Contribution of Variogram and Feature Vector of Texture for the Classification of Big Size SAR Images

Tonyé, Emmanuel; Fotsing, Janvier; Zobo, Bernard Essimbi; Tankam, Narcisse Talla; Kanaa, Thomas; Rudant, Jean Paul

doi:10.1109/sitis.2011.67

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…In all earlier studies, the features of the HD EMG signals were extracted independently from each channel with time-or frequency-domain characteristics, whereas there are no reports on the use of spatial-domain features. Nonetheless, this information has been exploited in other applications of pattern recognition, such as in texture classification of satellite images [19], [20] or synthetic-aperture radar (SAR) image processing [21]. In these applications, the experimental variogram is usually calculated on a defined window and fitted to a theoretical model, from which the features for classification are extracted.…”

mentioning

confidence: 99%

Spatial Correlation of High Density EMG Signals Provides Features Robust to Electrode Number and Shift in Pattern Recognition for Myocontrol

Stango

Negro

Farina

2015

IEEE Trans. Neural Syst. Rehabil. Eng.

168

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Research on pattern recognition for myoelectric control has usually focused on a small number of electromyography (EMG) channels because of better clinical acceptability and low computational load with respect to multi-channel EMG. However, recently, high density (HD) EMG technology has substantially improved, also in practical usability, and can thus be applied in myocontrol. HD EMG provides several closely spaced recordings in multiple locations over the skin surface. This study considered the use of HD EMG for controlling upper limb prostheses, based on pattern recognition. In general, robustness and reliability of classical pattern recognition systems are influenced by electrode shift in dons and doff, and by the presence of malfunctioning channels. The aim of this study is to propose a new approach to attenuate these issues. The HD EMG grid of electrodes is an ensemble of sensors that records data spatially correlated. The experimental variogram, which is a measure of the degree of spatial correlation, was used as feature for classification, contrary to previous approaches that are based on temporal or frequency features. The classification based on the variogram was tested on seven able-bodied subjects and one subject with amputation, for the classification of nine and seven classes, respectively. The performance of the proposed approach was comparable with the classic methods based on time-domain and autoregressive features (average classification accuracy over all methods ∼ 95% for nine classes). However, the new spatial features demonstrated lower sensitivity to electrode shift ( ± 1 cm) with respect to the classic features . When even just one channel was noisy, the classification accuracy dropped by ∼ 10% for all methods. However, the new method could be applied without any retraining to a subset of high-quality channels whereas the classic methods require retraining when some channels are omitted. In conclusion, the new spatial feature space proposed in this study improved the robustness to electrode number and shift in myocontrol with respect to previous approaches.

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mentioning

confidence: 99%

Spatial Correlation of High Density EMG Signals Provides Features Robust to Electrode Number and Shift in Pattern Recognition for Myocontrol

Stango

Negro

Farina

2015

IEEE Trans. Neural Syst. Rehabil. Eng.

168

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“…The experimental variogram [20, 21] measures the average dissimilarity between two data as a function of their separation, often presents slope changes, which indicate a change in spatial continuity from certain distances, and the variogram can be modeled as the sum of several elementary models called models nested or nested structures [22]

\begin{matrix} γ (h) = γ_{1} (h) + γ_{2} (h) + \dots + γ_{s} (h) . \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

Data Association Methodology to Improve Spatial Predictions in Alternative Marketing Circuits in Ecuador

Padilla¹,

García²

2018

Computational Intelligence and Neuroscience

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This work proposes a methodology that reduces the error of future estimations in commercialization based on multivariate spatial prediction techniques (cokriging) considering the products with strong associations. It is based on the Apriori algorithm to find association rules in sales of agricultural products of local markets. Results show the improvement in spatial prediction accuracy after using the best association rules.

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“…A quite generic model for variogram considers growing from the origin until a distance for stabilization, around a plateau, so that the random variables Z(x) and Z(x + h) are correlated when the length of the separation vector h is lower than a certain distance, the zone of influence, and beyond | h |= a the variogram keeps constant (the plateau). For instance, a spherical variogram of reach a and plateau C is defined as [17]:γ(h)=|C{32|normalh|a−12(|normalh|a)3} if |h| ≤normalaC otherwise…”

Section: Spatial Predictions Based On Data Fusionmentioning

confidence: 99%

Knowledge Extraction and Improved Data Fusion for Sales Prediction in Local Agricultural Markets

Padilla¹,

García

Molina

2019

Sensors

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In this paper, a monitoring system of agricultural production is modeled as a Data Fusion System (data from local fairs and meteorological data). The proposal considers the particular information of sales in agricultural markets for knowledge extraction about the associations among them. This association knowledge is employed to improve predictions of sales using a spatial prediction technique, as shown with data collected from local markets of the Andean region of Ecuador. The commercial activity in these markets uses Alternative Marketing Circuits (CIALCO). This market platform establishes a direct relationship between producer and consumer prices and promotes direct commercial interaction among family groups. The problem is presented first as a general fusion problem with a network of spatially distributed heterogeneous data sources, and is then applied to the prediction of products sales based on association rules mined in available sales data. First, transactional data is used as the base to extract the best association rules between products sold in different local markets, knowledge that allows the system to gain a significant improvement in prediction accuracy in the spatial region considered.

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Contribution of Variogram and Feature Vector of Texture for the Classification of Big Size SAR Images

Cited by 9 publications

References 12 publications

Spatial Correlation of High Density EMG Signals Provides Features Robust to Electrode Number and Shift in Pattern Recognition for Myocontrol

Spatial Correlation of High Density EMG Signals Provides Features Robust to Electrode Number and Shift in Pattern Recognition for Myocontrol

Data Association Methodology to Improve Spatial Predictions in Alternative Marketing Circuits in Ecuador

Knowledge Extraction and Improved Data Fusion for Sales Prediction in Local Agricultural Markets

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