A novel stochastic mean filter based on Ornstein–Uhlenbeck process

Based on the shortcomings of the traditional edge detection algorithm easily lead to the brink of fracture and discontinuous, a new improved Pulse-coupled neural network (PCNN) is proposed to image segmentation and edge detection. Using Gaussian filter pre-processing, gray image is segmented into binary image. Based on this binary image, the edge detection is accomplished using improved PCNN, which can increase contrast of image and enhance the edge of image. Compared with correlative result using Canny operator and others in the existing references, simulation results show that our methods have both preferable results of gray image edge detection and fine applicability.

Context dependent edge detection and evaluation

Most existing methods for edge detection rely on contrast dependent thresholds. We show that a local measurement defined by the ratio of the smallest to the largest eigenvalue of the second moment matrix of filter kernels, can be used to separate smooth, low curvature curves and straight lines from noise, independent of contrast, in both color and gray level images. This is done without applying a threshold to the gradient magnitude. The edge images are defined as zero crossings in the gradient direction. The covariance matrix can easily be computed for both gray level images and color images. Further we show the potentiality of such a measure by integrating it with the Hough transform to extract long straight lines in noisy color images. The method is shown to successfully extract consistent line features from color images of a scene, captured under drastically different lightening conditions.

Edge detection plays a crucial role in image processing and computer vision, and is widely used in tasks such as object recognition and image segmentation. Traditional edge detection algorithms perform well in many applications, but there are still some shortcomings in terms of real-time performance and processing efficiency. To address this issue, a highly efficient image edge detection model combining Sobel algorithm and field programmable gate array technology was proposed. YCbCr color space conversion was performed on the image, then Sobel operator was utilized to calculate the image gradient, and adaptive thresholding method was applied to determine the edges. Finally, the model was implemented and optimized on a field programmable gate array. The experimental results showed that when the dataset size was 1000, the information retention rate of the proposed image preprocessing model was 0.89, and the structural information loss was 0.05. When the data volume was 100, the accuracy of the proposed image edge detection model was 0.90, and the root mean square error value was 0.16. The research results indicate that the proposed image edge detection model based on field programmable gate arrays has significant advantages in edge detection performance and processing efficiency. The model has high accuracy and speed in image edge recognition, which can provide certain guidance for research in the field of image edge detection

Color Image Edge Detection Based on Fuzzy Rule Based Approach

Optimal investment for asset–liability management with delay and partial information under Ornstein–Uhlenbeck process

Edge detection plays an important role in the field of image processing. Various edge detection techniques are obtained like Sobel, PSO Preweitt, Laplacian and Laplacian of Gaussian. These techniques consume some restrictions such as fixed edge thickness and some parameter like threshold is problematic to implement. Although many different edge-detection methods have been proposed for gray-scale, color, and multispectral images, they still face difficulties when extracting edge features from hyperspectral images (HSIs) that contain a large number of bands with very narrow gap in the spectral domain. High performance computing (HPC) is a technique used to assemble process and analyze more quantity of remote sensing (satellite) data which needs less processing time. HPC method is used in distributed & cluster Computing, parallel computing and following satellite data analyzing & processing approaches like geo-referencing, image edge detection, image classification, image mosaicking, Morphological/neural approach and image fusion for hyperspectral satellite data. Stimulated by means of the specific clustering of theory called gravitational theory, an original edge detection process. The conservative methods include the usage of linear time invariant filters. Recognition of edge is done by these filters as a These filters recognize an edge as a sudden conversion of grey scale pixel intensity. These techniques are well recognized and computationally effective. Canny, Sobel, Robert, Kirsch, Prewitt and LOG, on idea of spatial differential filters by using local grade. Within the less time these filters process the data and are inclined to noise. But in fuzzy technique, it does not include like this restriction, by altering the rules and output parameters, it is possible to change the thickness of the edge. In this paper, a new technique is created on fuzzy logic, which is suggested for detection of edge in digital images deprived of finding the threshold value. This method starts through segmenting the images into sections by means of fluctuating 3 $$\times $$ 3 binary matrixes. The pixels of edge are planned to a range of standards separated from each other. The results of this proposed technique is compared with the results obtained from PSO and neural network methods. This suggested technique provides a permanent effect in the lines smoothness and straightness for the straight lines and good roundness for the curved lines. And sharpness in the curves.

In this paper we give easy to verify conditions for the strong consistency of the maximum likelihood estimator (MLE) in the case when data is sampled from a parametric family of selfdecomposable distributions. The difficulty arises from the fact that standard conditions for the consistency of the MLE are based on the pdf, which, for most selfdecomposable distributions, is not available in a closed form. Instead, our conditions are based on properties of the Levy triplet (i.e. the Levy measure, the Gaussian part, and the shift) of the distribution. Further, we extend out results to certain selfdecomposable stochastic processes, and, in particular, we give conditions in the case when the data is sampled from a Levy or an Ornstein–Uhlenbeck process.

The worldwide data for COVID-19 for active, infected individuals in multiple waves show that traditional epidemic models with constant parameters are not able to capture this kind of disease behavior. We solved this major open mathematical problem in this report. We first consider the disease transmission rate for the stochastic SIRVI epidemic model, which satisfies the mean-reverting Ornstein–Uhlenbeck (OU) process, and we propose a new stochastic SIRVI model. We then showed the existence and uniqueness of the global solution and obtained sufficient conditions for the persistent mean and exponential extinction of infectious disease, which have not been given before. In the second part, we derive a nonlinear system of differential equations for the time-dependent transmission rate from the deterministic SIRVI model and present an algorithm to compute the time-dependent transmission rate directly from the given active, infected individuals’ data. We then show that the time-dependent transmission obtained from and perturbed by the Ornstein–Uhlenbeck process could be represented after using a smoothing technique using a finite linear combination of a Gaussian radial basis function, which was obtained from our algorithm. This novel computer-assisted proof provides a theoretical basis for other epidemic models and epidemic waves. Finally, some numerical solutions of the stochastic SIRVI model are presented using COVID-19 data from Saudi Arabia and Austria.

Dynamical behavior and numerical simulation of a stochastic eco-epidemiological model with Ornstein–Uhlenbeck process

Sparse inference of the drift of a high-dimensional Ornstein–Uhlenbeck process

We introduce a novel method of splitting up color spaces into different components and then performing edge detection on individual color planes. The two general approaches taken for this are monochromatic and vector based. Also a new color space will be introduced in this paper, which is an improved version of the PCA algorithm. By analyzing the results of these algorithms we are able to determine which color space and edge detector is best suited for each algorithm. We test these methods using a number of well known edge detectors and color spaces. All the algorithms are tested on 17 different color images (12 natural, 5 synthetic). To analyze the results we use Pratt's Figure of Merit and Bovik's SSIM measures.

Image edge detection based tool condition monitoring with morphological component analysis

D-optimal designs for complex Ornstein–Uhlenbeck processes

In order to solve the problems of limited computing resources on the fuselage and autonomous recognition of visual images during the real-time control of the flapping-wing flying robot, and to realize the intelligent navigation control of the flapping-wing flying robot based on vision, this paper proposes a visual image processing method and edge detection method for a flapping-wing flying robot based on cluster analysis. Using cluster analysis method to classify the visual image pixel data of the flapping-wing flying robot, and using the classic edge detection algorithm to perform edge detection on the flying robot image after clustering analysis, and reasonably extracting the visual target contour features and environmental edges of the flapping-wing flying robot feature. An experimental platform for a flapping-wing flying robot was built with a flapping-wing flying robot and an airborne vision camera. Flight experiments are carried out on the visual image processing method and image edge detection method of flying robot based on cluster analysis. The experimental results verify the feasibility and effectiveness of the method in this paper. The innovation of this paper is embodied in the application of cluster analysis method to the visual image processing of the flapping-wing flying robot, and the edge detection of the visual image of the flapping-wing flying robot.