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This paper proposes a kind of transcoding scheme which compresses existing JPEG files without any loss of quality. In this scheme, H.264-like block-adaptive intra prediction is employed to exploit inter-block correlations of quantized DCT coefficients stored in the JPEG file. This prediction is performed in spatial domain of each block composed of 8×8 pels, but the corresponding prediction residuals...
Classical parameterization techniques for Speaker Identification use the codification of the power spectral density of raw speech, not discriminating between articulatory features produced by vocal tract dynamics (acoustic-phonetics) from glottal source biometry. Through the present paper a study is conducted to separate voicing fragments of speech into vocal and glottal components, dominated respectively...
In this paper a novel approach is presented for spectral unmixing in hyperspectral remote sensing images. By assuming knowledge of the number and spectral signatures of the materials present in an image, efficient estimation for their corresponding fractions in the pixels of the image is developed based on a recently proposed maximum a posteriori probability (MAP) method. By exploiting the constraints...
The biometric signature derived from the estimation of the power spectral density singularities of a speaker's glottal source is described in the present work. This consists in the collection of peak-trough profiles found in the spectral density, as related to the biomechanics of the vocal folds. Samples of parameter estimations from a set of 100 normophonic (pathology-free) speakers are produced...
This paper presents a novel framework to learn sparse representations for audiovisual signals. An audiovisual signal is modeled as a sparse sum of audiovisual kernels. The kernels are bimodal functions made of synchronous audio and video components that can be positioned independently and arbitrarily in space and time. We design an algorithm capable of learning sets of such audiovisual, synchronous,...
Vector filtering of signals and images has many applications, but there is little theoretical framework underpinning rather ad-hoc approaches to the development of such filters. In this paper we make a significant step towards improving this position by showing that the geometric operations possible on samples or pixels can be expressed in a canonic form. In the formalism of quaternions, this canonic...
This paper presents an original system for the automatic recognition of catenary elements. Based on a bottom-up approach, our analysis is composed of two stages: the first stage provides recognition hypothesis regarding previously segmented elements, with a precision rate of 91.7 %. The second stage allows to correct some hypothesis using a Markovian model which analyzes the catenary element sequence...
A novel reinforcement learning-based sensor scan optimisation scheme is presented for the purpose of multi-target tracking and threat evaluation from helicopter platforms. Reinforcement learning is an unsupervised learning technique that has been shown to be effective in highly dynamic and noisy environments. The problem is made suitable for the use of reinforcement learning by its casting into a...
One of the major problems in underdetermined Sparse Component Analysis (SCA) is the appropriate estimation of the mixing matrix, A, in the linear model x(t) = As(t), especially where more than one source is active at each instant of time (It is called ‘multiple dominant problem’). Most of the previous algorithms were restricted to single dominant problem in which it is assumed that at each instant,...
Today computers have become more accessible and easy to use for everyone, except the disabled. Though some progress has been made on this issue but still it has been focused on either a certain disability or is too expensive for real world scenarios. Major contributions have been made for people lacking fine motor skills and speech based interfaces, but what if they lack both. In this regard we have...
Due to the recent growing need for increased surveillance, we believe that it is necessary to move from single or stationary cameras to a network of multiple cameras. This is useful for various applications, like surveillance or 3D model building. In this paper, we deal with such a dynamic network of pan-tilt-zoom (PTZ) cameras. We allow the camera to freely vary its internal parameters by rotating...
In this paper, we provide a model for the Bluetooth (BT) interference and propose interference mitigation techniques at the channel estimation stage in IEEE 802.11g Wireless Local Area Network (WLAN) receivers. BT signal has been modelled as a narrow band tonal interference. Estimation of the BT interference by the Estimation of Signal Parameters using Rotational Invariance Techniques (ESPRIT) algorithm...
A new method for motion estimation and segmentation of cardiac magnetic resonance images, based on variational and level set techniques is presented. The variational method for motion estimation is based on a total variation approach following a matrix implementation. It has been integrated with a level set method for segmenting the endocardial wall, providing a valuable tool for motion estimation...
This paper studies coordination and consensus mechanisms for Wireless sensor networks in order to train a Support Vector Machine (SVM) classifier in a distributed fashion. We propose two selective gossip algorithms, which take advantage of the sparse representation that SVMs provide for their decision boundary (hyperplane), in order to ensure convergence to an optimal or close-to-optimal classifier,...
An algorithm for automatic image-map alignment problem using a new similarity measure named Edge-Based Code Mutual Information (EBCMI) and Hilbert scan is presented in this study. Because image and map are very different in their representations, the normal Mutual Information (MI) using the intensity in traditional alignment method may result in misalignment. To solve the problem, codes which are...
Estimating a visual evoked potential (VEP) from the human brain is challenging since its signal-to-noise ratio (SNR) is generally very low. An eigendecomposition-based subspace approach originally proposed for enhancing speech corrupted by colored noise, has been investigated and tested in the single trial extraction of VEPs. This scheme arbitrarily labeled as an eigen-decomposition (ED) method has...
In this paper, a new location tracker for cellular networks in mixed line-of-sight (LOS)/non-line-of-sight (NLOS) environments is presented. NLOS situations result in biased UMTS measurements such as Time of Arrival (TOA) or Angle of Arrival (AOA), hence in erroneous position estimates. We propose to consider NLOS as abrupt changes affecting the UMTS system which can be identified by fault detection...
Numerous applications demand that we manipulate large sets of very high-dimensional signals. A simple yet common example is the problem of finding those signals in a database that are closest to a query. In this paper, we tackle this problem by restricting our attention to a special class of signals that have a sparse approximation over a basis or a redundant dictionary. We take advantage of sparsity...
This paper proposes an original approach to cluster multicomponent data sets with an estimation of the number of clusters. From the construction of a minimal spanning tree with Prim's algorithm and the assumption that the vertices are approximately distributed according to a Poisson distribution, the number of clusters is estimated by thresholding the Prim's trajectory. The corresponding cluster centroids...
This paper describes a system on chip for image processing. It is based on a pipe-line of neighborhood processors named SPoC and is controlled by a general purpose processor. Each SPoC are connected one to the other through a reconfigurable data path to get more adaptability and their structure exploits temporal and spatial parallelism to speed up computations and minimize memory transfers. Two applications,...
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