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Hace 5 días · The algorithms utilized in the design of the FIR filter are the subject of Section refs3, which also describes the implementation of these algorithms that are specific to the problem at hand.
Hace 2 días · First-order algorithms have long dominated the training of deep neural networks, excelling in tasks like image classification and natural language processing. Now there is a compelling opportunity to explore alternatives that could outperform current state-of-the-art results. From the estimation theory, the Extended Kalman Filter (EKF) arose as a viable alternative and has shown advantages ...
Hace 3 días · In 2004, D.Lowe, University of British Columbia, came up with a new algorithm, Scale Invariant Feature Transform (SIFT) in his paper, Distinctive Image Features from Scale-Invariant Keypoints, which extract keypoints and compute its descriptors.
Hace 2 días · Kalman filters have been widely used as an engineering tool since the 1960s, notably in NASA’s Apollo mission, enabling accurate sensor correction and precise navigation tracking [1, 2].Recently, the use of Kalman filters expanded into the realm of digital twin technologies, focusing on parameter estimation [3,4,5].However, Kalman filter performances strongly depend on hyperparameters tuning ...
Hace 3 días · Canny Edge Detection is a popular edge detection algorithm. It was developed by John F. Canny in. It is a multi-stage algorithm and we will go through each stages. Noise Reduction. Since edge detection is susceptible to noise in the image, first step is to remove the noise in the image with a 5x5 Gaussian filter.
Hace 2 días · Estimation of atmospheric light: Performing multiscale minimum filtering on the dark channel map.Fuse the filtered outcomes for image blending to compute the atmospheric luminance values from them. Transmission map calculation: Applying multi-directional Gabor filtering to the dark channel map.Blend the filtered image to serve as the guided image for guided filter.
Hace 3 días · This paper introduces a novel global optimization algorithm called Particle Filter Optimization (PFO), designed for a class of stochastic problems. PFO leverages the Bayesian inference framework of Particle Filters (PF) by integrating the optimization problem into the PF estimation process. In this context, the objective function replaces the ...
