Ioannis Pitas

Aristotle University of Thessaloniki

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Co-Authors

Anastasios Tefas

Aristotle University of Thessaloniki

257

Nikos Nikolaidis

Aristotle University of Thessaloniki

231

Constantine Kotropoulos

Aristotle University of Thessaloniki

104

Ioannis Mademlis

Harokopio University of Athens

Alexandros Iosifidis

Aarhus University

Vasileios Mygdalis

University of Antwerp

Kleoniki Lyroudia

Aristotle University of Thessaloniki

A.N. Venetsanopoulos

University of Toronto

V. Solachidis

Centre for Research and Technology Hellas

Rui Fan

Tongji University

Cooperative Institutions

Center for Special Minimally Invasive and Robotic Surgery

219

Aristotle University of Thessaloniki

211

Institute of Electrical and Electronics Engineers

153

Signal Processing (United States)

142

Chinese Academy of Sciences

Gorgias Press (United States)

Centre for Research and Technology Hellas

Shanghai Jiao Tong University

Information Technologies Institute

University of Chinese Academy of Sciences

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Vision-based UAV Safe Landing exploiting Lightweight Deep Neural Networks

Charalampos Symeonidis Efstratios Kakaletsis Ioannis Mademlis Nikos Nikolaidis Anastasios Tefas

Recent advances in artificial intelligence, control and sensing technologies have facilitated the development of autonomous Unmanned Aerial Vehicles (UAVs, or drones) able to self-navigate in various settings. Although these technologies have already entered a mature stage, ensuring flight safety in crowded areas or performing an emergency landing in case of malfunctions, while adhering to relevant legislation, is generally treated as an afterthought when designing autonomous UAV platforms for unstructured environments. This paper proposes a UAV safe landing navigation pipeline that relies on lightweight computer vision modules, able to be executed on the limited computational resources on-board a typical UAV. Pre-trained Deep Neural Networks (DNNs) are mainly employed as the underlying building blocks, since deep learning has made a major impact on robotic perception by drastically improving the performance of relevant tasks, such as object detection or tracking, semantic image segmentation, etc. Evaluation of the proposed pipeline on a simulated environment indicates highly favorable results.

Drone

10.1145/3447587.3447590

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Citations (27)

Maximum Likelihood signal adaptive filtering of speckle in Ultrasound B-mode images

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (1992)

M.G. Strintzis X. Magnisalis Constantine Kotropoulos Ioannis Pitas Nicos Maglaveras

New techniques are presented for the processing of Ultrasound (US) B-mode images. A signal-adaptive Maximum Likelihood estimation algorithm is proposed, with local image adaptation based on a moving window. The algorithms are tested on US B-mode images obtained from simulated (phantom) and real liver scans ¹ .

Mode (computer interface)

SIGNAL (programming language)

10.1109/iembs.1992.5762078

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Citations (6)

Pseudoautomatic lip contour detection based on edge direction patterns

Mihaela Gordan Constantine Kotropoulos Ioannis Pitas

Detection and tracking of the lip contour is an important issue in lipreading. While there are solutions for lip tracking once a good contour initialization in the first frame is available, the problem of finding such a good initialization is not yet solved automatically, but done manually. Solutions based on edge detection and tracking have failed when applied to real world mouth images. In this paper, we propose a solution to lip contour detection that minimizes user interaction by requiring a minimal number of points to be marked manually on the mouth image. The proposed approach is based on edge detection using gradient masks and edge following. The method is based on the examination of gradient direction patterns in the lip area, and makes use of the local direction constancy along the lip contours, as opposed to the other regions of the mouth image that are characterized by random edge directions.

Initialization

Tracking (education)

Image gradient

10.1109/ispa.2001.938617

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Citations (6)

Incremental Label Propagation on Facial Images

Efstratios Kakaletsis Anastasios Tefas Nikos Nikolaidis Ioannis Pitas

The increasing computational complexity of label propagation-based facial image annotation when applied on multimedia data whose cardinality increases over the time (e.g., when analyzing video or movie content on-line), can be reduced by using an incremental approach. In this paper, a method for incremental label propagation on facial images is described. The similarity matrix is incrementally constructed by employing the kd-tree nearest neighbor algorithm. Furthermore, the matrix inversion, which is included in the label propagation solution, is calculated with a block-wise inversion formula involving the Woodbury matrix identity. Experiments show significant computational savings when the incremental approach is applied on a dataset of three full length movies. Moreover, the classification accuracy was improved in most cases.

Cardinality (data modeling)

Matrix (chemical analysis)

Similarity (geometry)

10.1109/mlsp.2019.8918825

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Virtual Dental Patient

IGI Global eBooks (2009)

Nikos Nikolaidis Ioannis Marras Georgios Mikrogeorgis Kleoniki Lyroudia Ioannis Pitas

The availability of datasets comprising of digitized images of human body cross sections (as well as images acquired with other modalities such as CT and MRI) along with the recent advances in fields like graphics, 3D visualization, virtual reality, 2D and 3D image processing and analysis (segmentation, registration, filtering, etc.) have given rise to a broad range of educational, diagnostic and treatment planning applications, such as virtual anatomy and digital atlases, virtual endoscopy, intervention planning etc. This chapter describes efforts towards the creation of the Virtual Dental Patient (VDP) i.e. a 3D face and oral cavity model constructed using human anatomical data that is accompanied by detailed teeth models obtained from digitized cross sections of extracted teeth. VDP can be animated and adapted to the characteristics of a specific patient. Numerous dentistry-related applications can be envisioned for the created VDP model. Here we focus on its use in a virtual tooth drilling system whose aim is to aid dentists, dental students and researchers in getting acquainted with the handling of drilling instruments and the skills and challenges associated with cavity preparation procedures in endodontic therapy. Virtual drilling can be performed within the VDP oral cavity, on 3D volumetric and surface models (meshes) of virtual teeth. The drilling procedure is controlled by the Phantom Desktop (Sensable Technologies Inc., Woburn, MA) force feedback haptic device. The application is a very promising educational and research tool that allows the user to practice in a realistic manner virtual tooth drilling for endodontic treatment cavity preparation and other related tasks.

10.4018/978-1-60566-292-3.ch018

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Citations (1)

Frontal Facial Pose Recognition Using a Discriminant Splitting Feature Extraction Procedure

Journal of Computing and Information Technology (2011)

Ioannis Marras Nikos Nikolaidis Ioannis Pitas

Frontal facial pose recognition deals with classifying facial images into two-classes: frontal and non-frontal. Recognition of frontal poses is required as a preprocessing step to face analysis algorithms (e.g. face or facial expression recognition) that can operate only on frontal views. A novel frontal facial pose recognition technique that is based on discriminant image splitting for feature extraction is presented in this paper. Spatially homogeneous and discriminant regions for each facial class are produced. The classical image splitting technique is used in order to determine those regions. Thus, each facial class is characterized by a unique region pattern which consists of homogeneous and discriminant 2-D regions. The mean intensities of these regions are used as features for the classification task. The proposed method has been tested on data from the XM2VTS facial database with very satisfactory results.

Three-dimensional face recognition

Feature (linguistics)

10.2498/cit.1002024

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Citations (3)

Digital image processing techniques for the detection and removal of cracks in digitized paintings

IEEE Transactions on Image Processing (2005)

I. Giakoumis Nikos Nikolaidis Ioannis Pitas

An integrated methodology for the detection and removal of cracks on digitized paintings is presented in this paper. The cracks are detected by thresholding the output of the morphological top-hat transform. Afterward, the thin dark brush strokes which have been misidentified as cracks are removed using either a median radial basis function neural network on hue and saturation data or a semi-automatic procedure based on region growing. Finally, crack filling using order statistics filters or controlled anisotropic diffusion is performed. The methodology has been shown to perform very well on digitized paintings suffering from cracks.

Hue

10.1109/tip.2005.860311

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Citations (138)

Global Gabor features for rotation invariant object classification

Ioan Buciu Ioan Nafornita Ioannis Pitas

The human visual system can rapidly and accurately recognize a large number of various objects in cluttered scenes under widely varying and difficult viewing conditions, such as illuminations changing, occlusion, scaling or rotation. One of the state-of-the-art feature extraction techniques used in image recognition and processing is based on the Gabor wavelet model. This paper deals with the application of the aforementioned model for object classification task with respect to the rotation issue. Three training sample sizes were applied to assess the method's performance. Experiments ran on the COIL - 100 database show the robustness of the Gabor approach when globally applied to extract relevant discriminative features. The method out-performs other state-of-the-art techniques compared in the paper such as, principal component analysis (PCA) or linear discriminant analysis (LDA).

10.1109/iccp.2008.4648352

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Citations (3)

Asymptotically optimal detection for additive watermarking in the DCT and DWT domains

IEEE Transactions on Image Processing (2003)

Alexandros K. Nikolaidis Ioannis Pitas

Most of the watermarking schemes that have been proposed until now employ a correlation detector (matched filter). The current paper proposes a new detector scheme that can be applied in the case of additive watermarking in the DCT (discrete cosine transform) or DWT (discrete wavelet transform) domain. Certain properties of the probability density function of the coefficients in these domains are exploited. Thus, an asymptotically optimal detector is constructed based on well known results of the detection theory. Experimental results prove the superiority of the proposed detector over the correlation detector.

Asymptotically optimal algorithm

10.1109/tip.2003.810586

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Citations (206)

Leader and breakaway detection in racing sports videos

Sotirios Papadopoulos Charalampos Symeonidis Ioannis Pitas

This paper addresses the important problem of leader detection in racing sports videos (e.g., cycling, boating and car racing events), as his/her proper framing is a pivotal issue in racing sports cinematography, where the events have a linear spatial deployment. Over the last few years, as autonomous drone vision and cinematography emerged, new challenges appeared in drone vision. While, until recently, most computer vision methods typically addressed still camera AV footage, drone sports cinematography typically employs moving cameras. In this paper, we solve the problem of leader detection in a group of similarly moving targets in sports videos, e.g. the leader of a sports cyclist group and his/her breakaway during a cycling event. This is very useful in drone sports cinematography, as it is important that the drone camera automatically centers on such a leader. We demonstrate that the novel method described in this paper can effectively solve the problem of leader detection in sports videos.

Cinematography

Drone

Framing (construction)

10.1109/mmsp53017.2021.9733640

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