Candidate Circles Research Articles

A robust circle detector with regionalized radius aid

The detection of circles in geometric shapes is highly valued in computer vision and pattern recognition. Conventionally, the least-squares fitting is sensitive to occlusion or noise and prone to false circles. Therefore, this paper proposes a novel algorithm for robust circle detection using the least-squares fitting method combined with regionalized radius aid on the arc and chord lengths. To reduce edge noise impact, we present an edge pruning method to prune non-curve edge branch ports. Furthermore, we extract arcs based on the inflection points and sharp corners of the approximate line segment. Next, curves that belong to the arcs obtain circle parameters according to the regionalized radius aided the least-squares method. Then, valid circles are obtained by considering two distance deviations to verify the candidate circles. Finally, valid arcs that belong to the same circle are combined and refitted, wherein the most representative arc is used as the basis for the refitting of all arcs. All experiments are conducted on real images from four publicly diverse datasets (one of them is the one we built). The detection results are compared with those of representative state-of-the-art algorithms, and the proposed algorithm has several advantages based on the comparison results: more robust to noise, effective rejection of false circles, and better performance.

A Fast Circle Detection Algorithm Based on Information Compression.

Circle detection is a fundamental problem in computer vision. However, conventional circle detection algorithms are usually time-consuming and sensitive to noise. In order to solve these shortcomings, we propose a fast circle detection algorithm based on information compression. First, we introduce the idea of information compression, which compresses the circular information on the image into a small number of points while removing some of the noise through sharpness estimation and orientation filtering. Then, the circle parameters stored in the information point are obtained by the average sampling algorithm with a time complexity of to obtain candidate circles. Finally, we set different constraints on the complete circle and the defective circle according to the sampling results and find the true circle from the candidate circles. The experimental results on the three datasets show that our method can compress the circular information in the image into 1% of the information points, and compared to RHT, RCD, Jiang, Wang and CACD, Precision, Recall, Time and F-measure are greatly improved.

A Framework to Automatic Detect Center Pivots Using Land Use and Land Cover Data

Water management is a key field to support life and economic activity nowadays. The greatly increased mechanization of agriculture, mainly through center pivot irrigation systems, represents a big challenge to control this resource. Irrigated agriculture makes up the large majority of consumptive water use, therefore it is important to identify and quantify these systems. Currently, with 6.95x10⁶ ha, Brazil is among the 10 largest countries in irrigation areas in the world. In this study, a combined Computer Vision and Machine Learning approach is proposed for the identification of center pivots in remote sensing images. The methodology is based on Circular Hough Transform (CHT) and Balanced Random Forest (BRF) classifier using vegetation indices NDVI/SAVI generated from Landsat 8 images and Land Use and Land Cover (LULC) data provided by project MapBiomas. The candidate's circles of pivots identified on images are filtered based on vegetation behavior and shape characteristics of these areas. Our approach was able to detect 7358 pivots, reaching 83.86% of Recall for 52 scenes analyzed overall Brazil compared with mapping done by the Brazilian National Water and Sanitation Agency (ANA). In some scenes, the Recall reaches up to 100%.

A fast and accurate circle detection algorithm based on random sampling

Circle detection in digital images is an important problem in computer vision, pattern recognition, and artificial intelligence. However, the common circle detection strategies, including random sample consensus, randomized Hough transform, and randomized circle detection, have a very low sampling efficiency and thus a slow detection speed, owing to aimless random sampling. This paper proposes a fast and accurate randomized circle detection algorithm, with the aim to improve the speed and accuracy of circle detection based on random sampling. The proposed algorithm mainly focuses on four aspects: calculating circle parameters, determining candidate circles, searching for true circle, and improving detection accuracy. To verify the effectiveness of our algorithm, contrastive experiments were conducted on lots of synthetic and real images. The results show that our algorithm achieved much higher detection speed and accuracy than random sample consensus, randomized Hough transform, and randomized circle detection, and realized similar robustness as the three contrastive strategies. The research ideas can also be applied to ellipse detection.

An occlusion-resistant circle detector using inscribed triangles

Circle detection is a critical issue in pattern recognition and image analysis. Conventional geometry-based methods such as tangent or symmetry are sensitive to noise or occlusion. Area computation is more robust against noise, because it avoids differential calculations. Inspired by this characteristic, we present a novel method for fast circle detection using inscribed triangles. The proposed algorithm, which is robust to noise and resistant to occlusion, first extracts circular arcs by approximating line segments and identifying inflection points and sharp corners. To speed up the computation, irrelevant segments are filtered out through the triangle inequality. Arcs that belong to the same circle are then combined according to the position constraint and the inscribed triangle constraint. The circle parameters are further estimated by inscribed triangles based upon the Theil-Sen estimator and linear error refinement without the dependence of least-square fitting but still with the equivalent accuracy. Finally, candidate circles are verified to prune false positives through an inlier ratio rule, which jointly considers both distance and angle deviations. Extensive experiments are conducted on synthetic images including overlapping circles, and real images from four diverse datasets (three publicly available and one we built). Results are compared with those of representative state-of-the-art methods, and the proposed method is demonstrated to embraces several advantages: resistant to occlusion, more robust to noise, and better performance and efficiency.

A Robust Multi-Circle Detector Based on Horizontal and Vertical Search Analysis Fitting with Tangent Direction

In this paper, we propose an efficient Multi-Circle detector which follows the fixed search order. The method makes use of horizontal and vertical search to realize circle detection, which is named as HVCD. First, this method computes edge areas in a given image. The edge areas could be divided into some regions by means of region growing. Each of regions could be efficiently searched to achieve not only one-pixel wide edges but edge segments as well. Next, the candidate circles can be extracted from every edge segment. Finally, the circle candidates could be validated with the help of Helmholtz principle. Experimental results demonstrate that HVCD could effectively detect circles on synthetic and natural images on the one hand; on the other hand, HVCD here could solve the weakness in the process of circle Hough transform implementation and EDcircles implementation.

Automatic multi‐circle detection on images using the teaching learning based optimisation algorithm

Circle detection has numerous applications towards industry, robotics, and science in general. Therefore, a significant effort has been made in order to develop an accurate and fast method for circle extraction. Commonly, different techniques such as the ones based on the Hough transform have been widely used because of their robustness. However, these techniques usually demand a considerable computational load and large storage, and therefore meta-heuristic approaches such as evolutionary and swarm-based algorithms have been studied as an alternative. This study introduces a circle-detection method based on a recently proposed meta-heuristic technique: the teaching learning based optimisation algorithm, which is a population-based technique that is inspired by the teaching and learning processes. The algorithm uses the encoding of three points as candidate circles over the edge image. To evaluate if such candidate circles are actually present within the edge map, an objective function is used to guide the search. To validate the efficacy of the proposed approach, several tests using noisy and complex images as input were carried out, and the results were compared with different approaches for circle detection.

Artificial Bee Colony (ABC) algorithm and its use in digital image processing

Classical methods often face great difficulties in solving image processing problems in images containing noise and distortions. Under such conditions, the use of bio-inspired optimization approaches has been extended. This paper explores the use of the Artificial Bee Colony (ABC) algorithm for digital image processing seen as an optimization problem. ABC is a heuristic algorithm motivated by the biological behaviour of honey-bees which has been successfully employed to solve complex optimization problems. In this paper, image segmentation and circle detection tasks are considered as examples, both issues approached as optimization problems. In segmentation, an image 1-D histogram is approximated through a Gaussian mixture model whose parameters are calculated by the ABC algorithm. On the other hand, the circle detector uses a combination of three edge points as parameters to construct candidate circles. A matching function determines is such candidate circles are actually present in the image. Experimental results show that the generated solutions are able to solve properly the considered problems.

Randomized circle detection with isophotes curvature analysis

Circle detection is a critical issue in image analysis and object detection. Although Hough transform based solvers are largely used, randomized approaches, based on the iterative sampling of the edge pixels, are object of research in order to provide solutions less computationally expensive. This work presents a randomized iterative work-flow, which exploits geometrical properties of isophotes in the image to select the most meaningful edge pixels and to classify them in subsets of equal isophote curvature. The analysis of candidate circles is then performed with a kernel density estimation based voting strategy, followed by a refinement algorithm based on linear error compensation. The method has been applied to a set of real images on which it has also been compared with two leading state of the art approaches and Hough transform based solutions. The achieved results show how, discarding up to 57% of unnecessary edge pixels, it is able to accurately detect circles within a limited number of iterations, maintaining a sub-pixel accuracy even in the presence of high level of noise.

Circle detection on images based on the Clonal Selection Algorithm (CSA)

Bio-inspired computing has demonstrated to be useful in several application areas. Over the last decade, new bio-inspired algorithms have emerged with applications for detection, optimisation and classification for use in computer vision tasks. On the other hand, automatic circle detection in digital images is considered an important and complex task for the computer vision community. Consequently, a tremendous amount of research has been devoted to find an optimal circle detector. This article presents an algorithm for the automatic detection of circular shapes from complicated and noisy images with no consideration of the conventional Hough transform principles. The proposed algorithm is based on newly developed Artificial Immune Optimisation (AIO) technique, known as the Clonal Selection Algorithm (CSA). The CSA is an effective method for searching and optimising following the Clonal Selection Principle (CSP) in the human immune system which generates a response according to the relationship between antigens (Ags), i.e. patterns to be recognised and antibodies (Abs), i.e. possible solutions. The algorithm uses the encoding of three points as candidate circles (x,y,r) over the edge image. An objective function evaluates if such candidate circles (Ab) are actually present in the edge image (Ag). Guided by the values of this objective function, the set of encoded candidate circles are evolved using the CSA so that they can fit to the actual circles on the edge map of the image. Experimental results over several synthetic as well as natural images with varying range of complexity validate the efficiency of the proposed technique with regard to accuracy, speed and robustness.

A robust circle detection algorithm based on top-down least-square fitting analysis

In this paper, we propose a robust and efficient circle detector, which achieves accurate results with a controlled number of false detections and requires no parameter tuning. The proposed algorithm consists of three steps as follows. First, we propose a novel edge point chaining method to extract Canny edge segments (i.e., contiguous and sequential chains of Canny edge points). Second, we split each edge segment into several smooth sub-segments, and detect candidate circles within each obtained sub-segment based on top-down least-square fitting analysis. Third, we employ Desolneux et al.’s method to reject the false detections. Experimental results demonstrate that the proposed method is efficient and more robust than the state-of-the-art algorithm EDCircles.

Automatic detection and quantification of WBCs and RBCs using iterative structured circle detection algorithm.

Segmentation and counting of blood cells are considered as an important step that helps to extract features to diagnose some specific diseases like malaria or leukemia. The manual counting of white blood cells (WBCs) and red blood cells (RBCs) in microscopic images is an extremely tedious, time consuming, and inaccurate process. Automatic analysis will allow hematologist experts to perform faster and more accurately. The proposed method uses an iterative structured circle detection algorithm for the segmentation and counting of WBCs and RBCs. The separation of WBCs from RBCs was achieved by thresholding, and specific preprocessing steps were developed for each cell type. Counting was performed for each image using the proposed method based on modified circle detection, which automatically counted the cells. Several modifications were made to the basic (RCD) algorithm to solve the initialization problem, detecting irregular circles (cells), selecting the optimal circle from the candidate circles, determining the number of iterations in a fully dynamic way to enhance algorithm detection, and running time. The validation method used to determine segmentation accuracy was a quantitative analysis that included Precision, Recall, and F-measurement tests. The average accuracy of the proposed method was 95.3% for RBCs and 98.4% for WBCs.

White Blood Cell Segmentation by Circle Detection Using Electromagnetism-Like Optimization

Medical imaging is a relevant field of application of image processing algorithms. In particular, the analysis of white blood cell (WBC) images has engaged researchers from fields of medicine and computer vision alike. Since WBCs can be approximated by a quasicircular form, a circular detector algorithm may be successfully applied. This paper presents an algorithm for the automatic detection of white blood cells embedded into complicated and cluttered smear images that considers the complete process as a circle detection problem. The approach is based on a nature-inspired technique called the electromagnetism-like optimization (EMO) algorithm which is a heuristic method that follows electromagnetism principles for solving complex optimization problems. The proposed approach uses an objective function which measures the resemblance of a candidate circle to an actual WBC. Guided by the values of such objective function, the set of encoded candidate circles are evolved by using EMO, so that they can fit into the actual blood cells contained in the edge map of the image. Experimental results from blood cell images with a varying range of complexity are included to validate the efficiency of the proposed technique regarding detection, robustness, and stability.

Multi-circle detection on images inspired by collective animal behavior

Hough transform (HT) has been the most common method for circle detection that delivers robustness but adversely demands considerable computational efforts and large memory requirements. As an alternative to HT-based techniques, the problem of shape recognition has also been handled through optimization methods. In particular, extracting multiple circle primitives falls into the category of multi-modal optimization as each circle represents an optimum which must be detected within the feasible solution space. However, since all optimization-based circle detectors focus on finding only a single optimal solution, they need to be applied several times in order to extract all the primitives which results on time-consuming algorithms. This paper presents an algorithm for automatic detection of multiple circular shapes that considers the overall process as a multi-modal optimization problem. In the detection, the approach employs an evolutionary algorithm based on the way in which the animals behave collectively. In such an algorithm, searcher agents emulate a group of animals which interact to each other using simple biological rules. These rules are modeled as evolutionary operators. Such operators are applied to each agent considering that the complete group maintains a memory which stores the optimal solutions seen so-far by applying a competition principle. The detector uses a combination of three non-collinear edge points as parameters to determine circle candidates (possible solutions). A matching function determines if such circle candidates are actually present in the image. Guided by the values of such matching functions, the set of encoded candidate circles are evolved through the evolutionary algorithm so that the best candidate (global optimum) can be fitted into an actual circle within the edge-only image. Subsequently, an analysis of the incorporated memory is executed in order to identify potential local optima which represent other circles. Experimental results over several complex synthetic and natural images have validated the efficiency of the proposed technique regarding accuracy, speed and robustness.

Fast algorithm for multiple-circle detection on images using learning automata

Hough transform has been the most common method for circle detection exhibiting robustness but adversely demanding a considerable computational load and large storage. Alternative approaches include heuristic methods that employ iterative optimisation procedures for detecting multiple circles under the inconvenience that only one circle can be marked at each optimisation cycle demanding a longer execution time. In contrast, learning automata (LA) is a heuristic method to solve complex multi-modal optimisation problems. Although LA converges to just one global minimum, the final probability distribution holds valuable information regarding other local minima which have emerged during the optimisation process. The detection process is considered as a multi-modal optimisation problem, allowing the detection of multiple circular shapes through only one optimisation procedure. The algorithm uses a combination of three edge points as parameters to determine circles candidates. A reinforcement signal determines whether such circle candidates are actually present at the image. Guided by the values of such reinforcement signal, the set of encoded candidate circles are evolved using the LA so that they can fit into actual circular shapes over the edge-only map of the image. The overall approach is a fast multiple-circle detector despite facing complicated conditions.

EDCircles: A real-time circle detector with a false detection control

We propose a real-time, parameter-free circle detection algorithm that has high detection rates, produces accurate results and controls the number of false circle detections. The algorithm makes use of the contiguous (connected) set of edge segments produced by our parameter-free edge segment detector, the Edge Drawing Parameter Free (EDPF) algorithm; hence the name EDCircles. The proposed algorithm first computes the edge segments in a given image using EDPF, which are then converted into line segments. The detected line segments are converted into circular arcs, which are joined together using two heuristic algorithms to detect candidate circles and near-circular ellipses. The candidates are finally validated by an a contrario validation step due to the Helmholtz principle, which eliminates false detections leaving only valid circles and near-circular ellipses. We show through experimentation that EDCircles works real-time (10–20ms for 640×480 images), has high detection rates, produces accurate results, and is very suitable for the next generation real-time vision applications including automatic inspection of manufactured products, eye pupil detection, circular traffic sign detection, etc.

Efficient symmetry-based screening strategy to speed up randomized circle-detection

Randomized approaches for circle detections are often used for the advantages of less computational time and memory requirements. However, randomized approaches involve examining a large number of candidate circles and may not be suitable for real-time applications. In this paper, a screening strategy based on the symmetric property of the circle is adopted to select the promising candidates for further investigation, resulting in substantial reduction in the computational time while maintaining the accuracy. Empirical results show that, under the same accuracy level, the proposed symmetry-based method achieves the improvement ratios of 40%–90% on the execution-time when compared to four state-of-the-art randomized methods.

Efficient randomized Hough transform for circle detection using novel probability sampling and feature points

This paper presents an efficient randomized Hough transform algorithm for circle detection. It optimizes the methods for determining sample points and finding candidate circles. Due to these two optimizations, sampling validity is improved and many false circles are prevented from being regarded as candidate circles. Experimental results demonstrate that the proposed algorithm, which features a strong robustness and high resolution, can dramatically speed up circle detection as compared to other algorithms. It can also be applied to detect ellipses.

Circle detection on images using learning automata

The outcome of Turing’s seminal work, originally proposed as a simple operational definition of intelligence, delivered several computer applications for solving complex engineering problems such as object detection and pattern recognition. Among such issues, circle detection over digital images has received considerable attention from the computer vision community over the last few years. This chapter presents an algorithm for the automatic detection of circular shapes from complicated and noisy images with no consideration of conventional Hough transform principles. The proposed algorithm is based on Learning Automata (LA) which is a probabilistic optimization method that explores an unknown random environment by progressively improving the performance via a reinforcement signal. The approach uses the encoding of three non-collinear points as a candidate circle over the edge image. A reinforcement signal indicates if such candidate circles are actually present in the edge map. Guided by the values of such reinforcement signal, the probability set of the encoded candidate circles is modified through the LA algorithm so that they can fit to the actual circles on the edge map. Experimental results over several complex synthetic and natural images have validated the efficiency of the proposed technique regarding accuracy, speed and robustness.

Automatic multiple circle detection based on artificial immune systems

Hough transform (HT) has been the most common method for circle detection, exhibiting robustness but adversely demanding a considerable computational load and large storage. Alternative approaches for multiple circle detection include heuristic methods built over iterative optimization procedures which confine the search to only one circle per optimization cycle yielding longer execution times. On the other hand, artificial immune systems (AIS) mimic the behavior of the natural immune system for solving complex optimization problems. The clonal selection algorithm (CSA) is arguably the most widely employed AIS approach. It is an effective search method which optimizes its response according to the relationship between patterns to be identified, i.e. antigens (Ags) and their feasible solutions also known as antibodies (Abs). Although CSA converges to one global optimum, its incorporated CSA-Memory holds valuable information regarding other local minima which have emerged during the optimization process. Accordingly, the detection is considered as a multi-modal optimization problem which supports the detection of multiple circular shapes through only one optimization procedure. The algorithm uses a combination of three non-collinear edge points as parameters to determine circles candidates. A matching function determines if such circle candidates are actually present in the image. Guided by the values of such function, the set of encoded candidate circles are evolved through the CSA so the best candidate (global optimum) can fit into an actual circle within the edge map of the image. Once the optimization process has finished, the CSA-Memory is revisited in order to find other local optima representing potential circle candidates. The overall approach is a fast multiple-circle detector despite considering complicated conditions in the image.