Search results for "Pattern recognition"
showing 10 items of 2301 documents
Détection automatique des repères visuels associés à la dépression
2018
Depression is the most prevalent mood disorder worldwide having a significant impact on well-being and functionality, and important personal, family and societal effects. The early and accurate detection of signs related to depression could have many benefits for both clinicians and affected individuals. The present work aimed at developing and clinically testing a methodology able to detect visual signs of depression and support clinician decisions.Several analysis pipelines were implemented, focusing on motion representation algorithms, including Local Curvelet Binary Patterns-Three Orthogonal Planes (LCBP-TOP), Local Curvelet Binary Patterns- Pairwise Orthogonal Planes (LCBP-POP), Landma…
Real metrology by using depth map information
2004
Usually in an image no real information about the scene’s depth (in terms of absolute distance) is available. In this paper, a method that extracts real depth measures is developed. This approach starts considering a region located in the center of the depth map. This region can be positioned, interactively, in any part of the depth map in order to measure the real distance of every object inside the scene. The histogram local maxima of this region are determined. Among these values the biggest, that represents the gray-level of the most considerable object, is chosen. This gray-level is used in an exponential mapping function that converts, using the input camera settings, the depth map gr…
Combining Haar Wavelet and Karhunen Loeve Transforms for Medical Images Watermarking
2014
This paper presents a novel watermarking method, applied to the medical imaging domain, used to embed the patient’s data into the corresponding image or set of images used for the diagnosis. The main objective behind the proposed technique is to perform the watermarking of the medical images in such a way that the three main attributes of the hidden information (i.e., imperceptibility, robustness, and integration rate) can be jointly ameliorated as much as possible. These attributes determine the effectiveness of the watermark, resistance to external attacks, and increase the integration rate. In order to improve the robustness, a combination of the characteristics of Discrete Wavelet and K…
Fast nosological imaging using canonical correlation analysis of brain data obtained by two-dimensional turbo spectroscopic imaging.
2007
A new fast and accurate tissue typing technique has recently been successfully applied to prostate MR spectroscopic imaging (MRSI) data. This technique is based on canonical correlation analysis (CCA), a statistical method able to simultaneously exploit the spectral and spatial information characterizing the MRSI data. Here, the performance of CCA is further investigated by using brain data obtained by two-dimensional turbo spectroscopic imaging (2DTSI) from patients affected by glioblastoma. The purpose of this study is to investigate the applicability of CCA when typing tissues of heterogeneous tumors. The performance of CCA is also compared with that of ordinary correlation analysis on s…
Multispectral integral imaging acquisition and processing using a monochrome camera and a liquid crystal tunable filter
2012
This paper presents an acquisition system and a procedure to capture 3D scenes in different spectral bands. The acquisition system is formed by a monochrome camera, and a Liquid Crystal Tunable Filter (LCTF) that allows to acquire images at different spectral bands in the [480, 680]nm wavelength interval. The Synthetic Aperture Integral Imaging acquisition technique is used to obtain the elemental images for each wavelength. These elemental images are used to computationally obtain the reconstruction planes of the 3D scene at different depth planes. The 3D profile of the acquired scene is also obtained using a minimization of the variance of the contribution of the elemental images at each …
Hidden Markov random field model and Broyden–Fletcher–Goldfarb–Shanno algorithm for brain image segmentation
2018
International audience; Many routine medical examinations produce images of patients suffering from various pathologies. With the huge number of medical images, the manual analysis and interpretation became a tedious task. Thus, automatic image segmentation became essential for diagnosis assistance. Segmentation consists in dividing the image into homogeneous and significant regions. We focus on hidden Markov random fields referred to as HMRF to model the problem of segmentation. This modelisation leads to a classical function minimisation problem. Broyden-Fletcher-Goldfarb-Shanno algorithm referred to as BFGS is one of the most powerful methods to solve unconstrained optimisation problem. …
Multi-level contrast filtering in image difference metrics
2013
In this paper, we present a new metric to estimate the perceived difference in contrast between an original image and a reproduction. This metric, named weighted-level framework Δ E E (WLF-DEE), implements a multilevel filtering based on the difference of Gaussians model proposed by Tadmor and Tolhurst (2000) and the new Euclidean color difference formula in log-compressed OSA-UCS space proposed by Oleari et al. (2009). Extensive tests and analysis are presented on four different categories belonging to the well-known Tampere Image Database and on two databases developed at our institution, providing different distortions directly related to color and contrast. Comparisons in performance wi…
Gaussian and non-Gaussian stochastic sensitivity analysis of discrete structural systems
2000
Abstract The derivatives of the response of a structural system with respect to the system parameters are termed sensitivities. They play an important role in assessing the effect of uncertainties in the mathematical model of the system and in predicting changes of the response due to changes of the design parameters. In this paper, a time domain approach for evaluating the sensitivity of discrete structural systems to deterministic, as well as to Gaussian or non-Gaussian stochastic input is presented. In particular, in the latter case, the stochastic input has been assumed to be a delta-correlated process and, by using Kronecker algebra extensively, cumulant sensitivities of order higher t…
Hybrid (diffractive-refractive) optical processor for space-variant color pattern recognition
2002
Space-variant optical processing constitutes an interesting approach in information processing techniques when the location of the reference object is of as much importance as its identification. Applications range from machine vision, optical logic, or neural network systems, to cryptography. First results of positional sensitivity were obtained in the past few years by Fresnel transform correlators with coherent light [1,2]. On the other hand, optical Fresnel cor-relators working under broadband point-source illumination allow us to exploit color information of input scenes and present a discrimination ability higher than its monochromatic counterparts. However, the use of the wavelength …
Wavelength-compensated time-sequential multiplexed color joint transform correlator
2010
We report a wavelength-compensated three-channel (RGB) joint transform correlator (JTC) for color pattern recognition using a ferroelectric liquid-crystal spatial light modulator (SLM) operating in binary pure phase modulation. We apply a previously reported time-multiplexing technique useful in creating wavelength-compensated diffraction patterns, based on the synchronization of properly scaled diffraction masks with the input wavelength selection obtained by applying a rotating RGB color-filter wheel to an Ar-Kr laser. The application of this technique to a JTC architecture permits real-time color object detection. In order to achieve a high light efficiency for the correlation process, w…