Redouane El Moubtahij

Bio: Redouane El Moubtahij is an academic researcher from Aix-Marseille University. The author has contributed to research in topics: Autofocus & Infrared microscopy. The author has an hindex of 1, co-authored 3 publications receiving 4 citations.

FMPOD: A Novel Focus Metric Based on Polynomial Decomposition for Infrared Microscopy

Abstract: This paper proposes an innovative autofocus method to ensure the image of an integrated circuit is correctly in focus under an infrared microscope. It discusses the difficulties inherent to the optical system and explores several inefficient methods used for natural scenes. It will also present a Focus Metric based on POlynomial Decomposition (FMPOD) adapted to our context. This approach relies on analyzing the projection of images on an orthonormal polynomial basis. Its robustness is tested using different magnifying lenses in addition to multiple distortions. In conclusion, we will demonstrate how this novel approach outperforms existing methods related to our work environment.

Mine Detection Based on Adaboost and Polynomial Image Decomposition.

Abstract: In this paper, we propose a new method for underwater mine detection. This detection strategy is based on the use of the Adaboost algorithm with a Polynomial Image Decomposition (PID). PID splits a given image into two components the geometrical component (cartoon) and the textural one (small scale). This decomposition is based on the use of a polynomial transform. The use of PID reduces the noise and turbidity of underwater images, which results a consequent improvements on the visibility of underwater objects. As a result, our detector achieves a high detection rate and good efficiency. It also shows better performance against the use of a simple adaboost algorithm for underwater mine detection.

Spatial Location in Integrated Circuits through Infrared Microscopy

Abstract: In this paper, we present an infrared microscopy based approach for structures’ location in integrated circuits, to automate their secure characterization. The use of an infrared sensor is the key device for internal integrated circuit inspection. Two main issues are addressed. The first concerns the scan of integrated circuits using a motorized optical system composed of an infrared uncooled camera combined with an optical microscope. An automated system is required to focus the conductive tracks under the silicon layer. It is solved by an autofocus system analyzing the infrared images through a discrete polynomial image transform which allows an accurate features detection to build a focus metric robust against specific image degradation inherent to the acquisition context. The second issue concerns the location of structures to be characterized on the conductive tracks. Dealing with a large amount of redundancy and noise, a graph-matching method is presented—discriminating graph labels are developed to overcome the redundancy, while a flexible assignment optimizer solves the inexact matching arising from noises on graphs. The resulting automated location system brings reproducibility for secure characterization of integrated systems, besides accuracy and time speed increase.

Automatic Focus Control for Assembly Alignment in a Lens Module Process

Abstract: This study proposed an auto focusing method for a multi-focus image in assembling lens modules in digital camera phones. A camera module in a camera phone is composed of a lens barrel, an IR glass, a lens mount, a PCB board and aspheric lenses. Alignment among the components is one of the important factors in product quality. Auto-focus is essential to adjust image quality of an IR glass in a lens holder, but there are two focal points in the captured image due to thickness of IR glass. So, sharpness, probability and a scale factor are defined to find desired focus from a multi-focus image. The sharpness is defined as clarity of an image. Probability and a scale factors are calculated using pattern matching with a registered image. The presented algorithm was applied to a lens assembly machine which has 5 axes, two vacuum chucks and an inspection system. The desired focus can be determined on the local maximum of the sharpness, the probability and the scale factor in the experiment.

Research on performance evaluation and optimization theory for thermal microscope imaging systems

Abstract: Infrared imaging theory is an important theoretical basis for the design of infrared imaging systems, but there is no research on infrared imaging theory for designing thermal microscope imaging systems. Therefore, we studied the performance evaluation and optimization theory of thermal microscope imaging systems. In this paper, we analyzed the difference in spectral radiant flux between thermal microscope imaging and telephoto thermal imaging. The expression of signal-to-noise ratio of the output image of the thermal microscope imaging systems was derived, based on the analysis of the characteristics of thermal microscope imaging. We studied the performance evaluation model of thermal microscope imaging systems based on the minimum resolvable temperature difference and the minimum detectable temperature difference. Simulation and analysis of different detectors (ideal photon detector and ideal thermal detector) were also carried out. Finally, based on the conclusion of theoretical research, we carried out a system design and image acquisition experiment. The results show that the theoretical study of thermal microscope imaging systems in this paper can provide reference for the performance evaluation and optimization of thermal microscope imaging systems.

Transformations polynomiales, applications à l'estimation de mouvements et la classification

Abstract: Ces travaux de recherche concernent la modelisation de l'information dynamique fonctionnelle fournie par les champs de deplacements apparents a l'aide de base de polynomes orthogonaux. Leur objectif est de modeliser le mouvement et la texture extraites afin de l'exploiter dans les domaines de l'analyse et de la reconnaissance automatique d'images et de videos. Nous nous interessons aussi bien aux mouvements humains qu'aux textures dynamiques. Les bases de polynomes orthogonales ont ete etudiees. Cette approche est particulierement interessante car elle offre une decomposition en multi-resolution et aussi en multi-echelle. La premiere contribution de cette these est la definition d'une methode spatiale de decomposition d'image : l'image est projetee et reconstruite partiellement avec un choix approprie du degre d'anisotropie associe a l'equation de decomposition basee sur des transformations polynomiales. Cette approche spatiale est etendue en trois dimensions afin d'extraire la texture dynamique dans des videos. Notre deuxieme contribution consiste a utiliser les sequences d'images qui representent les parties geometriques comme images initiales pour extraire les flots optiques couleurs. Deux descripteurs d'action, spatial et spatio-temporel, fondes sur la combinaison des informations du mouvement/texture sont alors extraits. Il est ainsi possible de definir un systeme permettant de reconnaitre une action complexe (composee d'une suite de champs de deplacement et de textures polynomiales) dans une video.

An object detection method for the work of an unmanned sweeper in a noisy environment on an improved YOLO algorithm

Abstract: Abstract Efficient and accurate object detection is crucial for the widespread use of low-cost unmanned sweepers. This paper focuses on the low-cost sweeper in practical working scenarios and proposes a traffic participant detection method based on an enhanced YOLO-v5 model. To train the model on noise knowledge, three types of noise were added to the data set based on the mathematical model's vibration response. The loss function was optimized to balance detection accuracy and real-time performance while focusing on traffic participant detection using YOLO-v5. CTDS and BFSA modules were proposed based on the attention mechanism to enhance the YOLO-v5 model. Comparative experiments demonstrated the effectiveness of the proposed method, with the enhanced YOLO-v5 model achieving a 4.5% higher mean average precision than the traditional YOLO-v5 network. Moreover, the proposed method can process images at a frame per second (FPS) of 89 while ensuring real-time performance, meeting the object detection requirements of actual sweeper.