[PDF] Top 20 Gradient-based Sharpness Function
Has 10000 "Gradient-based Sharpness Function" found on our website. Below are the top 20 most common "Gradient-based Sharpness Function".
Gradient-based Sharpness Function
... In this paper we have given a better insight into the analytical properties of the gradient-based sharpness function in the 2-D setting. It has been shown that the role of the artificial blur ... See full document
6
Anisotropic Diffusion-Based Unsharp Masking for Sharpness Improvement in Digital Images
... Sobolev gradient flows (Calder et ...made based on the unsharp mask concept in the past period due to its rapidness and ...enhanced sharpness for the processed images without delivering any unwanted ... See full document
6
Volume Rendering Transfer Function Design Based on eXtreme Gradient Boosting for Fast Visualization
... Abstract. Volume visualization has been widely used to simulate and observe complex data in the fields of science, engineering, biomedicine, etc. One central topic of volume visualization is the transfer function ... See full document
6
Spectral Gradient Algorithm Based on the Generalized Fiser Burmeister Function for Sparse Solutions of LCPS
... NCP-function. Based on the generalized Fiser-Burmeister func- tion, a sequential smoothing spectral gradient method is proposed to solve the equivalent prob- ... See full document
9
Performance evaluation of artificial neural network-based shaping algorithm for planetary pinpoint guidance
... scalar function which satisfies the properties typically exhibited by a Lyapunov ...potential function gradient ensure convergence and safe ...its gradient by using machine learning algorithm ... See full document
19
FIR Linear Phase Fractional Order Digital Differentiator Design using Convex Optimization
... and gradient images. Average gradient and entropy are calculated for color images by taking mean of the three planes (R, G and ...optimization based design as compared to RBF ...Basis Function ... See full document
10
A new modified back propagation algorithm for forecasting Malaysian housing demand
... The proposed algorithm BPIWAG adaptively modifies the gradient based search direction by intoducing the value of gain parameter in the activation function.. The results show that the pro[r] ... See full document
5
On Approximating the Gradient of the Value Function
... The optimality conditions for macroeconomic problems with limited com- mitment often contain partial derivatives of the optimal value function, cor- responding to the outside option. This paper contributes to the ... See full document
13
Performance evaluation of artificial neural network-based shaping algorithm for planetary pinpoint guidance
... scalar function which satisfies the properties typically exhibited by a Lyapunov ...potential function gradient ensure convergence and safe ...its gradient by using machine learning algorithm ... See full document
19
Land surface model parameter optimisation using in situ flux data: comparison of gradient-based versus random search algorithms (a case study using ORCHIDEE v1.9.5.2)
... local gradient-based (the L-BFGS-B algo- rithm, limited memory Broyden–Fletcher–Goldfarb–Shanno algorithm with bound constraints) and global random search (the genetic algorithm) – by evaluating their ... See full document
16
ENSEMBLE OF CLUSTERING ALGORITHMS FOR ANOMALY INTRUSION DETECTION SYSTEM
... been proposed for coordinated and tuning of multiple power system stabilizers [4-10]. In many searches, the location of PSSs is chosen before selecting tuning methods. The participation factors (PF) method has been ... See full document
6
Comparing policy gradient and value function based reinforcement learning methods in simulated electrical power trade
... Policy gradient methods are found to be a valid option for modelling the strategies of electricity market ...action-value function algorithm in all of the sim- ...policy gradient methods can exploit ... See full document
8
Potential-Function Proofs for Gradient Methods
... An orthogonal extension is to potential-based proofs of Nesterov’s accelerated gradient descent method for smooth and well-conditioned convex functions. The ideas in this section build on the simple ... See full document
32
A New Conjugate Gradient Projection Method for Solving Stochastic Generalized Linear Complementarity Problems
... time. Based on the Fischer-Burmeister function, a new conjugate gradient projection method is given for solving the stochastic generalized linear com- plementarity ...conjugate gradient ... See full document
8
Step Size Optimization of LMS Algorithm Using Aunt Colony Optimization & Its comparison with Particle Swarm optimization Algorithm in System Identification
... a gradient-based method of steepest ...the gradient vector from the available ...the gradient vector which eventually leads to the minimum mean square ...correlation function ... See full document
7
Brain Tumor MRI Using Gradient Profile Sharpness
... Super resolution based gradient profile sharpness is introduced in proposed method for single image. High resolution is achieved from low resolution images with super resolution. The high resolution ... See full document
6
Adaptive particle image velocimetry based on sharpness metrics
... spread function (PSF) for high-resolution confocal microscopy ...holography- based modal wavefront sensing [5], the Hartmann-Shack wavefront sensor [6] is a widely spread ... See full document
8
1. Super resolution reconstruction of image gradient profile sharpness
... technique based on modelling embedding is ...uses gradient feature vector in modelling selection. However, gradient feature is high-frequency feature, it is not appropriate for feature representation ... See full document
7
An Effective Image Fusion Technique based on Multiresolution Singular Value Decomposition
... scheme based on Multiresolution Singular Value Decomposition (MSVD) and gradient based sharpness approach using Discrete Wavelet Transform (DWT) is ...by gradient based ... See full document
13
The gradient of a graph
... The traditional syllabus causes difficulties at the outset by attempting (unsuccessfully) to introduce the general notion of limit before discussing the gradient of a curved graph. The development suggested here ... See full document
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