Search results for "Pattern recognition"
showing 10 items of 2301 documents
Synergetic and redundant information flow detected by unnormalized Granger causality: application to resting state fMRI
2015
Objectives: We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. Methods: The presence of redundancy and/or synergy in multivariate time series data renders difficult to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently we introduce a pairwise index of synergy which is zero when two in…
Fast PET Scan Tumor Segmentation Using Superpixels, Principal Component Analysis and K-Means Clustering
2018
Positron Emission Tomography scan images are extensively used in radiotherapy planning, clinical diagnosis, assessment of growth and treatment of a tumor. These all rely on fidelity and speed of detection and delineation algorithm. Despite intensive research, segmentation remained a challenging problem due to the diverse image content, resolution, shape, and noise. This paper presents a fast positron emission tomography tumor segmentation method in which superpixels are extracted first from the input image. Principal component analysis is then applied on the superpixels and also on their average. Distance vector of each superpixel from the average is computed in principal components coordin…
Myocardial Infarction Quantification from Late Gadolinium Enhancement MRI Using Top-Hat Transforms and Neural Networks
2019
Significance: Late gadolinium enhanced magnetic resonance imaging (LGE-MRI) is the gold standard technique for myocardial viability assessment. Although the technique accurately reflects the damaged tissue, there is no clinical standard for quantifying myocardial infarction (MI), demanding most algorithms to be expert dependent. Objectives and Methods: In this work a new automatic method for MI quantification from LGE-MRI is proposed. Our novel segmentation approach is devised for accurately detecting not only hyper-enhanced lesions, but also microvascular-obstructed areas. Moreover, it includes a myocardial disease detection step which extends the algorithm for working under healthy scans.…
Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer
2023
Tumor-stroma ratio (TSR) is a prognostic factor for many types of solid tumors. In this study, we propose a method for automated estimation of TSR from histopathological images of colorectal cancer. The method is based on convolutional neural networks which were trained to classify colorectal cancer tissue in hematoxylin-eosin stained samples into three classes: stroma, tumor and other. The models were trained using a data set that consists of 1343 whole slide images. Three different training setups were applied with a transfer learning approach using domain-specific data i.e. an external colorectal cancer histopathological data set. The three most accurate models were chosen as a classifie…
Can visualization alleviate dichotomous thinking? Effects of visual representations on the cliff effect
2021
Common reporting styles for statistical results in scientific articles, such as $p$ p -values and confidence intervals (CI), have been reported to be prone to dichotomous interpretations, especially with respect to the null hypothesis significance testing framework. For example when the $p$ p -value is small enough or the CIs of the mean effects of a studied drug and a placebo are not overlapping, scientists tend to claim significant differences while often disregarding the magnitudes and absolute differences in the effect sizes. This type of reasoning has been shown to be potentially harmful to science. Techniques relying on the visual estimation of the strength of evidence have been recom…
Automatic landmark detection and 3D Face data extraction
2017
Abstract This paper contributes to 3D facial synthesis by presenting a novel method for parameterization using Landmark Point detection. The approach presented aims at improving facial recognition even in varying facial expressions, and missing data in 3D facial models. As such, the prime objective was to develop an automatically embedded process that can detect any frontal face in 3D face recognition systems, with face segmentation and surface curvature information. Using the hybrid interpolation method, experiments on facial landmarks were performed on 4950 images from Face Recognition Grand Challenge database (FRGC). Distinctive facial landmarks from the nose–tips, Limits mouth and two e…
Probabilistic Corner Detection for Facial Feature Extraction
2009
After more than 35 years of resarch, face processing is considered nowadays as one of the most important application of image analysis. It can be considered as a collection of problems (i.e., face detection, normalization, recognition and so on) each of which can be treated separately. Some face detection and face recognition techniques have reached a certain level of maturity, however facial feature extraction still represents the bottleneck of the entire process. In this paper we present a novel facial feature extraction approach that could be used for normalizing Viola-Jones detected faces and let them be recognized by an appearance-based face recognition method. For each observed featur…
Remote heart rate variability for emotional state monitoring
2018
International audience; Several researches have been conducted to recognize emotions using various modalities such as facial expressions , gestures, speech or physiological signals. Among all these modalities, physiological signals are especially interesting because they are mainly controlled by the autonomic nervous system. It has been shown for example that there is an undeniable relationship between emotional state and Heart Rate Variability (HRV). In this paper, we present a methodology to monitor emotional state from physiological signals acquired remotely. The method is based on a remote photoplethysmography (rPPG) algorithm that estimates remote Heart Rate Variability (rHRV) using a …
Coarse scales are sufficient for efficient categorization of emotional facial expressions: Evidence from neural computation
2010
The human perceptual system performs rapid processing within the early visual system: low spatial frequency information is processed rapidly through magnocellular layers, whereas the parvocellular layers process all the spatial frequencies more slowly. The purpose of the present paper is to test the usefulness of low spatial frequency (LSF) information compared to high spatial frequency (HSF) and broad spatial frequency (BSF) visual stimuli in a classification task of emotional facial expressions (EFE) by artificial neural networks. The connectionist modeling results show that an LSF information provided by the frequency domain is sufficient for a distributed neural network to correctly cla…
Adaptive Distance-Based Pooling in Convolutional Neural Networks for Audio Event Classification
2020
In the last years, deep convolutional neural networks have become a standard for the development of state-of-the-art audio classification systems, taking the lead over traditional approaches based on feature engineering. While they are capable of achieving human performance under certain scenarios, it has been shown that their accuracy is severely degraded when the systems are tested over noisy or weakly segmented events. Although better generalization could be obtained by increasing the size of the training dataset, e.g. by applying data augmentation techniques, this also leads to longer and more complex training procedures. In this article, we propose a new type of pooling layer aimed at …