 state-of-the-art methods on 9 datasets as a whole, with reference to recall, precision, F-measure, and time-consumption.

Rock‐ring detection accuracy improvement in infrared satellite image with sub‐pixel edge detection

The projection of space circle can be used for relative pose measurement of satellite targets. The accuracy of elliptical parameters plays an important role in the accuracy of pose recovery. However, the quality of space visible and infrared image is poor. The traditional ellipse detection method is mainly based on the edge of pixel-accuracy-wise and the ellipse accuracy is low, resulting in pose measurement errors. In this study, a sub-pixel-accuracy-wise edge-based ellipse fitting method is proposed to improve the ellipse accuracy. To realise this goal, we improved the arc-based ellipse detection method and designed sub-pixel-edge-based ellipse detection method. Experimental results show that the ellipse accuracy fitted by sub-pixel edge coordinates is at least 50% higher than that by pixel edge coordinates, especially when the ellipse is incomplete. The author's method is the first one to propose and verify the sub-pixel edge coordinate which contributes to enhancing ellipse accuracy.

Robust Powerline Equipment Inspection System Based on a Convolutional Neural Network.

Electric power line equipment such as insulators, cut-out-switches, and lightning-arresters play important roles in ensuring a safe and uninterrupted power supply. Unfortunately, their continuous exposure to rugged environmental conditions may cause physical or electrical defects in them which may lead to the failure to the electrical system. In this paper, we present an automatic real-time electrical equipment detection and defect analysis system. Unlike previous handcrafted feature-based approaches, the proposed system utilizes a Convolutional Neural Network (CNN)-based equipment detection framework, making it possible to detect 17 different types of powerline insulators in a highly cluttered environment. We also propose a novel rotation normalization and ellipse detection method that play vital roles in the defect analysis process. Finally, we present a novel defect analyzer that is capable of detecting gunshot defects occurring in electrical equipment. The proposed system uses two cameras; a low-resolution camera that detects insulators from long-shot images, and a high-resolution camera which captures close-shot images of the equipment at high-resolution that helps for effective defect analysis. We demonstrate the performances of the proposed real-time equipment detection with up to 93% recall with 92% precision, and defect analysis system with up to 98% accuracy, on a large evaluation dataset. Experimental results show that the proposed system achieves state-of-the-art performance in automatic powerline equipment inspection.

Accurate detection of ellipses with false detection control at video rates using a gradient analysis

Accurate ellipse detection in image streams at real-time execution is an open challenge. We present a novel fast and robust ellipse detection method. The method adopts arcs selection, smart grouping, and repeated utilization of gradient information to significantly reduce the computations otherwise needed without compromising the detection effectiveness. Geometric properties calculable with few computations, such as arc smoothness, relative placement of curves, and region of confidence for ellipse centres, are utilized for this purpose. An exhaustive sensitivity analysis of the method's control parameters has been performed. It reveals range of values that support consistent performance over diverse challenging datasets with complex background, multiple differently sized ellipses, and occluded, overlapping ellipses. The method's performance is compared with six state-of-the-art detectors over four diverse datasets. Among all the tested methods, the proposed method demonstrates the best balance between detection effectiveness (the best or the second best F-measure scores) and computation time (>40 Hz) across all the datasets.

A versatile major axis voted method for efficient ellipse detection

The recent developments in the field of particle science and technology show that the shape of the particle other than size and surface chemistry are important. Most commonly, digital images captured either by microscopy or other means are analyzed by image processing methods to completely characterize the shape of the objects. Among many possible shapes, ellipse like objects are ubiquitous and encountered across several disciplines - for example - face recognition in computer vision, classification of food grains and leaves, study of particle shape effects in colloid science, etc. Detection of particle position and their orientation is therefore of great interest to quantitatively understand the phenomena under investigation. In this article we propose a major axis voted method for the detection of ellipse-like objects in 2D images which is based on the assumption that each pair of boundary pixels are on the endpoints of the minor axis of the ellipse. We show that the method thus developed is capable of detecting occluded ellipses as well as deformed ellipses.

A hybrid method for ellipse detection in industrial images

Many ellipse detection methods have been proposed for detecting ellipses in images. However, they are unsuitable for industrial images due to low signal-to-noise ratios (SNR). This paper presents an ellipse detection method combining the advantages of Hough transform(HT) based methods and the advantages of edge following methods, which is capable of detecting fragmented ellipses and is both computational and memory efficient. Our method works in two steps. In the first step, an edge following method is proposed to quickly and accurately extract the majority of ellipses. For ellipses missed in the first step, candidate regions where each may contain one missed ellipse are extracted in the second step using cluster analysis, and then a HT based method is performed on these regions to extract the missed ellipses. This can not only guarantee the accuracy of the HT based method, but also save the memory and computation time. We test the performance of our method using both synthetic images and low SNR industrial images. Experimental results demonstrate that the proposed method performs far better than existing methods in terms of recall, precision, F-measure, and reliability. Especially in term of reliability, our method has achieved a very high value close to 1 while the reliabilities of state-of-the art methods are almost less than 0.5.

Effective ellipse detection method in limited-performance embedded system for aerospace application

Up to present, there have been numerous articles on ellipse detection. However, it is still a great challenge especially for embedded application with restricted available computing and memory resources. To handle this problem, a novel and effective method is presented for ellipse detection. The method consists of two main steps: extraction of elliptic edge contours and edge contours grouping. These two steps are integrated to improve the accuracy and speed of the method. Finally, the aim of this method is to process high-resolution image for embedded system, which needs fast and accurate ellipse detection with limited computational and memory resources.