A simple least squares method for fitting of ellipses and circles depends on border points of a two-tone image and their 3-D extensions

A Novel Four-Step Algorithm for Detecting a Single Circle in Complex Images

Single-circle detection is vital in industrial automation, intelligent navigation, and structural health monitoring. In these fields, the circle is usually present in images with complex textures, multiple contours, and mass noise. However, commonly used circle-detection methods, including random sample consensus, random Hough transform, and the least squares method, lead to low detection accuracy, low efficiency, and poor stability in circle detection. To improve the accuracy, efficiency, and stability of circle detection, this paper proposes a single-circle detection algorithm by combining Canny edge detection, a clustering algorithm, and the improved least squares method. To verify the superiority of the algorithm, the performance of the algorithm is compared using the self-captured image samples and the GH dataset. The proposed algorithm detects the circle with an average error of two pixels and has a higher detection accuracy, efficiency, and stability than random sample consensus and random Hough transform.

Method for improving the measurement accuracy of binocular stereo vision in a scattering environment

In the scattering environment, binocular stereo vision measurement technology produces large errors due to the change of refractive index of the imaging light path and the decrease in target image contrast. To address this problem, this paper proposes a method for improving the measurement accuracy of binocular stereo vision in a scattering environment combined with polarization imaging theory. First, scattering images with different polarization directions are obtained and filtered by a Gaussian low-pass filter to calculate the degree of polarization and angle of polarization. Then, the scattered light intensity is calculated by using polarization information to obtain images after removing the scattering. Second, feature extraction and matching are carried out for the images after scattering removal. Finally, the target is measured based on the binocular stereo vision measurement model. The experimental results show that when the scattering concentration is high enough, the conventional method can no longer perform measurement, but the method proposed in this paper can still obtain the target parameters at this time, and can also improve measurement accuracy by at least 46.30%. In conclusion, the proposed method provides a valuable reference to improve the accuracy of binocular stereo vision measurement in a scattering environment by reducing the interference of scattering light.

Characterization of Collagen I Fiber Thickness, Density, and Orientation in the Human Skin In Vivo Using Second-Harmonic Generation Imaging

The assessment of dermal alterations is necessary to monitor skin aging, cancer, and other skin diseases and alterations. The gold standard of morphologic diagnostics is still histopathology. Here, we proposed parameters to distinguish morphologically different collagen I structures in the extracellular matrix and to characterize varying collagen I structures in the skin with similar SAAID (SHG-to-AF Aging Index of Dermis, SHG—second-harmonic generation; AF—autofluorescence) values. Test datasets for the papillary and reticular extracellular matrix from images in 24 female subjects, 36 to 50 years of age, were generated. Parameters for SAAID, edge detection, and fast Fourier transformation directionality were determined. Additionally, textural analyses based on the grey level co-occurrence matrix (GLCM) were conducted. At first, changes in the GLCM parameters were determined in the native greyscale images and, furthermore, in the Hilbert-transformed images. Our results demonstrate a robust set of parameters for noninvasive in vivo classification for morphologically different collagen I structures in the skin, with similar and different SAAID values. We anticipate our method to enable an automated prevention and monitoring system with an age- and gender-specific algorithm.

Real-Time Docking Ring Detection Based on the Geometrical Shape for an On-Orbit Spacecraft

Docking ring is a circular hatch of spacecraft that allows servicing spacecraft to dock in various space missions. The detection of the ring is greatly beneficial to automatic capture, rendezvous and docking. Based on its geometrical shape, we propose a real-time docking ring detection method for on-orbit spacecraft. Firstly, we extract arcs from the edge mask and classify them into four classes according to edge direction and convexity. By developing the arc selection strategy, we select a combination of arcs possibly belonging to the same ellipse, and then estimate its parameters via the least squares fitting technique. Candidate ellipses are validated according to the fitness of the estimation with the actual edge pixels. The experiments show that our method is superior to the state-of-the-art methods, and can be used in real time application. The method can also be extended to other applications.

Determine turntable coordinate system considering its non-orthogonality

In industrial measurement and laboratory research, many measured objects are placed on the three-axis turntable. In this paper, we propose a fast and practical method to determine the turntable coordinate system with the aid of spatial coordinate of point cloud data. By sphere fitting, plane fitting, and point projection, the scattered point cloud data are combined together to obtain initial direction vectors. Considering the non-orthogonality of turntable, the least two pairs of skew lines are used to compute the approximate turntable center. And the intersection angles and distances between each axis are given to judge the degree of non-orthogonality. Then, based on the approximate sphere center and the initial rotation vectors, the direction vectors are, respectively, optimized in a predefined order. An experimental system is set up to validate the proposed method. Attitude parameters computed by spatial point coordinates before and after turntable calibration are employed to give the quantitative evaluation results. And the total attitude errors in three axes motion forms all reduce, which demonstrates the effectiveness and practicality of the proposed method.

Ellipse Fitting Using the Finite Rate of Innovation Sampling Principle

Standard approaches for ellipse fitting are based on the minimization of algebraic or geometric distance between the given data and a template ellipse. When the data are noisy and come from a partial ellipse, the state-of-the-art methods tend to produce biased ellipses. We rely on the sampling structure of the underlying signal and show that the x - and y -coordinate functions of an ellipse are finite-rate-of-innovation (FRI) signals, and that their parameters are estimable from partial data. We consider both uniform and nonuniform sampling scenarios in the presence of noise and show that the data can be modeled as a sum of random amplitude-modulated complex exponentials. A low-pass filter is used to suppress noise and approximate the data as a sum of weighted complex exponentials. The annihilating filter used in FRI approaches is applied to estimate the sampling interval in the closed form. We perform experiments on simulated and real data, and assess both objective and subjective performances in comparison with the state-of-the-art ellipse fitting methods. The proposed method produces ellipses with lesser bias. Furthermore, the mean-squared error is lesser by about 2 to 10 dB. We show the applications of ellipse fitting in iris images starting from partial edge contours, and to free-hand ellipses drawn on a touch-screen tablet.