Christos Stentoumis

National Technical University of Athens

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I. Kalisperakis

Technological Educational Institute of Athens

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Λάζαρος Γραμματικόπουλος

University of West Attica

Paraskevi Nomikou

National and Kapodistrian University of Athens

George C. Karras

University of Thessaly

Sotiris Diplaris

Information Technologies Institute

Eftychios Protopapadakis

National Technical University of Athens

Stefanos Vrochidis

Centre for Research and Technology Hellas

Anastasios Doulamis

National Technical University of Athens

Andreas El Saer

University of West Attica

Varvara Antoniou

National and Kapodistrian University of Athens

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National Technical University of Athens

National and Kapodistrian University of Athens

Centre for Research and Technology Hellas

Information Technologies Institute

Technical University of Crete

Institute of Communication and Computer Systems

University of Crete

Aristotle University of Thessaloniki

Pompeu Fabra University

China Philanthropy Research Institute

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xR4DRAMA: Enhancing situation awareness using immersive (XR) technologies

Spyridon Symeonidis Sotiris Diplaris Nicolaus Heise Theodora Pistola Athina Tsanousa

In this paper we describe the xR4DRAMA system, a solution that makes use of XR capabilities to support professionals who deal with disasters, man-made crises or media productions. The key contribution of this work in progress is the increase of situation awareness, which is achieved by the innovative combination of data collection, multimedia and sensor analysis, linking data, GIS and interactive XR technologies. The proposed platform is designed to facilitate the creation of immersive environments using semantically enriched content and comprises a powerful tool that is applicable to multiple real use case scenarios.

10.1109/icir51845.2021.00010

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TOMATOD: EVALUATION OF OBJECT DETECTION ALGORITHMS ON A NEW REAL-WORLD TOMATO DATASET

The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences (2020)

V. Tsironis Stavroula Bourou Christos Stentoumis

Abstract. The integration of modern technologies in farming poses a challenging task to the research community. In this work, the task of selective cropping and treating is considered, whereas learning algorithms can provide essential assistance on crop growth and disease prediction, species recognition and fruit detection. In this paper, we introduce a highly specialized object detection (OD) and classification dataset of tomato fruits that contains class information for the ripening stage of each tomato fruit apart from its corresponding bounding box. With this dataset we aim to encourage the development of task-specific production ready object detection algorithms, as well as to evaluate and provide a baseline result of common state-of-the-art generic OD algorithms. In detail, a thorough presentation of the most common OD datasets takes place, where we discuss both generic OD and some highly specialized datasets. Our dataset contains more than 250 images and 2400 annotations in total. The dataset contains class information for three ripening stages of a tomato fruit provided by expert agriculturists, while providing views consistent with the targeted real-world use case scenario. Compared to other OD datasets our proposition differs in core areas such as the quality of the annotations, the object size distribution and the public availability. Evaluating the performance in our dataset for six object detection models we draw conclusions about the strength and weaknesses of each one’s performance. Finally, we present a future roadmap of revisions and discuss some future research topics that could improve the performance of OD algorithms in our dataset.

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10.5194/isprs-archives-xliii-b3-2020-1077-2020

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A hybrid algorithm for dense stereo correspondences in challenging indoor scenes

Stavros Georgousis Christos Stentoumis Nikolaos Doulamis Athanasios Voulodimos

The algorithms for dense correspondences in stereo images are an extensively researched topic, since it is an essential step in a large number of applications. Despite the fact that the first stereo matching algorithms were proposed some decades ago, novel approaches regarding typical, but also cutting-edge applications, are always in demand. Stereo matching is an inverse, ill-posed problem, which usually depends on the application and the scenario. In this contribution, a hybrid approach for stereo matching is proposed, which is based on graph-cuts optimization (global) and cross-based aggregation (local) under a hierarchical scheme. It is shown that the combined effect of a global method in a coarse layer and a local method in finer layers improves the matching results. This hybrid approach exploits the strengths and ameliorates the weaknesses of the individual global and local algorithms. The resulted disparity map is robust without outliers even in untextured areas and at the same time high fidelity details are accurately represented. This hybrid scheme is evaluated on challenging indoor datasets. It is also computationally efficient for applying it on low-processing power applications.

10.1109/ist.2016.7738270

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Citations (5)