Search results for "Image"
showing 10 items of 6818 documents
Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection
2008
The multitemporal classification of remote sensing images is a challenging problem, in which the efficient combination of different sources of information (e.g., temporal, contextual, or multisensor) can improve the results. In this paper, we present a general framework based on kernel methods for the integration of heterogeneous sources of information. Using the theoretical principles in this framework, three main contributions are presented. First, a novel family of kernel-based methods for multitemporal classification of remote sensing images is presented. The second contribution is the development of nonlinear kernel classifiers for the well-known difference and ratioing change detectio…
Feature Extraction and Selection for Pain Recognition Using Peripheral Physiological Signals.
2019
In pattern recognition, the selection of appropriate features is paramount to both the performance and the robustness of the system. Over-reliance on machine learning-based feature selection methods can, therefore, be problematic; especially when conducted using small snapshots of data. The results of these methods, if adopted without proper interpretation, can lead to sub-optimal system design or worse, the abandonment of otherwise viable and important features. In this work, a deep exploration of pain-based emotion classification was conducted to better understand differences in the results of the related literature. In total, 155 different time domain and frequency domain features were e…
Nonnegative Tensor Train Decompositions for Multi-domain Feature Extraction and Clustering
2016
Tensor train (TT) is one of the modern tensor decomposition models for low-rank approximation of high-order tensors. For nonnegative multiway array data analysis, we propose a nonnegative TT (NTT) decomposition algorithm for the NTT model and a hybrid model called the NTT-Tucker model. By employing the hierarchical alternating least squares approach, each fiber vector of core tensors is optimized efficiently at each iteration. We compared the performances of the proposed method with a standard nonnegative Tucker decomposition (NTD) algorithm by using benchmark data sets including event-related potential data and facial image data in multi-domain feature extraction and clustering tasks. It i…
The Impact of Human Walking on the Time-Frequency Distribution of In-Home Radio Channels
2018
Passive activity recognition of home occupants has become a very hot topic in the area of radio communications, as it enables the development of cutting-edge healthcare monitoring solutions. Thanks to ubiquitous radio waves, such as WiFi signals, at today's homes, one can process radio waves reflected off a person's body for identifying certain mobility patterns. This new approach ignores the need for any wearable sensors. This paper reports a challenging indoor radio channel measurement campaign at 5.9 GHz, which has been conducted to study the impact of walking persons on the temporal and spectral properties of the channel. In particular, the time-frequency distribution of the channel has…
Computational Intelligence and Citizen Communication in the Smart City
2016
Information and communication are at the core of the intelligent city of tomorrow, and the key components of a smart city cannot prescind from data exchanges and interconnectedness. Citizen communication is an integral part of the smart city’s development plans: freedom of information and involvement in collective decisions, e-democracy and decision-making feedback can be greatly enhanced in an intelligent city, and, among other smart city components, foster a new era of participation and wise decisions. In this contribution we describe the methodologies that can be implemented in order to correctly develop automatic recognition systems for citizen communication, paying special attention to…
Filter Bank: a Directional Approach for Retinal Vessel Segmentation
2017
It is well known that retinal diseases are sometimes identified by tortuosity of the vessels, presence of exudates and hemorrhages while lesions of tissues are associated to diabetic retinopathy, retinopathy of prematurity and more general cerebrovascular problems. One of the main issues in this research field is detecting small curvilinear structures, thus the aim of this contribution is to introduce a non-supervised and automated methodology to detect features such as curvilinear structures in retinal images. The core of the proposed methodology consists in using an approach that resembles the “a trous” wavelet algorithm. With respect to the standard Gabor analysis our methodology is base…
A video-based real-time vehicle counting system using adaptive background method
2008
International audience; This paper presents a video-based solution for real time vehicle detection and counting system, using a surveillance camera mounted on a relatively high place to acquire the traffic video stream.The two main methods applied in this system are: the adaptive background estimation and the Gaussian shadow elimination. The former allows a robust moving detection especially in complex scenes. The latter is based on color space HSV, which is able to deal with different size and intensity shadows. After these two operations, it obtains an image with moving vehicle extracted, and then operation counting is effected by a method called virtual detector.
Optimal Filter Estimation for Lucas-Kanade Optical Flow
2012
Optical flow algorithms offer a way to estimate motion from a sequence of images. The computation of optical flow plays a key-role in several computer vision applications, including motion detection and segmentation, frame interpolation, three-dimensional scene reconstruction, robot navigation and video compression. In the case of gradient based optical flow implementation, the pre-filtering step plays a vital role, not only for accurate computation of optical flow, but also for the improvement of performance. Generally, in optical flow computation, filtering is used at the initial level on original input images and afterwards, the images are resized. In this paper, we propose an image filt…
Application based on dynamic reconfiguration of field-programmable gate arrays: JPEG 2000 arithmetic decoder
2005
This paper describes the implementation of a part of the JPEG 2000 algorithm (MQ decoder and arithmetic decoder) on a field-programmable gate array (FPGA) board by using dynamic reconfiguration. A comparison between static and dynamic reconfiguration is presented, and new analysis criteria (spatiotemporal efficiency, logic cost, and performance time) have been defined. The MQ decoder and arithmetic decoder are attractive for dynamic reconfiguration implementation in applications without parallel processing. This implementation is done on an architecture designed to study the dynamic reconfiguration of FPGAs: the ARDOISE architecture. The obtained implementation, based on four partial config…
Krill herd algorithm-based neural network in structural seismic reliability evaluation
2018
ABSTRACTIn this research work, the relative displacement of the stories has been determined by means of a feedforward Artificial Neural Network (ANN) model, which employs one of the novel methods for the optimization of the artificial neural network weights, namely the krill herd algorithm. For the purpose of this work, the area, elasticity, and load parameters were the input parameters and the relative displacement of the stories was the output parameter. To assess the precision of the feedforward (FF) model optimized using the Krill Herd Optimization (FF-KH) algorithm, comparison of results has been performed relative to the results obtained by the linear regression model, the Genetic Alg…