Ms. Nadia El Bekri Profile

Nadia El Bekri

at Fraunhofer IOSB

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Publications (3)

Proceedings Article | 12 May 2016 Paper

A knowledge-based approach for task-oriented mission planning

Nadia El Bekri, Yvonne Fischer, David Marosz

Proceedings Volume 9851, 98510A (2016) https://doi.org/10.1117/12.2223830

KEYWORDS: Sensors, Image analysis, Reconnaissance, Bridges, Avionic systems, Expectation maximization algorithms, Image quality, Unmanned aerial vehicles, Data modeling, Image sensors

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Proceedings Article | 21 October 2015 Paper

RecceMan: an interactive recognition assistance for image-based reconnaissance: synergistic effects of human perception and computational methods for object recognition, identification, and infrastructure analysis

Nadia El Bekri, Susanne Angele, Martin Ruckhäberle, Elisabeth Peinsipp-Byma, Bruno Haelke

Proceedings Volume 9653, 96530O (2015) https://doi.org/10.1117/12.2196300

KEYWORDS: Image analysis, Image processing, Object recognition, Automatic target recognition, Reconnaissance, Databases, Feature extraction, Visualization, Analytical research, Sensors

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This paper introduces an interactive recognition assistance system for imaging reconnaissance. This system supports aerial image analysts on missions during two main tasks: Object recognition and infrastructure analysis. Object recognition concentrates on the classification of one single object. Infrastructure analysis deals with the description of the components of an infrastructure and the recognition of the infrastructure type (e.g. military airfield). Based on satellite or aerial images, aerial image analysts are able to extract single object features and thereby recognize different object types. It is one of the most challenging tasks in the imaging reconnaissance. Currently, there are no high potential ATR (automatic target recognition) applications available, as consequence the human observer cannot be replaced entirely. State-of-the-art ATR applications cannot assume in equal measure human perception and interpretation. Why is this still such a critical issue? First, cluttered and noisy images make it difficult to automatically extract, classify and identify object types. Second, due to the changed warfare and the rise of asymmetric threats it is nearly impossible to create an underlying data set containing all features, objects or infrastructure types. Many other reasons like environmental parameters or aspect angles compound the application of ATR supplementary. Due to the lack of suitable ATR procedures, the human factor is still important and so far irreplaceable. In order to use the potential benefits of the human perception and computational methods in a synergistic way, both are unified in an interactive assistance system. RecceMan® (Reconnaissance Manual) offers two different modes for aerial image analysts on missions: the object recognition mode and the infrastructure analysis mode. The aim of the object recognition mode is to recognize a certain object type based on the object features that originated from the image signatures. The infrastructure analysis mode pursues the goal to analyze the function of the infrastructure. The image analyst extracts visually certain target object signatures, assigns them to corresponding object features and is finally able to recognize the object type. The system offers him the possibility to assign the image signatures to features given by sample images. The underlying data set contains a wide range of objects features and object types for different domains like ships or land vehicles. Each domain has its own feature tree developed by aerial image analyst experts. By selecting the corresponding features, the possible solution set of objects is automatically reduced and matches only the objects that contain the selected features. Moreover, we give an outlook of current research in the field of ground target analysis in which we deal with partly automated methods to extract image signatures and assign them to the corresponding features. This research includes methods for automatically determining the orientation of an object and geometric features like width and length of the object. This step enables to reduce automatically the possible object types offered to the image analyst by the interactive recognition assistance system.

Proceedings Article | 15 May 2015 Paper

Classification of short-lived objects using an interactive adaptable assistance system

Nadia El Bekri, Susanne Angele, Elisabeth Peinsipp-Byma

Proceedings Volume 9499, 949908 (2015) https://doi.org/10.1117/12.2180346

KEYWORDS: Image analysis, Data storage, Classification systems, Image classification, Visualization, Satellite imaging, Earth observing sensors, Satellites, Databases, Aluminum

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“Although we know that it is not a familiar object, after a while we can say what it resembles”. The core task of an aerial image analyst is to recognize different object types based on certain clearly classified characteristics from aerial or satellite images. An interactive recognition assistance system compares selected features with a fixed set of reference objects (core data set). Therefore it is mainly designed to evaluate durable single objects like a specific type of ship or vehicle. Aerial image analysts on missions realized a changed warfare over the time. The task was not anymore to classify and thereby recognize a single durable object. The problem was that they had to classify strong variable objects and the reference set did not match anymore. In order to approach this new scope we introduce a concept to a further development of the interactive assistance system to be able to handle also short-lived, not clearly classifiable and strong variable objects like for example dhows. Dhows are the type of ships that are often used during pirate attacks at the coast of West Africa. Often these ships were build or extended by the pirates themselves. They follow no particular pattern as the standard construction of a merchant ship. In this work we differ between short-lived and durable objects. The interactive adaptable assistance system is supposed to assist image analysts with the classification of objects, which are new and not listed in the reference set of objects yet. The human interaction and perception is an important factor in order to realize this task and achieve the goal of recognition. Therefore we had to model the possibility to classify short-lived objects with appropriate procedures taking into consideration all aspects of short-lived objects. In this paper we will outline suitable measures and the possibilities to categorize short-lived objects via simple basic shapes as well as a temporary data storage concept for shortlived objects. The interactive adaptable approach offers the possibility to insert the data (objects) into the system directly and on-site. To mitigate the manipulation risk the entry of data (objects) into the main reference (core data) set is granted to a central authorized unit.

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