Ellipse Detection Algorithm Research Articles

A Robust Real-Time Ellipse Detection Method for Robot Applications

Over the years, many ellipse detection algorithms have been studied broadly, while the critical problem of accurately and effectively detecting ellipses in the real-world using robots remains a challenge. In this paper, we proposed a valuable real-time robot-oriented detector and simple tracking algorithm for ellipses. This method uses low-cost RGB cameras for conversion into HSV space to obtain reddish regions of interest (RROIs) contours, effective arc selection and grouping strategies, and the candidate ellipses selection procedures that eliminate invalid edges and clustering functions. Extensive experiments are conducted to adjust and verify the method’s parameters for achieving the best performance. The method combined with a simple tracking algorithm executes only approximately 30 ms on a video frame in most cases. The results show that the proposed method had high-quality performance (precision, recall, F-Measure scores) and the least execution time compared with the existing nine most advanced methods on three public actual application datasets. Our method could detect elliptical markers in real-time in practical applications, detect ellipses adaptively under natural light, well detect severely blocked and specular reflection ellipses when the elliptical object was far from or close to the robot. The average detection frequency can meet the real-time requirements (>10 Hz).

Ellipse Detection Algorithm for Color Image Processing

Ellipse Detection Algorithm for Color Image Processing

Monocular-Vision-Based Relative Pose Estimation of Noncooperative Spacecraft Using Multicircular Features

Accurate relative pose (position and orientation) estimation for noncooperative target spacecraft is the basic module for on-orbit services, such as capture and repair. This article technically proposes a nonlinear optimization method based on monocular vision to estimate the relative pose for the solid-of-revolution-shaped noncooperative spacecraft. Specifically, considering the multicircular features on the target, the parameters of the ellipses derived from the circles perspective projection are first obtained by the propounded ellipse detection algorithm. Moreover, the constraint that the center of each circle falls on the center axis (normal direction) of the spacecraft is utilized to optimally solve the center position and the normal of the multicircles. In particular, the roll angle around the center axis is recovered through the geometric constraints of the solar panels, and the six-degree-of-freedom spacecraft pose estimation is then accomplished. Consequently, it is analyzed that the proposed optimization algorithm is able to provide a closed-form solution to ensure the accuracy of the relative pose and only requires limited computational resources. Finally, experimental results on the synthetic images and the physical scenes are conducted to evaluate the efficiency of the proposed approach.

Binocular stereo vision calibration based on accurate ellipse detection algorithm of direct calculation and grating conversion check

With the rapid development of science and technology, binocular vision detection technology has become one of the most important technologies because of its high precision and stability. In order to achieve high-precision three-dimensional measurement of high-speed rail train, a binocular stereo vision system is built in this paper. The visual image is gray-scale processed, and then the integral image is used to realize the edge preservation processing of local mean square deviation, and then the denoised image is enhanced. At the same time, FAST algorithm based on accelerated segmentation detection is proposed to extract feature points. In order to solve the problem of camera calibration, this paper proposes a high-precision binocular stereo vision calibration algorithm based on direct calculation and grating conversion calibration. Firstly, the reliable information points are determined from the two-dimensional image, and then the edge image is obtained by effectively detecting the ellipse contour according to the set of information points. Finally, the ellipse parameters are extracted by using the ellipse contour information, and finally the camera parameters are solved by using the ring point. Compared with the traditional calibration method, the experimental results show that the average reprojection error of the two cameras is about 0.1 pixel, and the data accuracy is high, which proves the feasibility and theoretical correctness of the experimental method.

A method of locating the 3D centers of retroreflectors based on deep learning

Purpose Laser-based measurement techniques offer various advantages over conventional measurement techniques, such as no-destructive, no-contact, fast and long measuring distance. In cooperative laser ranging systems, it’s crucial to extract center coordinates of retroreflectors to accomplish automatic measurement. To solve this problem, this paper aims to propose a novel method. Design/methodology/approach We propose a method using Mask RCNN (Region Convolutional Neural Network), with ResNet101 (Residual Network 101) and FPN (Feature Pyramid Network) as the backbone, to localize retroreflectors, realizing automatic recognition in different backgrounds. Compared with two other deep learning algorithms, experiments show that the recognition rate of Mask RCNN is better especially for small-scale targets. Based on this, an ellipse detection algorithm is introduced to obtain the ellipses of retroreflectors from recognized target areas. The center coordinates of retroreflectors in the camera coordinate system are obtained by using a mathematics method. Findings To verify the accuracy of this method, an experiment was carried out: the distance between two retroreflectors with a known distance of 1,000.109 mm was measured, with 2.596 mm root-mean-squar error, meeting the requirements of the coarse location of retroreflectors. Research limitations/implications The research limitations/implications are as follows: (i) As the data set only has 200 pictures, although we have used some data augmentation methods such as rotating, mirroring and cropping, there is still room for improvement in the generalization ability of detection. (ii) The ellipse detection algorithm needs to work in relatively dark conditions, as the retroreflector is made of stainless steel, which easily reflects light. Originality/value The originality/value of the article lies in being able to obtain center coordinates of multiple retroreflectors automatically even in a cluttered background; being able to recognize retroreflectors with different sizes, especially for small targets; meeting the recognition requirement of multiple targets in a large field of view and obtaining 3 D centers of targets by monocular model-based vision.

Automatic Fetal Biometry Evaluation in Ultrasound Images Using a Deep Learning-Based Approach

Background: The 2D fetal ultrasound biometrics have been extensively used to establish (or confirm) the gestational age of the fetus, estimate its size and weight, and identify growth patterns and abnormalities. Typically, an ultrasound examination is routinely performed between 18 and 22 weeks of pregnancy to evaluate the growth of the fetus by measuring its head, abdomen, and femur. Automatic methods for fetal biometric measurements have been investigated recently to reduce intra- and inter-observer variability and create more accurate and reproducible measurements. Objectives: In this paper, we proposed a deep learning-based approach to calculate fetal biometry parameters automatically. Patients and Methods: The fetal biometry parameters came from the fetal head, abdomen, and femur evaluation. Head circumference (HC) and biparietal diameter (BPD) were related to the fetal head, whereas abdominal circumference (AC) was related to the fetal abdomen and femur length (FL) was related to the fetal femur. Figure 1 shows these parameters in ultrasound images. Our prepared dataset included three parts, as follows: (1) 1334 2D ultrasound images of the fetal head in the standard plane. This dataset was publically available from the automated measurement of fetal head circumference challenge. (2) 158 2D ultrasound images of the fetal abdomen in the standard plane. The dataset was gathered from Alvand Medical Imaging Center, Tehran, Iran, by expert radiologists. (3) 315 2D ultrasound images of the fetal femur in the standard plane. The dataset was gathered from two distinct centers: (i) Alvand Medical Imaging Center, Tehran, Iran, and (ii) Laleh Hospital, Tehran, Iran. We trained and evaluated a novel convolutional network for the segmentation of fetal head and abdomen. The proposed network, called MFP-Unet, was a combination of Unet and feature pyramid network (FPN). The network architecture is depicted in Figure 2. For fetal femur, we had a pre-processing step. We used the superpixel algorithm to remove darker parts of the image, as the femur was typically the brightest part of the US image. Then, we applied an image saliency algorithm to represent the salient features of the image. Finally, MFP-Unet was trained on these pre-processed images to segment the femur. After the segmentation process, image analysis algorithms achieved all of the required measures. An ellipse detection algorithm on the segmented area of the fetal head was to measure the HC and BPD while an ellipse detection algorithm on the segmented area of the fetal abdomen was to measure the AC. Finally, a skeletonization algorithm achieved the femur length. Results: We used the mean absolute difference (MAD) and root mean square error (RMSE) for the measurement errors. The values of MAD and RMSE were 0.23 mm and 0.11 mm for BPD, 0.13 mm and 0.09 mm for HC, 0.17 mm and 0.08 mm for AC, and 0.18 mm and 0.12 mm for FL, respectively. Table 1 shows the results. The correlation between automatic and manual measurements was evaluated by correlation graphs. The R values were 0.97, 0.91, and 0.97 for HC, BPD, and FL, respectively. Conclusion: According to the results, we proposed a robust and useful algorithm for automatic fetal biometry evaluation that could be extended to nuchal translucency (NT) measurement based on the providing dataset.

Arc-support Line Segments Revisited: An Efficient High-quality Ellipse Detection.

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 arc-support 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.

Speed-Up Automatic Quadcopter Position Detection by Sensing Propeller Rotation

Determining unit position is very important for the indoor autonomous aerial robots. In prior research, we used externally installed cameras to detect natural unit features to reduce the burden of measuring position, focusing on the elliptical trajectory of the rotating rotors while the unit was in flight as the natural feature of interest. Ellipse detection within images allows the calculation of unit position. An outstanding problem is that ellipse detection takes a considerable amount of time, and in some environments it is difficult to distinguish between the rotor and the background. In this paper, we investigate methods for addressing these problems, proposing a novel algorithm for fast ellipse detection. Furthermore, we record and visualize change in the rotating rotor pattern over time to enable detection against previously problematic backgrounds. For verification, we fly a general-purpose unit in a variety of environments and measure unit position. The results show that the proposed method reduces processing times for ellipse detection and that position can be measured without depending on the composition of flight environment, and thus that unit position detection is improved through the use of infrastructure cameras.

Ellipse proposal and convolutional neural network discriminant for autonomous landing marker detection

AbstractAutonomous landing in complex environments is a critical problem for unmanned aerial vehicle autonomous control, and efficiently detecting landing identification mark in real‐world scenario is still challenging. Due to the limited computational power of airborne computing equipment, current target detection algorithms cannot meet the demand efficiently. In this paper, we proposed a new landing marker detection algorithm for autonomous landing systems in a real environment. We used an ellipse detection algorithm to detect the ellipse landmark or other elliptical objects. Furthermore, convolution neural networks were utilized to obtain the correct landmarks, which is robust, fast and can achieve a speed of 25 fps with 720p resolution video on an Intel NUC onboard computer. Unlike the other methods, the accuracy and speed of our algorithm is verified in a real‐time application with more harsh conditions. During system testing, the flight system can detect the object above 20 m, track it, and automatically land on it with this vision algorithm. The algorithm has helped us achieve the first position at Mohamed Bin Zayed International Robotics Challenge in Abu Dhabi this year.

Fast and robust ellipse detection algorithm for head-mounted eye tracking systems

In head-mounted eye tracking systems, the correct detection of pupil position is a key factor in estimating gaze direction. However, this is a challenging issue when the videos are recorded in real-world conditions, due to the many sources of noise and artifacts that exist in these scenarios, such as rapid changes in illumination, reflections, occlusions and an elliptical appearance of the pupil. Thus, it is an indispensable prerequisite that a pupil detection algorithm is robust in these challenging conditions. In this work, we present one pupil center detection method based on searching the maximum contribution point to the radial symmetry of the image. Additionally, two different center refinement steps were incorporated with the aim of adapting the algorithm to images with highly elliptical pupil appearances. The performance of the proposed algorithm is evaluated using a dataset consisting of 225,569 head-mounted annotated eye images from publicly available sources. The results are compared with the better algorithm found in the bibliography, with our algorithm being shown as superior.

Sensor Guided Robot Path Generation for Surface Repair Tasks on a Large-Scale Buoyancy Module

In the offshore oil and gas industry, the repair of damaged drilling riser buoyancy modules (DRBMs) currently involves the removal of defective regions using hand-held abrasive tools. This operation is not only time consuming, but also exposes the worker to hazardous materials generated by the grinding process. In order to increase productivity and minimize occupational health hazards, an automated robotic repair system is developed. The DRBM has a large and almost featureless surface making it particularly challenging to accurately and efficiently detect and localize all the defective regions on the surface. To address these challenges, an eye-in-hand monocular vision system is used to generate the DRBM's global surface map. The defective regions can then be identified from the map using ellipse detection algorithms, and subsequently removed with a robotic manipulator. Experimental results have shown that the proposed solution is able to accurately and efficiently remove defective regions on the surface of DRBMs.

A fast and effective ellipse detector for embedded vision applications

Several papers addressed ellipse detection as a first step for several computer vision applications, but most of the proposed solutions are too slow to be applied in real time on large images or with limited hardware resources. This paper presents a novel algorithm for fast and effective ellipse detection and demonstrates its superior speed performance on large and challenging datasets. The proposed algorithm relies on an innovative selection strategy of arcs which are candidate to form ellipses and on the use of Hough transform to estimate parameters in a decomposed space. The final aim of this solution is to represent a building block for new generation of smart-phone applications which need fast and accurate ellipse detection also with limited computational resources.

3-Parameter Hough Ellipse Detection Algorithm for Accurate Location of Human Eyes

Accurately positioning the Human Eyes plays an important role in the detection of the fatigue driving. In order to improve the performance of positioning of human face and eyes, an accurately positioning method of the human eyes is proposed based on the 3-parameter Hough ellipse detection. Firstly, the human face area is divided by using the skin color clustering and segmentation algorithm. Then, the segmented image is filtered by using its geometric structure and the approximate positions of the human face area and eyes are calculated. Finally, on the basis of the spinning cone-shape eye model, the position of human face and eyes is accurately determined by using the 3-parameter Hough transformation ellipse detection algorithm. The different images of human face are used to test the performance of the proposed method. The experimental results show that the extreme value of upper and lower eyelids and the actual position is 0.104 and the proposed algorithm has higher positioning accuracy

Automatic detection of the macula in retinal fundus images using multilevel thresholding

The paper proposes a novel method for detection of macula in medical image of human eye – retinal fundus images, that can be used in ophthalmology for detecting various eye’s diseases as glaucoma, diabetic retinopathy or macula oedema. The method utilizes an approach of multilevel thresholding ofred channel of fundus retinal image. Subsequently, thethresholded layers are preprocessed by application of median blur filter. And algorithm for ellipse detection from OpenCVlibrary is used on all thresholding level. Then proposed technique analyzes detection and evaluates position of macula in fundus image. The precision of the method is evaluated on dataset from public fundus image library DRIVE. The results were 100% for image with visible macula.

A novel tool for segmenting 3D medical images based on generalized cylinders and active surfaces

Three-dimensional (3D) medical imaging has been incorporated in routine clinical practice, since the required infrastructure has become increasingly affordable. New algorithms and applications are needed to serve the additional image processing and analysis functions in 3D space. In this work we propose a system for semi-automatic modeling and segmentation of elongated salient and anatomical objects in 3D medical images. The proposed methodology is based on a novel mathematical formalization of a well-known class of geometric primitives, namely generalized cylinders (GCs), which exhibits advantages over the existing parametric definition. Since the anatomical objects have to be modeled by their intersection with the transverse image planes, the proposed methodology includes also a new seeded region growing (SRG) segmentation algorithm for ellipse detection in 2D images, based on a priori shape knowledge. Finally, the resulting GC model is used to initialize an active surface (AS) segmentation method, in order to accurately delineate the required object. In this work we present the proposed algorithms in detail, along with the evaluation of the accuracy of the model-based segmentation by experts. Results show that elongated objects like the aorta and the trachea may be segmented with sensitivity between 90% and 95%. The proposed SRG-ellipse detector requires minimal user-initialization and its executions requires only few seconds for each image slice on an average laptop. The evolution of the AS requires less than one second per iteration for a typical CT image. Comparisons are provided with state of the art semi-automatic medical image processing software, which validate the merit of the proposed work.

Detección Eficiente de Elipses en Imágenes. Aplicación al Posicionamiento 3D de un Robot Industrial

In this paper, we present a ellipse detection algorithm developed to measure the 3-D position of a circular feature in a robotic application. The algorithm uses a RANSAC stochastic procedure whose efficiency has been significantly improved, substituting the random sampling with a guided sampling on the curve segments in the image. The contours of the image are first split analyzing their curvature. Then the curve segments are sorted according to their likelihood to be part of the ellipse. We have used the length as a prior indicator of this likelihood. The RANSAC algorithm starts considering only the longer curve segments whilst shorter curve segments are progressively incorporated. This strategy notably diminishes the amount of samples needed and makes the algorithm suitable for real time.

Robust ellipse detection based on hierarchical image pyramid and Hough transform

In this research we propose a fast and robust ellipse detection algorithm based on a multipass Hough transform and an image pyramid data structure. The algorithm starts with an exhaustive search on a low-resolution image in the image pyramid using elliptical Hough transform. Then the image resolution is iteratively increased while the candidate ellipses with higher resolution are updated at each step until the original image resolution is reached. After removing the detected ellipses, the Hough transform is repeatedly applied in multiple passes to search for remaining ellipses, and terminates when no more ellipses are found. This approach significantly reduces the false positive error of ellipse detection as compared with the conventional randomized Hough transform method. The analysis shows that the computing complexity of this algorithm is Θ(n(5/2)), and thus the computation time and memory requirement are significantly reduced. The developed algorithm was tested with images containing various numbers of ellipses. The effects of noise-to-signal ratio combined with various ellipse sizes on the detection accuracy were analyzed and discussed. Experimental results revealed that the algorithm is robust to noise. The average detection accuracies were all above 90% for images with less than seven ellipses, and slightly decreased to about 80% for images with more ellipses. The average false positive error was less than 2%.

Quantization-free parameter space reduction in ellipse detection

A new ellipse detection algorithm based on Hough transform and Direct Least Square method is presented. Pair of edges equidistant to ellipse center are used to determine ellipse center location. Normal distribution is used to model the presence probability of ellipse edges from ellipse center. Only edges pertinent to ellipse are passed onto Direct Least Square method for ellipse fitting in order to determine the rest of ellipse parameters. The average time complexity of the algorithm is cO(n2) where c is a fraction. The behavior and merits of the proposed algorithm are shown through several applications.

Counting touching cell nuclei using fast ellipse detection to assess in vitro cell characteristics: a feasibility study

In this article, we describe a new image analysis software that allows rapid segmentation and separation of fluorescently stained cell nuclei using a fast ellipse detection algorithm. Detection time ranged between 1.84 and 3.14 s. Segmentation results were compared with manual evaluation. The achieved over-segmentation rate was 0.11 (0.1 double counts and 0.01 false positive detections), and the under-segmentation rate was of 0.03 over all images. We demonstrate the applicability of the proposed algorithm to automated counting of fluorescent-labeled cell nuclei and to tissue characterization. Moreover, the performance of the proposed algorithm is compared with preexisting automated image analysis techniques described by others.

Fast algorithm for detection of reference spheres in digital panoramic radiography

In this paper, an algorithm for detection of reference spheres from digital panoramic radiographic images is presented. The proposed algorithm was tested on a database of 107 digital panoramic radiographic images which were used for dental diagnostics. Results show that the proposed method exhibits for detection of reference spheres, a sensitivity of 97.33% and specificity of 93.85%. Performance time differed between 0.55 and 2.36 s depending on image size. The aim of this work was to provide a fast ellipse detection algorithm to reduce measuring time on preoperative implant planning by lowering the computational cost.