scholarly journals Ghost Detection and Removal Based on Two-Layer Background Model and Histogram Similarity

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4558
Author(s):  
Yiping Xu ◽  
Hongbing Ji ◽  
Wenbo Zhang
Keyword(s):  
State Of The Art ◽  
Background Model ◽  
Scene Change ◽  
Layer Model ◽  
Distance Threshold ◽  
Similarity Threshold ◽  
Important Challenge ◽  
Overall Performance ◽  
Two Layer Model ◽  
Histogram Similarity

Detecting and removing ghosts is an important challenge for moving object detection because ghosts will remain forever once formed, leading to the overall detection performance degradation. To deal with this issue, we first classified the ghosts into two categories according to the way they were formed. Then, the sample-based two-layer background model and histogram similarity of ghost areas were proposed to detect and remove the two types of ghosts, respectively. Furthermore, three important parameters in the two-layer model, i.e., the distance threshold, similarity threshold of local binary similarity pattern (LBSP), and time sub-sampling factor, were automatically determined by the spatial-temporal information of each pixel for adapting to the scene change rapidly. The experimental results on the CDnet 2014 dataset demonstrated that our proposed algorithm not only effectively eliminated ghost areas, but was also superior to the state-of-the-art approaches in terms of the overall performance.

scholarly journals The accuracy of simple soil water models in climate forecasting

1997 ◽  
Vol 1 (2) ◽  
pp. 241-248 ◽  
Author(s):  
E. M. Blyth ◽  
C. C. Daamen
Keyword(s):  
Soil Water ◽  
Sandy Soils ◽  
Clay Soils ◽  
Soil Types ◽  
Layer Model ◽  
Climate Forecasting ◽  
Number Of Layers ◽  
Overall Performance ◽  
Two Layer Model ◽  
Water Models

Abstract. Several simple soil water models with four layers or less, typical of those used in GCMS, are compared to a complex multilayered model. They are tested by applying a repeating wetting/drying cycle at different frequencies, and run to equilibrium. The ability of the simple soil models to reproduce the results of the multilayer model vary according to the frequency of the forcing cycle, the soil type, the number of layers and the depth of the top layer of the model. The best overall performance was from the four layer model. The two layer model with a thin top layer (0.1 m) modelled sandy soils well while the two layer model with a thick top layer (0.5 m) modelled clay soils well. The model with just one layer overestimated evaporation during long drying periods for all soil types.

Coherent structures of nonlinear barotropic-baroclinic interaction in unequal depth two-layer model

2021 ◽  
Vol 408 ◽  
pp. 126347
Author(s):  
Jiaqi Zhang ◽  
Ruigang Zhang ◽  
Liangui Yang ◽  
Quansheng Liu ◽  
Liguo Chen
Keyword(s):  
Coherent Structures ◽  
Layer Model ◽  
Two Layer Model

scholarly journals Ensemble-Based Out-of-Distribution Detection

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 567
Author(s):  
Donghun Yang ◽  
Kien Mai Mai Ngoc ◽  
Iksoo Shin ◽  
Kyong-Ha Lee ◽  
Myunggwon Hwang
Keyword(s):  
Detection Method ◽  
State Of The Art ◽  
Metric Learning ◽  
Feature Space ◽  
Confidence Score ◽  
Distance Metric Learning ◽  
Current State ◽  
Overall Performance ◽  
Deep Learning Model

To design an efficient deep learning model that can be used in the real-world, it is important to detect out-of-distribution (OOD) data well. Various studies have been conducted to solve the OOD problem. The current state-of-the-art approach uses a confidence score based on the Mahalanobis distance in a feature space. Although it outperformed the previous approaches, the results were sensitive to the quality of the trained model and the dataset complexity. Herein, we propose a novel OOD detection method that can train more efficient feature space for OOD detection. The proposed method uses an ensemble of the features trained using the softmax-based classifier and the network based on distance metric learning (DML). Through the complementary interaction of these two networks, the trained feature space has a more clumped distribution and can fit well on the Gaussian distribution by class. Therefore, OOD data can be efficiently detected by setting a threshold in the trained feature space. To evaluate the proposed method, we applied our method to various combinations of image datasets. The results show that the overall performance of the proposed approach is superior to those of other methods, including the state-of-the-art approach, on any combination of datasets.

scholarly journals GASAT: A Genetic Local Search Algorithm for the Satisfiability Problem

2006 ◽  
Vol 14 (2) ◽  
pp. 223-253 ◽  
Author(s):  
Frédéric Lardeux ◽  
Frédéric Saubion ◽  
Jin-Kao Hao
Keyword(s):  
Tabu Search ◽  
Local Search ◽  
Hybrid Algorithm ◽  
State Of The Art ◽  
Search Algorithm ◽  
Satisfiability Problem ◽  
Local Search Algorithm ◽  
Overall Performance ◽  
Genetic Local Search ◽  
Search Stage

This paper presents GASAT, a hybrid algorithm for the satisfiability problem (SAT). The main feature of GASAT is that it includes a recombination stage based on a specific crossover and a tabu search stage. We have conducted experiments to evaluate the different components of GASAT and to compare its overall performance with state-of-the-art SAT algorithms. These experiments show that GASAT provides very competitive results.

Propagation and localization of Rossby waves over random topography (two-layer model)

Wave Motion ◽  
1998 ◽  
Vol 28 (4) ◽  
pp. 333-352 ◽  
Author(s):  
V.I. Klyatskin ◽  
N.V. Gryanik ◽  
D. Gurarie
Keyword(s):  
Rossby Waves ◽  
Layer Model ◽  
Two Layer Model

Permafrost mapping by audiofrequency magnetotellurics

1978 ◽  
Vol 15 (10) ◽  
pp. 1539-1546 ◽  
Author(s):  
A. Koziar ◽  
D. W. Strangway
Keyword(s):  
Mackenzie Delta ◽  
Conductive Layer ◽  
Frequency Range ◽  
Layer Model ◽  
Resistive Layer ◽  
Asymptotic Models ◽  
Two Layer Model ◽  
Electrical Methods ◽  
Permafrost Mapping ◽  
Lateral Variations

The audiofrequency magnetotelluric (AMT) method has been used to study permafrost thickness near Tuktoyaktuk, N.W.T. in the Mackenzie Delta. In the frequency range of 10 Hz–10 kHz the permafrost behaves as a simple resistive layer over a conductive layer. This simple two-layer model can be inverted by asymptotic models to give a unique value for the thickness of the highly resistive frozen layer. In areas of simple layering, these results correlate well with drilling. In areas of sharp lateral variations in resistivity, depths tend to be underestimated. Unlike other electrical methods, AMT is not hampered by the presence of a surface melt layer in the summer if the conductivity–thickness product of this 'active layer' is less than about 0.03 mho (0.03 S).

Sign in / Sign up

Export Citation Format

Share Document