Search results for "Pattern Recognition"
showing 10 items of 2301 documents
Area-Based Depth Estimation for Monochromatic Feature-Sparse Orthographic Capture
2018
With the rapid development of light field technology, depth estimation has been highlighted as one of the critical problems in the field, and a number of approaches have been proposed to extract the depth of the scene. However, depth estimation by stereo matching becomes difficult and unreliable when the captured images lack both color and feature information. In this paper, we propose a scheme that extracts robust depth from monochromatic, feature-sparse scenes recorded in orthographic sub-aperture images. Unlike approaches which rely on the rich color and texture information across the sub-aperture views, our approach is based on depth from focus techniques. First, we superimpose shifted …
POLARIZATION-BASED CAR DETECTION
2018
International audience; Road scene understanding is a vital task for driving assistance systems. Robust vehicle detection is a precondition for diverse applications particularly for obstacle avoidance and secure navigation. Color images provide limited information about the physical properties of the object. This results in unstable vehicle detection caused mainly from road scene complexity (strong reflexions, noises and radiometric distortions). Instead, polarimetric images, characteristic of the light wave, can robustly describe important physical properties of the object (e.g., the surface geometric structure, material and roughness etc). This modality gives rich physical informations wh…
Estimation of brain connectivity through Artificial Neural Networks
2019
Among different methods available for estimating brain connectivity from electroencephalographic signals (EEG), those based on MVAR models have proved to be flexible and accurate. They rely on the solution of linear equations that can be pursued through artificial neural networks (ANNs) used as MVAR model. However, when few data samples are available, there is a lack of accuracy in estimating MVAR parameters due to the collinearity between regressors. Moreover, the assessment procedure is also affected by the lack of data points. The mathematical solution to these problems is represented by penalized regression methods based on l 1 norm, that can reduce collinearity by means of variable sel…
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…
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…
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…
Multiscale Granger causality analysis by à trous wavelet transform
2017
Since interactions in neural systems occur across multiple temporal scales, it is likely that information flow will exhibit a multiscale structure, thus requiring a multiscale generalization of classical temporal precedence causality analysis like Granger's approach. However, the computation of multiscale measures of information dynamics is complicated by theoretical and practical issues such as filtering and undersampling: to overcome these problems, we propose a wavelet-based approach for multiscale Granger causality (GC) analysis, which is characterized by the following properties: (i) only the candidate driver variable is wavelet transformed (ii) the decomposition is performed using the…