Search results for "pattern recognition"
showing 10 items of 2301 documents
A Multiple Local Models Approach to Accuracy Improvement in Continuous Glucose Monitoring
2011
Continuous glucose monitoring (CGM) devices estimate plasma glucose (PG) from measurements in compartments alternative to blood. The accuracy of currently available CGM is yet unsatisfactory and may depend on the implemented calibration algorithms, which do not compensate adequately for the differences of glucose dynamics between the compartments. Here we propose and validate an innovative calibration algorithm for the improvement of CGM performance.CGM data from GlucoDay(®) (A. Menarini, Florence, Italy) and paired reference PG have been obtained from eight subjects without diabetes during eu-, hypo-, and hyperglycemic hyperinsulinemic clamps. A calibration algorithm based on a dynamic glo…
Analysis of Spatially and Temporally Overlapping Events with Application to Image Sequences
2006
Counting spatially and temporally overlapping events in image sequences and estimating their shape-size and duration features are important issues in some applications. We propose a stochastic model, a particular case of the nonisotropic 3D Boolean model, for performing this analysis: the temporal Boolean model. Some probabilistic properties are derived and a methodology for parameter estimation from time-lapse image sequences is proposed using an explicit treatment of the temporal dimension. We estimate the mean number of germs per unit area and time, the mean grain size and the duration distribution. A wide simulation study in order to assess the proposed estimators showed promising resul…
Processing of rock core microtomography images: Using seven different machine learning algorithms
2016
The abilities of machine learning algorithms to process X-ray microtomographic rock images were determined. The study focused on the use of unsupervised, supervised, and ensemble clustering techniques, to segment X-ray computer microtomography rock images and to estimate the pore spaces and pore size diameters in the rocks. The unsupervised k-means technique gave the fastest processing time and the supervised least squares support vector machine technique gave the slowest processing time. Multiphase assemblages of solid phases (minerals and finely grained minerals) and the pore phase were found on visual inspection of the images. In general, the accuracy in terms of porosity values and pore…
Fast prototyping of a SoC-based smart-camera: a real-time fall detection case study
2014
International audience; Smart camera, i.e. cameras that are able to acquire and process images in real-time, is a typical example of the new embedded computer vision systems. A key example of application is automatic fall detection, which can be useful for helping elderly people in daily life. In this paper, we propose a methodology for development and fast-prototyping of a fall detection system based on such a smart camera, which allows to reduce the development time compared to standard approaches. Founded on a supervised classification approach, we propose a HW/SW implementation to detect falls in a home environment using a single camera and an optimized descriptor adapted to real-time t…
Learning to Navigate in the Gaussian Mixture Surface
2021
In the last years, deep learning models have achieved remarkable generalization capability on computer vision tasks, obtaining excellent results in fine-grained classification problems. Sophisticated approaches based-on discriminative feature learning via patches have been proposed in the literature, boosting the model performances and achieving the state-of-the-art over well-known datasets. Cross-Entropy (CE) loss function is commonly used to enhance the discriminative power of the deep learned features, encouraging the separability between the classes. However, observing the activation map generated by these models in the hidden layer, we realize that many image regions with low discrimin…
Real-time flaw detection on a complex object: comparison of results using classification with a support vector machine, boosting, and hyperrectangle-…
2006
We present a classification work performed on industrial parts using artificial vision, a support vector machine (SVM), boost- ing, and a combination of classifiers. The object to be controlled is a coated heater used in television sets. Our project consists of detect- ing anomalies under manufacturer production, as well as in classi- fying the anomalies among 20 listed categories. Manufacturer speci- fications require a minimum of ten inspections per second without a decrease in the quality of the produced parts. This problem is ad- dressed by using a classification system relying on real-time ma- chine vision. To fulfill both real-time and quality constraints, three classification algorit…
Mutual information-based feature selection for low-cost BCIs based on motor imagery
2016
In the present study a feature selection algorithm based on mutual information (MI) was applied to electro-encephalographic (EEG) data acquired during three different motor imagery tasks from two dataset: Dataset I from BCI Competition IV including full scalp recordings from four subjects, and new data recorded from three subjects using the popular low-cost Emotiv EPOC EEG headset. The aim was to evaluate optimal channels and band-power (BP) features for motor imagery tasks discrimination, in order to assess the feasibility of a portable low-cost motor imagery based Brain-Computer Interface (BCI) system. The minimal sub set of features most relevant to task description and less redundant to…
3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients
2022
Rationale and Objectives: To develop and validate a radiomic model, with radiomic features extracted from breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) from a 1.5T scanner, for predicting the malignancy of masses with enhancement. Images were acquired using an 8-channel breast coil in the axial plane. The rationale behind this study is to show the feasibility of a radio-mics-powered model that could be integrated into the clinical practice by exploiting only standard-of-care DCE-MRI with the goal of reducing the required image pre-processing (ie, normalization and quantitative imaging map generation).Materials and Methods: 107 radiomic features were extracted from a …
A method to reduce the FP/imm number through CC and MLO views comparison in mammographic images
2008
In this paper we propose a method to reduce the FP/imm number through CC and MLO mammographic views comparison of the same patient. The proposed solution uses the symmetry properties of the breast to compute a geometric transformation that permits to represent the two images in comparable coordinates systems. Through this method, potential pathological ROIs of one of the projections are correlated with the ROIs in the second view. To show the effectiveness of the result we apply the method on a dataset composed of 112 couples of pathological images. Experiments shows that method enables a reduction by up to 700/0 of the FP/imm number detected after the classification step
Recognition of polychromatic three-dimensional objects
2004
We propose to use optical multichannel correlation in various chromatic systems to obtain a setup for recognition of polychromatic three-dimensional (3-D) objects based on Fourier-transform profilometry. Because red-green-blue color components are not able to split the luminance information of objects in a defined component, when the 3-D objects are brighter than the reference objects the correlation result gives false alarms. We demonstrate that it is possible to use different color spaces that can split luminance from chromatic information to yield adequate recognition of polychromatic 3-D objects. We show experimental results that prove the utility of the proposed method.