An Inverse Eigenvalue Problem for Jacobi Matrices

A kind of inverse eigenvalue problem is proposed which is the reconstruction of a Jacobi matrix by given four or five eigenvalues and corresponding eigenvectors. The solvability of the problem is discussed, and some sufficient conditions for existence of the solution of this problem are proposed. Furthermore, a numerical algorithm and two examples are presented

Soft computing techniques in engineering applications

The Soft Computing techniques, which are based on the information processing of biological systems are now massively used in the area of pattern recognition, making prediction & planning, as well as acting on the environment. Ideally speaking, soft computing is not a subject of homogeneous concepts and techniques; rather, it is an amalgamation of distinct methods that confirms to its guiding principle. At present, the main aim of soft computing is to exploit the tolerance for imprecision and uncertainty to achieve tractability, robustness and low solutions cost. The principal constituents of soft computing techniques are probabilistic reasoning, fuzzy logic, neuro-computing, genetic algorithms, belief networks, chaotic systems, as well as learning theory. This book covers contributions from various authors to demonstrate the use of soft computing techniques in various applications of engineering.

Soft Computing Techniques in Engineering Applications

VI, 206 p. 134 illus., 57 illus. in color.online

A Benchmark for the Evaluation of Corner Detectors

Corners are an important kind of image feature and play a crucial role in solving various tasks. Over the past few decades, a great number of corner detectors have been proposed. However, there is no benchmark dataset with labeled ground-truth corners and unified metrics to evaluate their corner detection performance. In this paper, we build three benchmark datasets for corner detection. The first two consist of those binary and gray-value images that have been commonly used in previous corner detection studies. The third one contains a set of urban images, called the Urban-Corner dataset. For each test image in these three datasets, the ground-truth corners are manually labeled as objectively as possible with the assistance of a line segment detector. Then, a set of benchmark evaluation metrics is suggested, including five conventional ones: the precision, the recall, the arithmetic mean of precision and recall (APR), the F score, the localization error (Le), and a new one proposed in this work called the repeatability referenced to ground truth (RGT). Finally, a comprehensive evaluation of current state-of-the-art corner detectors is conducted

Predictor-corrector image interpolation

A novel image interpolation methodology is proposed in this paper, called the predictor-corrector interpolation (PCI). Given a low-resolution (LR) image, our PCI scheme begins with the prediction stage, aiming to interpolate the LR-sized input image to a high-resolution (HR) image which is of the same size as the final interpolated image. In the subsequent correction stage, those salient pixels (e.g., edge pixels) of the predicted image are identified and then necessary corrections are made to them for further improving the image quality. To demonstrate the effectiveness of this PCI methodology, the sparse mixing estimator (SME) interpolation is selected as the predictor, and a modified version of the contrast-guided interpolation (CGI) is developed and exploited as the corrector. Hence, the proposed PCI algorithm is denoted as PCI(SME,HR-CGI), which shows a superior performance over a number of comparable state-of-the-art image interpolation algorithms in both objective and subjective image quality assessment