The process of image segmentation is one of the most important steps in computer vision for image retrieval, visual summary, image based modeling and in many other processes. The goal of segmentation is typically to locate certain objects of interest. In this paper, we have studied and investigated graph based normalized cut segmentation methods and proposed a technique for adding flexibility to the parameters for performance improvement. These methods are examined analytically and tested their performance for the standard images. The results obtained for the important metrics show that these methods perform better than others approaches and are computationally efficient, and useful for precise image segmentation.
We introduce the concept of graph colouring and discuss some classical results in this area. In particular, we consider the problem of finding the minimal graphs, complete or not, whose vertex or edge colouring contains or avoids certain subgraphs. This is generally known as Ramsey theory. We give short proofs of some elementary results in this area, and discuss their relationship to colouring integer sequences.
In this research the solution of coupled modified Korteweg-de Vries equation (mKdV) and the generalized Hirota–Satsuma coupled KdV equation by using El-gendi nodal Galerkin (EGG) approaches are presented. El-gendi nodal Galerkin (EGG) (EGG) approaches consist of two approaches, the first is El-gendi Chebyshev nodal Galerkin (ECG) and the second approach is called El-gendi Legendre nodal Galerkin (ELG). In these new approaches spaces of the solution and the weak form to the system are presented. The resulted systems of ODES are solved by the fourth order Runge-Kutta solver. The convergence and the stability of these new methods are analyzed numerically. Numerical results are presented and compared with the results obtained by pseudo-spectral method.
In this paper, we present a new algorithm for solving the blind deconvolution problem. In our method, we reblur a given degraded CT image with R different, but known PSFs, and get R different degraded CT images. Then we blindly deblur using the R new degraded CT images. Also, we introduce Bilateral Total Variation regularization term. In computer simulations in Matlab, it is the most advantages of our proposed algorithm that it performs more effectively than M. Jiang’s ENR method.
In this paper, a three-level quadratic programming (QP) problem is considered where some or all of its coefficients in the objective function are rough intervals. At the first phase of the solution approach and to avoid the complexity of the problem, two QP problems with interval coefficients will be formulated. One of these problems is a QP where all of its coefficients are upper approximation of rough intervals and the other problem is a QP where all of its coefficients are lower approximations of rough intervals. At the second phase, a membership function is constructed to develop a fuzzy model for obtaining the optimal solution of the three-level quadratic programming problem. Finally, an illustrative numerical example is given to demonstrate the obtained results.
This paper presents a new formalization of information flow detection in concurrent logic programming and applies it to the problem of deadlock detection. This work is based on a recent study of the detection of information flow in Datalog programs. Firstly, we define the concept of information flow in concurrent logic programming. Then, we propose a set of definitions of flow based on observation and transition systems while solving goals. Finally, we formalize a mechanism for deadlock detection in concurrent logic programs.
In recent years, fingerprint recognition has been moving through series of evolutions with the intent to decrease the False Acceptance Rate (FAR) and the False Rejection Rate (FRR) in order to achieve minimum Equal Error Rate (EER) while increasing recognition rate. In practical cases, fingerprint images stored in fingerprint databases may have come from scanners with different specifications under variant environmental conditions which may produce different or multiple impressions and backgrounds. The choice of what single and acceptable threshold value to use in order to characterize fingerprint features in images for recognition is therefore crucial in establishing a minimal EER. In this paper, we investigate and analyze the effect of several threshold values on EER when several families of wavelets based methods for feature extraction are applied on multiple impression datasets (Fingerprint Verification Competition-FVC2004). After conducting several threshold analysis on extracted features from multiple impression dataset, the results show that among the closely related wavelets families studied, the Reversed Bi-Orthogonal type 3:1 wavelet, analyzed with threshold value of 27 significantly topped with EER of 4:2% and a recognition rate of 95%. It however performed quite poorly outside of the threshold value indicating the importance of threshold analysis on datasets used for recognition.
In this work we study the Gardner equation or the combined KdV-mKdV equation. We use the exp(-Φ(ξ))-expansion method for a reliable treatment to establish exact traveling wave solutions then the solitary wave solutions for the aforementioned nonlinear partial differential equation (NPDEs). As a result, the traveling wave solutions are obtained in four arbitrary functions including hyperbolic function solutions, trigonometric function solutions, exponential function solutions, and rational function solutions.