Ioannis Pratikakis - Profile on Academia.edu (original) (raw)

Papers by Ioannis Pratikakis

A comprehensive survey of LIDAR-based 3D object detection methods with deep learning for autonomous driving

Computers & Graphics, Oct 1, 2021

Abstract LiDAR-based 3D object detection for autonomous driving has recently drawn the attention ... more Abstract LiDAR-based 3D object detection for autonomous driving has recently drawn the attention of both academia and industry since it relies upon a sensor that incorporates appealing features like insensitivity to light and capacity to capture the 3D spatial structure of an object along with the continuous reduction of its purchase cost. Furthermore, the emergence of Deep Learning as the means to boost performance in 3D data analysis stimulated the production of a multitude of solutions for LIDAR-based 3D object detection which followed different approaches in an effort to respond effectively to several challenges. In view of this, this paper presents a comprehensive survey of LIDAR-based 3D object detection methods wherein an analysis of existing methods is addressed by taking into account a new categorisation that relies upon a common operational pipeline which describes the end-to-end functionality of each method. We next, discuss the existing benchmarking frameworks and present the performance achieved by each method in each of them. Finally, a discussion is presented that provides key insights aiming to capture the essence of current trends in LIDAR-based 3D object detection.

Point Contrastive learning for LiDAR-based 3D object detection in autonomous driving

2023 24th International Conference on Digital Signal Processing (DSP)

IEEE Transactions on Information Forensics and Security

Bias and fairness of biometric algorithms have been key topics of research in recent years, mainl... more Bias and fairness of biometric algorithms have been key topics of research in recent years, mainly due to the societal, legal and ethical implications of potentially unfair decisions made by automated decision-making models. A considerable amount of work has been done on this topic across different biometric modalities, aiming at better understanding the main sources of algorithmic bias or devising mitigation measures. In this work, we contribute to these efforts and present the first study investigating bias and fairness of sclera segmentation models. Although sclera segmentation techniques represent a key component of sclera-based biometric systems with a considerable impact on the overall recognition performance, the presence of different types of biases in sclera segmentation methods is still underexplored. To address this limitation, we describe the results of a group evaluation effort (involving seven research groups), organized to explore the performance of recent sclera segmentation models within a common experimental framework and study performance differences (and bias), originating from various demographic as well as environmental factors. Using five diverse datasets, we analyze seven independently developed sclera segmentation models in different experimental configurations. The results of our experiments suggest that there are significant differences in the overall segmentation performance across the seven models and that among the considered factors, ethnicity appears to be the biggest cause of bias. Additionally, we observe that training with representative and balanced data does not necessarily lead to less biased results. Finally, we find that in general there appears to be a negative correlation between Manuscript

Integrating segmentation information into CNN for breast cancer diagnosis of mammographic masses

Computer Methods and Programs in Biomedicine, 2021

Background and ObjectivesSegmentation of mammographic lesions has been proven to be a valuable so... more Background and ObjectivesSegmentation of mammographic lesions has been proven to be a valuable source of information, as it can assist in both extracting shape-related features and providing accurate localization of the lesion. In this work, a methodology is proposed for integrating mammographic mass segmentation information into a convolutional neural network (CNN), aiming to improve the diagnosis of breast cancer in mammograms. MethodsThe proposed methodology involves modification of each convolutional layer of a CNN, so that information of not only the input image but also the corresponding segmentation map is considered. Furthermore, a new loss function is introduced, which adds an extra term to the standard cross-entropy, aiming to steer the attention of the network to the mass region, penalizing strong feature activations based on their location. The segmentation maps are acquired either from the provided ground-truth or from an automatic segmentation stage. ResultsPerformance evaluation in diagnosis is conducted on two mammographic mass datasets, namely DDSM-400 and CBIS-DDSM, with differences in quality of the corresponding ground-truth segmentation maps. The proposed method achieves diagnosis performance of 0.898 and 0.862 in terms AUC when using ground-truth segmentation maps and a maximum of 0.880 and 0.860 when a U-Net-based automatic segmentation stage is employed, for DDSM-400 and CBIS-DDSM, respectively. ConclusionsThe experimental results demonstrate that integrating segmentation information into a CNN leads to improved performance in breast cancer diagnosis of mammographic masses.

IEEE Access

Skeleton-based human action recognition with Graph Convolutional Networks is an active research f... more Skeleton-based human action recognition with Graph Convolutional Networks is an active research field that has gained increased popularity over the last few years. A challenge in skeleton-based action recognition is the design of a model in a way that captures fine-grained motions and the relations between the movements of different parts of the skeleton towards the recognition of specific actions. In this paper, the use of a set of part-aware graphs for the skeleton representation is proposed aiming to enhance discrimination between actions in the recognition task since each action put emphasis on specific parts of the skeleton. Extensive experimental work has been carried out in a consistent evaluation framework taking into account different combinations of part-aware graphs and feature representations leading to a configuration that achieves the optimal balance. Based upon two well-established datasets, namely NTU RGB+D and NTU RGB+D 120, we demonstrate that the proposed methodology compares favourably with the state-of-theart. Code is publicly available at: using-part-aware-graphs-in-a-multi-stream-fusion-context. INDEX TERMS Graph convolutional networks, skeleton-based action recognition, part-aware graphs.

Proceedings of the First International Conference on Computer Vision Theory and Applications, 2006

In this paper, we have built two binary classifiers for indoor/outdoor and city/landscape categor... more In this paper, we have built two binary classifiers for indoor/outdoor and city/landscape categories, respectively. The proposed classifiers consist of robust visual feature extraction that feeds a support vector classification. In the case of indoor/outdoor classification, we combine color and texture information using the first three moments of RGB color space components and the low order statistics of the energy wavelet coefficients from a two-level wavelet pyramid. In the case of city/landscape classification, we combine the first three moments of L*a*b color space components and structural information (line segment orientation). Experimental results show that a high classification accuracy is achieved.

Word Spotting as a Service for Handwritten Documents

In this paper, a segmentation-free and training-free word spotting method is proposed that allows... more In this paper, a segmentation-free and training-free word spotting method is proposed that allows mobile device users to search instances of a query word in handwritten document collections. The method is based on a document-oriented keypoint and feature extraction pipeline together with a fast feature matching method that permits the pipeline to be effectively employed in the cloud and to be introduced as a service in modern mobile device units. Evaluation results on two segmentation-free historical handwritten datasets show the efficiency of the proposed method in terms of matching accuracy along with its fast retrieval time.

Computers & Graphics, Jun 1, 2023

Localization and navigation are the two most important tasks for mobile robots, which require an ... more Localization and navigation are the two most important tasks for mobile robots, which require an upto-date and accurate map. However, to detect map changes from crowdsourced data is a challenging task, especially from billions of points collected by 3D acquisition devices. Collecting 3D data often requires expensive data acquisition equipment and there are limited data sources to evaluate point cloud change detection. To address these issues, in this Shape Retrieval Challenge (SHREC) track, we provide a city-scene dataset with real and synthesized data to detect 3D point cloud change. The dataset consists of 866 pairs of object changes from 78 city-scene 3D point clouds collected by LiDAR and 845 pairs of object changes from 100 city-scene 3D point clouds generated by a high-fidelity simulator. We compare three methods on this benchmark. Evaluation results show that data-driven methods are the current trend in 3D point cloud change detection. Besides, the siamese network architecture is helpful to detect changes in our dataset. We hope this benchmark and comparative evaluation results will further enrich and boost the research of point cloud change detection and its applications.

Bio-Inspired Modeling for the Enhancement of Historical Handwritten Documents

An important step for the document analysis and recognition pipeline is the document image binari... more An important step for the document analysis and recognition pipeline is the document image binarization procedure. In the case of historical handwritten document images, the inherent degradation of the documents requires a preprocessing step aiming to enhance the image and improve the subsequent binarization step. To address this challenge a new document image enhancement method is proposed based on Bio-Inspired Models and especially on the OFF-center ganglion cells of the Human Vision System. Experimental results demonstrate the improvement of standard binarization methods when the proposed enhancement method is used.

A Survey on Map-Based Localization Techniques for Autonomous Vehicles

IEEE transactions on intelligent vehicles, Feb 1, 2023

Improving performance of deep learning models for 3D point cloud semantic segmentation via attention mechanisms

Computers & Graphics, Aug 1, 2022

Handwritting: keyword spotting The Query by Example (QbE) case

Nova Science Publishers, Inc. eBooks, Jul 21, 2017

2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2018

H-DIBCO 2018 is the international Handwritten Document Image Binarization Contest organized in th... more H-DIBCO 2018 is the international Handwritten Document Image Binarization Contest organized in the context of ICFHR 2018 conference. The general objective of the contest is to record recent advances in document image binarization using established evaluation performance measures. This paper describes the contest details including the evaluation measures used as well as the performance of the 8 submitted methods along with a brief description of each method.

RETRIEVAL—An Online Performance Evaluation Tool for Information Retrieval Methods

IEEE Transactions on Multimedia, 2017

Performance evaluation is one of the main research topics in information retrieval. Evaluation me... more Performance evaluation is one of the main research topics in information retrieval. Evaluation metrics are used to quantify various performance aspects of a retrieval method. These metrics assist in identifying the optimum method for a specific retrieval challenge but also to allow its parameters fine-tuning in order to achieve a robust operation for a given set of requirements specification. In this work, we present RETRIEVAL, a Web-based integrated information retrieval performance evaluation platform. It offers a number of metrics that are popular within the scientific community, so as to compose an efficient framework for implementing performance evaluation. We discuss the functionality of RETRIEVAL by citing important aspects such as the data input approaches, the user-level performance metrics parameterization, the evaluation scenarios, the interactive plots, and the performance reports repository that offers both archiving and download functionalities.

Applying conformal geometry for creating a 3D model spatial-consistent texture map

2016 Digital Media Industry & Academic Forum (DMIAF), 2016

The aim of this research is to achieve spatial consistency of the UV map. We present an approach ... more The aim of this research is to achieve spatial consistency of the UV map. We present an approach to produce a fully spatially consistent UV mapping based on the planar parameterisation of the mesh. We apply our method on a 3D digital replica of an ancient Greek Lekythos vessel. We parameterise the mesh of a 3D model onto a unit square 2D plane using computational conformal geometry techniques. The proposed method is genus independent, due to an iterative 3D mesh cutting procedure. Having now the texture of a 3D model depicted on a spatially continuous two dimensional structure enables us to efficiently apply a vast range of image processing based techniques and algorithms.

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 2017

DIBCO 2017 is the international Competition on Document Image Binarization organized in conjuncti... more DIBCO 2017 is the international Competition on Document Image Binarization organized in conjunction with the ICDAR 2017 conference. The general objective of the contest is to identify current advances in document image binarization of machine-printed and handwritten document images using performance evaluation measures that are motivated by document image analysis and recognition requirements. This paper describes the competition details including the evaluation measures used as well as the performance of the 26 submitted methods along with a brief description of each method.

2020 IEEE International Joint Conference on Biometrics (IJCB), 2020

The paper presents a summary of the 2020 Sclera Segmentation Benchmarking Competition (SSBC), the... more The paper presents a summary of the 2020 Sclera Segmentation Benchmarking Competition (SSBC), the 7th in the series of group benchmarking efforts centred around the problem of sclera segmentation. Different from previous editions, the goal of SSBC 2020 was to evaluate the performance of sclera-segmentation models on images captured with mobile devices. The competition was used as a platform to assess the sensitivity of existing models to i) differences in mobile devices used for image capture and ii) changes in the ambient acquisition conditions. 26 research groups registered for SSBC 2020, out of which 13 took part in the final round and submitted a total of 16 segmentation models for scoring. These included a wide variety of deeplearning solutions as well as one approach based on standard image processing techniques. Experiments were conducted with three recent datasets. Most of the segmentation models achieved relatively consistent performance across images captured with different mobile devices (with slight differences across devices), but struggled most with lowquality images captured in challenging ambient conditions, i.e., in an indoor environment and with poor lighting.

Pattern Recognition, 2016

Partial 3D object retrieval has attracted intense research efforts due to its potential for a wid... more Partial 3D object retrieval has attracted intense research efforts due to its potential for a wide range of applications, such as 3D object repair and predictive digitization. This work introduces a partial 3D object retrieval method, applicable on both point clouds and structured 3D models, which is based on a shape matching scheme combining local shape descriptors with their Fisher encodings. Experiments on the SHREC 2013 large-scale benchmark dataset for partial object retrieval, as well as on the publicly available Hampson pottery dataset, demonstrate that the proposed method outperforms seven recently evaluated partial retrieval methods.

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2016

The H-KWS 2016, organized in the context of the ICFHR 2016 conference aims at setting up an evalu... more The H-KWS 2016, organized in the context of the ICFHR 2016 conference aims at setting up an evaluation framework for benchmarking handwritten keyword spotting (KWS) examining both the Query by Example (QbE) and the Query by String (QbS) approaches. Both KWS approaches were hosted into two different tracks, which in turn were split into two distinct challenges, namely, a segmentation-based and a segmentation-free to accommodate different perspectives adopted by researchers in the KWS field. In addition, the competition aims to evaluate the submitted training-based methods under different amounts of training data. Four participants submitted at least one solution to one of the challenges, according to the capabilities and/or restrictions of their systems. The data used in the competition consisted of historical German and English documents with their own characteristics and complexities. This paper presents the details of the competition, including the data, evaluation metrics and results of the best run of each participating methods.