Furthermore, we propose an adaptive anisotropy factor of which the value decreases as the kernel scale increases. This factor improves the noise robustness of small-scale kernels while alleviating the anisotropy stretch effect that occurs in conventional anisotropic methods. Finally, we evaluate our ...
Moreover, we demonstrate that discretisations of GaussDerNets based on the discrete analogue of the Gaussian kernel in combination with central difference operators perform best or among the best, compared to a set of other discrete approximations of the Gaussian derivative kernels. Furthermore, we ...
This makes sense mathematically since I am applying a stationary kernel, but given (9.1), I thought this quantity was supposed to represent the variance of my derivative, so something appears off here. Essentially these are two examples of codes trying to compute the variance associated to the ...
This is done by a weighted average using a Gaussian kernel. Using the described process, each particle moves with the average velocity of its neighborhood, resembling the collective velocity of a group of people in the crowd. Given the averaged velocity components that move each particle, e.g....
1986. Uniqueness of the Gaussian kernel for scale-space filtering. IEEE Trans. Pattern Anal. and Machine Intell., 8(1):26–33. Google Scholar Bergen J. and Adelson E. 1988. Early vision and texture perception. Nature, 333:363–364. Google Scholar Bigün J., Granlund G.H., and ...
When minimizing the level set functions, regularization can be imposed by applying Gaussian filtering to ensure smoothness in the evolution process. In addition, a corresponding stopping criterion is proposed to ensure the evolving curve automatically stops on true boundaries of objects. Dice similarity ...
The main types of derivatives are futures, forwards 3938 Words 16 Pages Good Essays Preview We Use A Gaussian Function As A Kernel Function In order to specify the middle layer of an RBF we have to decide the number of neurons of the layer and their kernel functions which are usually ...
of a testing sampleMathMLwhereMathMLis its corresponding meta-sample computed from DCA. The functionMathMLis a kernel function mappingMathMLandMathMLinto a same-dimensional or high-dimensional feature space. In this work, we employ the'linear'kernelMathMLfor its simplicity and efficiency (more ...
Motivated by the inequalities derived in [11] from the customary Peano kernel and by the current contribution [9], we prove in this note a new, sharp lower bound for the norm in L2[0,T] of the Caputo fractional derivative. The bound depends on functionals and the Gaussian hypergeometric ...
In the beta function of the scalar mass, the first term is its canonical dimension and the scalar mass becomes relevant at the Gaussian fixed point where all coupling constant vanish gi∗ = 0. When we have 3˜b + 2ξ˜0 > 0 at a non-trivial fixed point, the transverse graviton ...