Aladine Chetouani

Application of chemometric algorithms to MALDI mass spectrometry imaging of pharmaceutical tablets.

During drug product development, the nature and distribution of the active substance have to be controlled to ensure the correct activity and the safety of the final medication. Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), due to its structural and spatial specificities, provides an excellent way to analyze these two critical parameters in the same acquisition. The aim of this work is to demonstrate that MALDI-MSI, coupled with four well known multivariate statistical analysis algorithms (PCA, ICA, MCR-ALS and NMF), is a powerful technique to extract spatial and spectral information about chemical compounds from known or unknown solid drug product formulations. To test this methodology, an in-house manufactured tablet and a commercialized Coversyl(®) tablet were studied. The statistical analysis was decomposed into three steps: preprocessing, estimation of the number of statistical components (manually or using singular value decomposition), and multivariate statistical analysis. The results obtained showed that while principal component analysis (PCA) was efficient in searching for sources of variation in the matrix, it was not the best technique to estimate an unmixing model of a tablet. Independent component analysis (ICA) was able to extract appropriate contributions of chemical information in homogeneous and heterogeneous datasets. Non-negative matrix factorization (NMF) and multivariate curve resolution-alternating least squares (MCR-ALS) were less accurate in obtaining the right contribution in a homogeneous sample but they were better at distinguishing the semi-quantitative information in a heterogeneous MALDI dataset.

Indoor navigation assistance with a Smartphone camera based on vanishing points

Indoor navigation assistance is a highly challenging task that is increasingly needed in various types of applications such as visually impaired guidance, emergency intervention, tourism, etc. Many alternative techniques to GPS have been explored to deal with this challenge like pre-installed sensor networks (Wifi, Ultra Wide Band, Bluetooth, Radio Frequency IDentification etc), inertial sensors or camera. This paper presents an indoor navigation system on Smartphone that was designed taking into consideration low cost, portability and the lightweight of the used algorithm in terms of computation power and storage space. The proposed solution relies on embedded vision. Robust and fast camera orientation (3 dof) is estimated by tracking three orthogonal vanishing points in a video stream acquired with the camera of a free-handled Smartphone. The developed algorithm enables indoor pedestrian localization in two steps: an off-line learning step defines a reference path by selecting key frames along the way using saliency extraction method and computing the camera orientation in these frames. Then, in localization step, an approximate but realistic position of the walker is estimated in real time by comparing the orientation of the camera in the current image and that of reference to assist the pedestrian with navigation guidance. Unlike SLAM, this approach does not require to build 3D mapping of the environment. Online walking direction is given by Smartphone camera which advantageously replaces the compass sensor since it performs very poorly indoors due to electromagnetic noise. Experiments, executed online on Smartphone, that show the feasibility and evaluate the accuracy of the proposed positioning approach for different indoor paths.

Full reference image quality metric for stereo images based on Cyclopean image computation and neural fusion

In this paper, we present a New Stereo Full-Reference Image Quality Metric (SFR-IQM) based on Cyclopean Image (CI) computation and 2D IQM fusion. The Cyclopean images of the reference image and its degraded version are first computed from the left and the right views. 2D measures are then extracted from the obtained CIs and are combined using an Artificial Neural Networks (ANN) in order to derive a single index. The 3D LIVE Image Quality Database has been here used to evaluate our method and its capability to predict the subjective judgments. The obtained results have been compared to some recent methods considered as the state-of-the-art. The experimental results show the relevance of our method.

Improving a vision indoor localization system by a saliency-guided detection

In this paper, we propose to use visual saliency to improve an indoor localization system based on image matching. A learning step permits to determinate the reference trajectory by selecting some key frames along the path. During the localization step, the current image is then compared to the obtained key frames in order to estimate the users position. This comparison is realized by extracting primitive information through a saliency method, which aims to improve our localization system by focusing our attention on the more singular regions to match. Another advantage of the saliency-guided detection is to save computation time. The proposed framework has been developed and tested on a Smartphone. The obtained results show the interest of the use of saliency models by comparing the numbers of features and good matches in video sequence.

Indoor Pedestrian Localization With a Smartphone: A Comparison of Inertial and Vision-Based Methods

Indoor pedestrian navigation systems are increasingly needed in various types of applications. However, such systems are still face many challenges. In addition to being accurate, a pedestrian positioning system must be mobile, cheap, and lightweight. Many approaches have been explored. In this paper, we take the advantage of sensors integrated in a smartphone and their capabilities to develop and compare two low-cost, hands-free, and handheld indoor navigation systems. The first one relies on embedded vision (smartphone camera), while the second option is based on low-cost smartphone inertial sensors (magnetometer, accelerometer, and gyroscope) to provide a relative position of the pedestrian. The two associated algorithms are computationally lightweight, since their implementations take into account the restricted resources of the smartphone. In the experiment conducted, we evaluate and compare the accuracy and repeatability of the two positioning methods for different indoor paths. The results obtained demonstrate that the vision-based localization system outperforms the inertial sensor-based positioning system.

A Reduced Reference Image Quality assessment for Multiply Distorted Images

In this paper, we propose a new Reduced Reference Image Quality Metric for multiply degraded images based on a features extraction step and its combination. The selected features are extracted from the original image and its degraded version. Some of them aim to quantify the level of the considered degradation types, while the others quantify its sharpness. These features are then combined to obtain a single value, which corresponds to the predicted subjective score. Our method has been evaluated and compared in terms of correlation with subjective judgments to some recent methods by using the LIVE Multiply Distorted Image Quality Database.

Full Reference Image Quality Assessment: Limitation

In this work, we propose to study the universality of the Full-Reference Image Quality metrics (FR-IQMs) and show the no-relevance to use this kind of metrics without considering the degradation type contained in the image. Different experimental tests have been done in order to analyze its performance. Eight common FR-IQMs have been used and compared in terms of correlation with the subjective judgments. Obtained results show that the performance of a given FR-IQM differs totally from a degradation type to another. Therefore, we finally conclude by the pertinence of some recent works that propose alternative solutions to solve this limitation and then optimize the image quality estimation process.

How to Optimize the Utilization of Image Quality Metrics in Computer Vision

In this paper, we propose to show the importance to consider the image quality in Computer Vision (CV) applications. We also describe a proposed framework that not only take into account the quality but rather permits to select the more adapted measure for a given CV application. Here, the selection of the image quality metric is based on a degradation identification step using a Linear Discriminant Analysis (LDA) method. The proposed framework has been applied to a Full-Reference approach where the reference image is supposed to be available and for No-Reference approach where only the captured image is accessible. The method has been tested using the TID 2008 database, which is composed of 17 degradation types.

A fusion-based blind image quality metric for blurred stereoscopic images

Blur is certainly one of the most encountered and the most annoying degradation types in image. It is due to several causes such as compression, motion, filtering and so on. In order to estimate the quality of this kind of degraded images, several metrics have been proposed in the literature. In this paper, we focus our attention on stereoscopic images and we propose a fusion-based blind stereoscopic image quality metric for blur degradation. In order to characterize the considered degradation type, some relevant features are first computed. Note that these features are extracted from a cyclopean image (CI) derived from the stereoscopic image. The final index quality is given by combined all features through a Support Vector Machine (SVM) model used as a regression tool. The 3D LIVE and the IEEE image databases have been used to evaluate our method. The achieved performance has been compared to the state-of-the-art.

Using distortion and asymmetry determination for blind stereoscopic image quality assessment strategy

A blind quality assessment strategy for stereoscopic images is proposed.Determination of the distortion type and how this distortion impairs the stereo-pair (symmetrically or asymmetrically).Estimation of perceived 3D image quality based on different binocular combination strategies.The experimental results performed on four widely used databases show a significantly high correlation with human judgment. Predicting the perceived quality of stereoscopic 3D images is a challenging task, especially when the stereo-pair is asymmetrically distorted. Despite the considerable efforts to fix this issue, there is no commonly accepted metric. Most of the attempts consisted in developing full reference quality metrics, while very few efforts have been dedicated to blind/no-reference (NR) quality assessment of stereoscopic images. In this paper, we propose a blind/NR quality assessment strategy for stereoscopic images based on the identification of the distortion type in order to select the most efficient impairment measure in addition to the determination of whether a stereo-pair is symmetrically or asymmetrically distorted to account for the binocular fusion properties. Finally, the last step combines the two key information derived from previous steps and estimates the 3D image quality appropriately using different binocular combination strategies. Experimental results on four publicly available 3D image quality assessment databases showed that the proposed strategy reaches significant prediction consistency and accuracy when compared to state-of-the-art metrics.