Search results for "Pattern recognition"

Deep learning approach for prediction of impact peak appearance at ground reaction force signal of running activity

2020

Protruding impact peak is one of the features of vertical ground reaction force (GRF) that is related to injury risk while running. The present research is dedicated to predicting GRF impact peak appearance by setting a binary classification problem. Kinematic data, namely a number of raw signals in the sagittal plane, collected by the Vicon motion capture system (Oxford Metrics Group, UK) were employed as predictors. Therefore, the input data for the predictive model are presented as a multi-channel time series. Deep learning techniques, namely five convolutional neural network (CNN) models were applied to the binary classification analysis, based on a Multi-Layer Perceptron (MLP) classifi…

Robust spatio-temporal descriptors for real-time SVM-based fall detection

2014

Optimization of Complex SVM Kernels Using a Hybrid Algorithm Based on Wasp Behaviour

2010

The aim of this paper is to present a new method for optimization of SVM multiple kernels The kernel substitution can be used to define many other types of learning machines distinct from SVMs We introduced a new hybrid method which uses in the first level an evolutionary algorithm based on wasp behaviour and on the co-mutation operator LR−Mijn and in the second level a SVM algorithm which computes the quality of chromosomes The most important details of our algorithms are presented The testing and validation proves that multiple kernels obtained using our genetic approach are improving the classification accuracy up to 94.12% for the “leukemia” data set.

Definition and Performance Evaluation of a Robust SVM Based Fall Detection Solution

2012

We propose an automatic approach to detect falls in home environment. A Support Vector Machine based classifier is fed by a set of selected features extracted from human body silhouette tracking. The classifier is followed by filtering operations taking into account the temporal nature of a video. The features are based on height and width of human body bounding box, the user's trajectory with her/his orientation, Projection Histograms and moments of order 0, 1 and 2. We study several combinations of usual transformations of the features (Fourier Transform, Wavelet transform, first and second derivatives), and we show experimentally that it is possible to achieve high performance using a si…

Real-time flaw detection on complex part: Study of SVM and hyperrectangle based method

2002

We present in this paper the study of two classifications methods used in order to control in real-time some industrials parts. We present the practical frame in which is made the operations, natures of the anomaly to be detected as well as the features extractions method. We tested two techniques of classification, with different algorithm complexities and performances. We compare the results obtained on various features spaces. We end by a combinatorial perspective of results of classification.

A Review of Kernel Methods in ECG Signal Classification

2011

Kernel methods have been shown to be effective in the analysis of electrocardiogram (ECG) signals. These techniques provide a consistent and well-founded theoretical framework for developing nonlinear algorithms. Kernel methods exhibit useful properties when applied to challenging design scenarios, such as: (1) when dealing with low number of (potentially high dimensional) training samples; (2) in the presence of heterogenous multimodalities; and (3) with different noise sources in the data. These characteristics are particularly appropriate for biomedical signal processing and analysis, and hence, the widespread of these techniques in biomedical signal processing in general, and in ECG dat…

Applications of Kernel Methods

2009

In this chapter, we give a survey of applications of the kernel methods introduced in the previous chapter. We focus on different application domains that are particularly active in both direct application of well-known kernel methods, and in new algorithmic developments suited to a particular problem. In particular, we consider the following application fields: biomedical engineering (comprising both biological signal processing and bioinformatics), communications, signal, speech and image processing.

Improvement of multimodal images classification based on DSMT using visual saliency model fusion with SVM

2019

Multimodal images carry available information that can be complementary, redundant information, and overcomes the various problems attached to the unimodal classification task, by modeling and combining these information together. Although, this classification gives acceptable classification results, it still does not reach the level of the visual perception model that has a great ability to classify easily observed scene thanks to the powerful mechanism of the human brain.
  In order to improve the classification task in multimodal image area, we propose a methodology based on Dezert-Smarandache formalism (DSmT), allowing fusing the combined spectral and dense SURF features extracted …

HD-RTI: an adaptive multi-light imaging approach for the quality assessment of manufactured surfaces

2021

International audience; Reflectance Transformation Imaging (RTI) is a technique for estimating surface local angular reflectance from a set of stereo-photometric images captured with variable lighting directions. The digitization of this information fully fits into the industry 4.0 approach and makes it possible to characterize the visual properties of a surface. The proposed method, namely HD-RTI, is based on the coupling of RTI and HDR imaging techniques. This coupling is carried out adaptively according to the response at each angle of illumination. The proposed method is applied to five industrial samples which have high local variations of reflectivity because of their heterogeneity of…

Ricci-flow based conformal mapping of the proximal femur to identify exercise loading effects.

2018

AbstractThe causal relationship between habitual loading and adaptive response in bone morphology is commonly explored by analysing the spatial distribution of mechanically relevant features. In this study, 3D distribution of features in the proximal femur of 91 female athletes (5 exercise loading groups representing habitual loading) is contrasted with 20 controls. A femur specific Ricci-flow based conformal mapping procedure was developed for establishing correspondence among the periosteal surfaces. The procedure leverages the invariance of the conformal mapping method to isometric shape differences to align surfaces in the 2D parametric domain, to produce dense correspondences across an…