Academic literature on the topic 'Neighbour Mean Interpolation'

This chapter deals with image processing in the specific area of image zooming via interpolation. The authors employ bivariate rational cubic ball function defined on rectangular meshes. These bivariate spline have six free parameters that can be used to alter the shape of the surface without needed to change the data. It also can be used to refine the resolution of the image. In order to cater the image zomming, they propose an efficient algorithm by including image downscaling and upscaling procedures. To measure the effectiveness of the proposed scheme, they compare the performance based on the value of peak signal-to-noise ratio (PSNR) and root mean square error (RMSE). Comparison with existing schemes such as nearest neighbour (NN), bilinear (BL), bicubic (BC), bicubic Hermite (BH), and existing scheme Karim and Saaban (KS) have been made in detail. From all numerical results, the proposed scheme gave higher PSNR value and smaller RMSE value for all tested images.

This chapter deals with image processing in the specific area of image zooming via interpolation. The authors employ bivariate rational cubic ball function defined on rectangular meshes. These bivariate spline have six free parameters that can be used to alter the shape of the surface without needed to change the data. It also can be used to refine the resolution of the image. In order to cater the image zomming, they propose an efficient algorithm by including image downscaling and upscaling procedures. To measure the effectiveness of the proposed scheme, they compare the performance based on the value of peak signal-to-noise ratio (PSNR) and root mean square error (RMSE). Comparison with existing schemes such as nearest neighbour (NN), bilinear (BL), bicubic (BC), bicubic Hermite (BH), and existing scheme Karim and Saaban (KS) have been made in detail. From all numerical results, the proposed scheme gave higher PSNR value and smaller RMSE value for all tested images.

One of the key problems in the field of increasing the efficiency of neural network tools intended for the analysis of graphic materials is the formation of representative training databases. A promising way to overcome this problem is to increase the size of the training sample by applying the augmentation of training examples due to geometric transformations. However, the modern mathematical apparatus for modifying the geometric parameters of images has shortcomings that can reduce the quality of the obtained images or lead to their insufficient compliance with the tasks. The purpose of the work is the formation of mathematical support, which is used to implement geometric transformations of images during the augmentation of training data of neural network tools. The research methodology is based on the theory of digital processing of signals and images, system analysis and the theory of neural networks and involves the determination of key components of the mathematical support for affine transformations and the definition of the mathematical support for calculating the color of pixels when scaling an image using interpolation methods in various application conditions. As a result of the conducted research, mathematical support was formed, which is used to implement geometric transformations of images when augmenting the training data of neural network tools. The key components of the mathematical support of affine transformations related to the determination of the display dimensions of the modified image and the determination of the color of individual points of the modified image are defined. A mathematical apparatus has been defined that allows you to calculate the dimensions of the display area of the modified image, provided that a rectangular display area is provided. It is shown that in the task of augmentation of training data of neural network tools, it is advisable to perform image scaling using proven non-adaptive interpolation methods: nearest neighbor, bilinear interpolation, bicubic interpolation, Lanzosh interpolation, box filter, triangular filter. A mathematical apparatus for calculating the pixel color of a scaled image using each of the above interpolation methods is defined. The conditions that determine the expediency of using the specified methods in solving practical problems are outlined. It is shown that the method of the nearest neighbor is advisable to use in the case of the need to ensure the simplicity of implementation and high productivity of the augmentation procedure, when the noise of small details and the distortion of the shape of objects containing thin lines can be neglected. The bilinear interpolation method is recommended for use under the conditions of ensuring a balance between the quality of the scaled image and computational costs, and the bicubic interpolation method - under the condition that there are no strict limits on the computational resource intensity of the processing process, when it is necessary to achieve high image quality during scaling. The Lanzosh interpolation method is recommended for scaling images in cases where maximum preservation of quality and detail is required. The box method and the triangular filter method are appropriate to use when the speed of scaling is more important than the quality of the image obtained after scaling. Prospects for further research are related to the development of a methodology for adapting the means of integrated modification of geometry and visual characteristics of images to the conditions of augmentation of training data of neural network systems for video stream analysis.

Interpolation between evenly spaced image pixels is often required in optical metrology and other applications. The interpolation model considered here is motivated by neural network techniques and is applicable when each pixel is characterized by a single value. The model assumes that each pixel value may be approximated by a linear function of its nearest neighbors in accordance with a minimum mean-squared error criterion. This assumption is satisfied by functional forms for approximate pixel value vs interpixel distance (along each Cartesian coordinate) that consist of two possibly complex exponential terms. Linear combinations of these forms are used to synthesize functions that (1) match each pixel value and (2) provide continuous image values at all points between pixels. The resulting interpolation is accomplished at relatively low computational cost, as is demonstrated in computer simulations. This model may be advantageous compared to other interpolation methods because the assumption that each pixel value may be approximated by a linear function of its nearest neighbors is consistent with first-order analyses of the physics of many image forming processes.

This transformation allows the model to capture the relationships between different health indicators and their correlation with CKD risk. At the core of the predictive analysis lies a Random Forest Classifier, a powerful ensemble learning method known for its accuracy and robustness in classification tasks. The model is trained on a comprehensive dataset encompassing various health metrics associated with CKD. By analysing this data, the classifier predicts the likelihood of a user developing CKD based on their input, enabling early detection and timely intervention. In addition to predicting CKD risk, the application utilizes K means clustering to categorize users into distinct stages of CKD, based on their health data patterns. This clustering approach aids in providing a clearer understanding of an individual’s kidney health status, which is essential for appropriate treatment planning and management. Furthermore, to enhance user experience and support proactive healthcare decisions, the application employs the K Nearest Neighbors (KNN) algorithm to offer personalized recommendations for nearby hospitals. This web application is designed to predict chronic kidney disease (CKD) through an intuitive user interface that enables individuals to securely log in and input their health details. The application employs a robust data processing pipeline to ensure the reliability of the predictions. Initially, it utilizes interpolation techniques for data cleaning, addressing any missing values in the user input to enhance dataset integrity. This is crucial, as incomplete data can lead to inaccurate predictions and hinder effective analysis. To facilitate the handling of categorical variables, the application incorporates Label Encoder, which converts these categories into numerical values that machine learning algorithms can process effectively.

Abstract The oil and gas industry is currently undergoing a technology transformation with ‘big data’ playing a huge role in making smart data-driven decisions to optimize operations. New tools and systems generate a large amount of data while performing drilling, completions, or production operations and this has become invaluable in well design, field development, monitoring operations as well as optimizing production and recovery. However, sometimes, the data collected has issues that complicate its ability to be interpreted effectively – most commonly being the lack of adequate data to perform meaningful analysis or the presence of missing or null data points. Significant amounts of data are usually generated during the early stages of field development (seismic, well logs, modeling), during drilling and completions (MWD, LWD tools, wireline tools), as well as production operations (production data, pressure, and rate testing). Supervised and unsupervised machine learning (ML) algorithms such as K-Nearest Neighbor, K-Means, Regression (Logistic, Ridge) as well as Clustering algorithms can be used as predictive tools for modeling and interpreting limited datasets. These can be used to identify and resolve deficiencies in datasets including those with missing values and null datapoints. ML and predictive algorithms can be used to determine complex patterns and interdependencies between various variables and parameters in large and complex datasets, which may not be apparent through common regression or curve fitting methods. Work done on a representative dataset of oilwell cement properties including compressive strength, acoustic and density measurements showed potential for accurate pattern recognition with a reasonable margin of error. Missing or null datapoints were rectified through different strategies including interpolation, regression and imputation using KNN models. Supervised machine learning models were determined to be efficient and adequate for structured data when the variables and parameters are known and identified, while unsupervised models and clustering algorithms were more efficient when the data was unstructured and included a sizeable portion of missing or null values. Certain algorithms are more efficient in predicting or imputing missing data values and most models had a prediction accuracy of 85% or better, with reasonable error margins. Clustering algorithms also correctly grouped the datapoints into six clusters corresponding to each class of cement and their curing temperatures, indicating their effectiveness in predicting patterns in unlabeled datasets. Using such machine learning algorithms on oil and gas datasets can help create effective ML models by identifying and grouping similar data with consistent accuracy to complement industry expertise. This can be utilized as a reliable prediction tool when it comes to working with limited datasets or those with missing values, especially when it comes to downhole data.