This paper presents a geometric method for meaningful perspective, such that covers the classical perspective. In addition, it introduces meaningful geometric rules that include artist’s individuality. This method not only opens a way for creating flexible and mentally-oriented artworks but also provides a path for meaningful deformation in visual art.
This paper proposes an improved new exponential curve model based on the classical exponential curve and the modified exponential curve. System parameters of the new exponential curve are estimated by the nonlinear least squares method and the trust region algorithm. Furthermore, numerical examples are provided to validate and verify the accuracy of the new exponential curve model. Finally, the new model is applied to model and predict the tertiary industry in China. The computational results show that the proposed new exponential curve has higher precision than the others.
According to Cobham’s thesis, computational problems can be practically (or, in other words, feasibly) computed on some computational device only if they can be computed in polynomial time. Despite the presence of many objections to this claim, they are all not decisive. Then again, there is one not explored yet critical objection to the claim. Namely, Cobham's thesis is susceptible to paradoxical reasoning emerging as a result of the indeterminacy surrounding limits of application of the vague predicate “is practical” (“is feasible”). What is more, as it is demonstrated in the present paper, any attempt to defuse such reasoning and make Cobham's thesis non paradoxical causes it to become of no purpose at all.
Aims/ Objectives: The study examined the specific characteristics responsible for the recognition of edges in handmade embroidery patterns, designed a computational model for the process, implemented the model and evaluated its performance. This is with the view to detecting the edges of handmade embroidery patterns in the context of computational modeling. Study Design: Computational modeling. Place and Duration of Study: Department of Computer Science and Engineering and Department of Fine and Applied Art, between February 2016 and May 2017. Methodology: Samples of hand embroidery patterns were collected through embroiderer shop in Ìbàdàn, Òsogbo, and Ilé-Ifè and the collected samples were pre-processed and the edges of the patterns were detected using cellular automata (CA) and cellular learning automata (CLA). The performance of the system was evaluated in terms of computing time, Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR).
Results: The result obtained from all the experiments carried out showed that the Cellular Edge Detection (CED) algorithm has lower value in terms of MSE, a higher value in terms of PSNR and lesser computational time as compared to the standard edge detection algorithm. The automatic detection of edges showed that the complex stitches of handmade embroidery patterns are amenable to computational rendering through efficient and effective techniques.
Conclusion: This study will enhance the performance of the edge detection techniques employed in pattern recognition and computer vision applications.
A continuous two-step method using trigonometric function basis is developed and used to produce two discrete methods which are simultaneously applied as numerical integrators by assembling them into a block method with trigonometric basis for solving oscillatory initial value problems(IVP). The stability property of the method is well discussed and the performance of the method is demonstrated on some numerical examples to show accuracy and effciency advantages.