Probability density function estimation with the frequency polygon transform

López‐Rubio, Ezequiel; Pérez, José Muñoz

doi:10.1016/j.ins.2014.12.014

Cited by 7 publications

(2 citation statements)

References 56 publications

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“…For a given dataset, the estimation of underlying PDF for pattern recognition and machine learning has been used for many years by statisticians and engineers. They used density estimators as a tool to draw inferences from physical data in social and computer sciences [ 75 ]. This approach is not only from the desire to accurately characterize stochastic events like surface EMG signals, but also from the fact that distributions are the central models utilized to derive sample processing theories and methods.…”

Section: Automated Emg Analysismentioning

confidence: 99%

A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions

Nazmi

Rahman

Yamamoto

et al. 2016

Sensors

316

177

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In recent years, there has been major interest in the exposure to physical therapy during rehabilitation. Several publications have demonstrated its usefulness in clinical/medical and human machine interface (HMI) applications. An automated system will guide the user to perform the training during rehabilitation independently. Advances in engineering have extended electromyography (EMG) beyond the traditional diagnostic applications to also include applications in diverse areas such as movement analysis. This paper gives an overview of the numerous methods available to recognize motion patterns of EMG signals for both isotonic and isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who would like to select the most appropriate methodology in classifying motion patterns, especially during different types of contractions. For feature extraction, the probability density function (PDF) of EMG signals will be the main interest of this study. Following that, a brief explanation of the different methods for pre-processing, feature extraction and classifying EMG signals will be compared in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.

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Section: Automated Emg Analysismentioning

confidence: 99%

A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions

Nazmi

Rahman

Yamamoto

et al. 2016

Sensors

316

177

View full text Add to dashboard Cite

show abstract

“…However, the computational cost of DHT increases quickly as the number of histograms grows. López-Rubio and Muñoz-Pérez [19] proposed the use of multivariate frequency polygons (MFP) to reduce computational complexity. By contrast, probability estimations based on DHT and MFP also rely on training samples and bin width.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Medical Image Registration Based on an Information-Theory Measure with Histogram Estimation of Continuous Image Representation

Yang

Liu

et al. 2018

Mathematical Problems in Engineering

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This work presents a novel method for multimodal medical registration based on histogram estimation of continuous image representation. The proposed method, regarded as “fast continuous histogram estimation,” employs continuous image representation to estimate the joint histogram of two images to be registered. The Jensen–Arimoto (JA) divergence is a similarity measure to measure the statistical dependence between medical images from different modalities. The estimated joint histogram is exploited to calculate the JA divergence in multimodal medical image registration. In addition, to reduce the grid effect caused by the grid-aligning transformations between two images and improve the implementation speed of the registration method, random samples instead of all pixels are extracted from the images to be registered. The goal of the registration is to optimize the JA divergence, which would be maximal when two misregistered images are perfectly aligned using the downhill simplex method, and thus to get the optimal geometric transformation. Experiments are conducted on an affine registration of 2D and 3D medical images. Results demonstrate the superior performance of the proposed method compared to standard histogram, Parzen window estimations, particle filter, and histogram estimation based on continuous image representation without random sampling.

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Visual saliency based on frequency domain analysis and spatial information

Liu

2016

Multimed Tools Appl

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Probability density function estimation with the frequency polygon transform

Cited by 7 publications

References 56 publications

A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions

A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions

Multimodal Medical Image Registration Based on an Information-Theory Measure with Histogram Estimation of Continuous Image Representation

Visual saliency based on frequency domain analysis and spatial information

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