Search results for "pattern"
showing 10 items of 4203 documents
An Improved Skew Angle Detection and Correction Technique for Historical Scanned Documents Using Morphological Skeleton and Progressive Probabilistic…
2017
International audience; Skew detection is a crucial step for document analysis systems. Indeed, it represents one of the basic challenges, especially in case of historical documents analysis. In this paper, we propose a novel robust skew angle detection and correction technique. Morphological Skeleton is introduced to significantly reduce the amount of data to treat by removing the redundant pixels and keeping only the central curves of the image components. The proposed method then uses Progressive Probabilistic Hough Transform (PPHT) to identify image lines. A special procedure is finally applied in order to estimate the global skew angle of the document image from these detected lines. E…
Wi-Sense: a passive human activity recognition system using Wi-Fi and convolutional neural network and its integration in health information systems
2021
AbstractA human activity recognition (HAR) system acts as the backbone of many human-centric applications, such as active assisted living and in-home monitoring for elderly and physically impaired people. Although existing Wi-Fi-based human activity recognition methods report good results, their performance is affected by the changes in the ambient environment. In this work, we present Wi-Sense—a human activity recognition system that uses a convolutional neural network (CNN) to recognize human activities based on the environment-independent fingerprints extracted from the Wi-Fi channel state information (CSI). First, Wi-Sense captures the CSI by using a standard Wi-Fi network interface car…
Stress Detection from Speech Using Spectral Slope Measurements
2018
Automatic detection of emotional stress is an active research domain, which has recently drawn increasing attention, mainly in the fields of computer science, linguistics, and medicine. In this study, stress is automatically detected by employing speech-derived features. Related studies utilize features such as overall intensity, MFCCs, Teager Energy Operator, and pitch. The present study proposes a novel set of features based on the spectral tilt of the glottal source and of the speech signal itself. The proposed features rely on the Probability Density Function of the estimated spectral slopes, and consist of the three most probable slopes from the glottal source, as well as the correspon…
Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction
2006
Inductive learning systems have been successfully applied in a number of medical domains. It is generally accepted that the highest accuracy results that an inductive learning system can achieve depend on the quality of data and on the appropriate selection of a learning algorithm for the data. In this paper we analyze the effect of class noise on supervised learning in medical domains. We review the related work on learning from noisy data and propose to use feature extraction as a pre-processing step to diminish the effect of class noise on the learning process. Our experiments with 8 medical datasets show that feature extraction indeed helps to deal with class noise. It clearly results i…
Feature selection with Ant Colony Optimization and its applications for pattern recognition in space imagery
2016
This paper presents a feature selection (FS) algorithm using Ant Colony Optimization (ACO). It is inspired by the particular behavior of real ants, namely by the fact that they are capable of finding the shortest path between a food source and the nest. There are considered two ACO-FS model applications for pattern recognition in remote sensing imagery: ACO Band Selection (ACO-BS) and ACO Training Label Purification (ACO-TLP). The ACO-BS reduces dimensionality of an input multispectral image data by selecting the “best” subset of bands to accomplish the classification task. The ACO-TLP selects the most informative training samples from a given set of labeled vectors in order to optimize the…
Mean sets for building 3D probabilistic liver atlas from perfusion MR images
2012
This paper is concerned with liver atlas construction. One of the most important issues in the framework of computational abdominal anatomy is to define an atlas that provides a priori information for common medical task such as registration and segmentation. Unlike other approaches already proposed so far (to our knowledge), in this paper we propose to use the concept of random compact mean set to build probabilistic liver atlases. To accomplish this task a two-tier process was carried out. First a set of 3D images was manually segmented by a physician. We see the different 3D segmented shapes as a realization of a random compact set. Secondly, elements of two known definitions of mean set…
A Comparative Study to Analyze the Performance of Advanced Pattern Recognition Algorithms for Multi-Class Classification
2021
This study aims to implement the following four advanced pattern recognition algorithms, such as “optimal Bayesian classifier,” “anti-Bayesian classifier,” “decision trees (DTs),” and “dependence trees (DepTs)” on both artificial and real datasets for multi-class classification. Then, we calculated the performance of individual algorithms on both real and artificial data for comparison. In Sect. 1, a brief introduction is given about the study. In the second section, the different types of datasets used in this study are discussed. In the third section, we compared the classification accuracies of Bayesian and anti-Bayesian methods for both the artificial and real-life datasets. In the four…
New systems for extracting 3-D shape information from images
1993
Neural architectures may offer an adequate way to deal with early vision since they are able to learn shape features or classify unknown shapes, generalising the features of a few meaningful examples, with a low computational cost after the training phase. Two different neural approaches are proposed by the authors: the first one consists of a cascaded architecture made up by a first stage named BWE (Boundary Webs Extractor) which is aimed to extract a brightness gradient map from the image, followed by a backpropagation network that estimates the geometric parameters of the object parts present in the perceived scene. The second approach is based on the extraction of the boundary webs map …
An Optical Remote Sensor for Fingerprint Identification using Speckle Pattern
2017
The implementation of a simple, inexpensive optical device for remote fingerprint identification is presented. The sensor is based on temporal tracking of back-reflected secondary speckle patterns generated while illuminating a finger with a laser.
A combined analysis to extract objects in remote sensing images
1999
Abstract This paper describes an object recognition system to extract shape information from remote sensing images. One of the goals is to determine if towers and power lines can be seen on one-meter imagery and how much ground conditions can influence the resolution power of the recognition algorithms. To this end, an integrated analysis system has been implemented inside the Remote Sensing Imaging System (RSIS). The methodology consists in the combination of statistical and structural information. It has been tested on real images and it will be integrated in an automatic system for the assessment of post storm damage.