Solid waste generation can increase due to people's activities that compared the early pandemic in 2020. The solid management paradigm is no longer focused on landfilling landfills. reduction extend landfill's lifetime, one of activity facilities Material Recovery Facilities (MRF). Surabaya, capital city East Java Province Indonesia, has several MRF. One them Tambak Osowilangon . This study aim...

The Huber Markov Random Field (H-MRF) has been proposed for image resolution enhancement as a preferable alternative to Gaussian Random Markov Fields (G-MRF) for its ability to preserve discontinuities in the image. However, its performance relies on a good choice of a regularisation parameter. While automating this choice has been successfully tackled for G-MRF, the more sophisticated form of ...

Markov random field (MRF) learning is intractable, and the approximation algorithms are computationally expensive. Since only a small subset of MRF is used frequently in computer vision, we characterize this subset with three concepts: (1) Lattice, (2) Homogeneity, and (3) Inertia; and design a non-markov high-bias low-variance model as an alternative to this subclass of MRF. Our goal is robust...

Recent stereo algorithms have achieved impressive results by modelling the disparity image as a Markov Random Field (MRF). An important component of an MRF-based approach is the inference algorithm used to find the most likely setting of each node in the MRF. Algorithms have been proposed which use Graph Cuts or Belief Propagation for inference. These stereo algorithms differ in both the infere...

In order to improve the response performance of a proportion integration differentiation (PID) controller for magnetorheological fluids (MRF) brake and to reduce the braking fluctuation rate, an improved fruit fly optimization algorithm for PID controller parameters tuning of MRF brake is proposed. A data acquisition system for MRF brake is designed and the transfer function of MRF brake is ide...

We propose a new segmentation or clustering model that combines Markov Random Field (MRF) and Normalized Cut (NC) objectives. Both NC and MRF models are widely used in machine learning and computer vision, but they were not combined before due to significant differences in the corresponding optimization, e.g. spectral relaxation and combinatorial max-flow techniques. On the one hand, we show th...

In this work we present Discriminative Random Fields (DRFs), a discriminative framework for the classification of image regions by incorporating neighborhood interactions in the labels as well as the observed data. The discriminative random fields offer several advantages over the conventional Markov Random Field (MRF) framework. First, the DRFs allow to relax the strong assumption of condition...