A Study on the Effect of Regularization Matrices in Motion Estimation

Coelho, Alessandra Martins; Estrela, Vania V.

doi:10.5120/8151-1886

Cited by 7 publications

(7 citation statements)

References 14 publications

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“…The algorithms were applied to three image sequences: one synthetically produced, with known movement; the "Mother and Daughter" (MD) and the "Foreman" (FM). For each video sequence, two types of experiments were done: one for the noiseless case and the other for a sequence whose frames are corrupted by a Signal-to-Noise-Ratio SNR = 10log 10 [σ 2 / σ c 2 ], with σ 2 is the variance of the original image and σ c 2 is the variance of the noisecorrupted image 9,10 . DFD and IMC( dB ) for the estimated OF obtained with the OLS, GTLS, and GSTLS methods in the absence of noise.…”

Section: Experiments and Discussionmentioning

confidence: 99%

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Optical Flow Estimation Using Total Least Squares Variants

Jesús

Estrela

2017

Orient. J. Chem

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The problem of recursively approximating motion resulting from the Optical Flow (OF) in video thru Total Least Squares (TLS) techniques is addressed. TLS method solves an inconsistent system Gu=z , with G and z in error due to temporal/spatial derivatives, and nonlinearity, while the Ordinary Least Squares (OLS) model has noise only in z. Sources of difficulty involve the non-stationarity of the field, the ill-posedness, and the existence of noise in the data. Three ways of applying the TLS with different noise conjectures to the end problem are observed. First, the classical TLS (cTLS) is introduced, where the entries of the error matrices of each row of the augmented matrix [G;z] have zero mean and the same standard deviation. Next, the Generalized Total Least Squares (GTLS) is defined to provide a more stable solution, but it still has some problems. The Generalized Scaled TLS (GSTLS) has G and z tainted by different sources of additive zero-mean Gaussian noise and scaling [G;z] by nonsingular D and E, that is, D[G;z] E makes the errors iid with zero mean and a diagonal covariance matrix. The scaling is computed from some knowledge on the error distribution to improve the GTLS estimate. For moderate levels of additive noise, GSTLS outperforms the OLS, and the GTLS approaches. Although any TLS variant requires more computations than the OLS, it is still applicable with proper scaling of the data matrix.

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Section: Experiments and Discussionmentioning

confidence: 99%

“…The non-generic TLS is more ill-conditioned than the full rank OLS, which implies that its solution is more sensitive to outliers. According to 8,9,10 , the difference between the squares of σn and σ n+1 is a reasonable measure of how close Gu ≈ z is to the class of non-generic TLS problems. If the ratio…”

Section: Generalized Total Least Squares (Gtls)mentioning

confidence: 99%

Optical Flow Estimation Using Total Least Squares Variants

Jesús

Estrela

2017

Orient. J. Chem

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“…Neural network models, MLP-NN and RBF-NN, have been remarkable developments in the number and variations of the models established and the models' theoretical understanding in the past few years. The authors in 23 examined the Artificial Neural network in predicting minimal temperatures. They have applied MLP-NN architecture to model the predicting system and back-propagation algorithm to train the networks.…”

Section: Introductionmentioning

confidence: 99%

Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia

Hanoon

Ahmed

Zaini

et al. 2021

Sci Rep

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Accurately predicting meteorological parameters such as air temperature and humidity plays a crucial role in air quality management. This study proposes different machine learning algorithms: Gradient Boosting Tree (G.B.T.), Random forest (R.F.), Linear regression (LR) and different artificial neural network (ANN) architectures (multi-layered perceptron, radial basis function) for prediction of such as air temperature (T) and relative humidity (Rh). Daily data over 24 years for Kula Terengganu station were obtained from the Malaysia Meteorological Department. Results showed that MLP-NN performs well among the others in predicting daily T and Rh with R of 0.7132 and 0.633, respectively. However, in monthly prediction T also MLP-NN model provided closer standards deviation to actual value and can be used to predict monthly T with R 0.8462. Whereas in prediction monthly Rh, the RBF-NN model's efficiency was higher than other models with R of 0.7113. To validate the performance of the trained both artificial neural network (ANN) architectures MLP-NN and RBF-NN, both were applied to an unseen data set from observation data in the region. The results indicated that on either architecture of ANN, there is good potential to predict daily and monthly T and Rh values with an acceptable range of accuracy.

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“…Previous studies on time series data forecasting show that the errors of forecasting are still significant and the forecasting is still inaccurate to predict rainfalls and weather. One of the reasons is because the weather data have a non-linear structure ( Haviluddin & Alfred, 2014 ; Shrivastava et al, 2012 ). However, in another study, the statistical methods of rainfall forecasting have been able to produce accurate forecasts ( Farajzadeh, Fard & Lotfi, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

A new model for learning-based forecasting procedure by combining k-means clustering and time series forecasting algorithms

Hartomo

Nataliani

2021

PeerJ Computer Science

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This paper aims to propose a new model for time series forecasting that combines forecasting with clustering algorithm. It introduces a new scheme to improve the forecasting results by grouping the time series data using k-means clustering algorithm. It utilizes the clustering result to get the forecasting data. There are usually some user-defined parameters affecting the forecasting results, therefore, a learning-based procedure is proposed to estimate the parameters that will be used for forecasting. This parameter value is computed in the algorithm simultaneously. The result of the experiment compared to other forecasting algorithms demonstrates good results for the proposed model. It has the smallest mean squared error of 13,007.91 and the average improvement rate of 19.83%.

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A Study on the Effect of Regularization Matrices in Motion Estimation

Cited by 7 publications

References 14 publications

Optical Flow Estimation Using Total Least Squares Variants

Optical Flow Estimation Using Total Least Squares Variants

Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia

A new model for learning-based forecasting procedure by combining k-means clustering and time series forecasting algorithms

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