SciTePress - Affiliation Profile

Research.Publish.Connect.

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Affiliation

Computer Science Department, University of Crete, Greece, Institute of Computer Science, FORTH and Greece

Context (please use complete words only)

Field

Sort by

6 Papers

Detecting Object States vs Detecting Objects: A New Dataset and a Quantitative Experimental Study

Topics: Object Detection and Localization; Deep Learning for Visual Understanding ; Few Shot Learning

Authors: Filippos Gouidis ^{1

;

2} , Theodore Patkos ² , Antonis Argyros ^{1

;

2} and Dimitris Plexousakis ^{1

;

2}

Affiliations: ¹ Computer Science Department, University of Crete, Hellas, Greece , ² Institute of Computer Science, Foundation for Research and Technology, Hellas, Greece

Digital Object Identifier: 10.5220/0010898400003124

Proceedings of VISAPP 2022, ISBN: 978-989-758-555-5

Quick Abstract

The detection of object states in images (State Detection - SD) is a problem of both theoretical and practical importance and it is tightly interwoven with other important computer vision problems, such as action recognition and affordance detection. It is also highly relevant to any entity that needs to reason and act in dynamic domains, such as robotic systems and intelligent agents. Despite its importance, up to now, the research on this problem has been limited. In this paper, we attempt a systematic study of the SD problem. First, we introduce the Object State Detection Dataset (OSDD), a new publicly available dataset consisting of more than 19,000 annotations for 18 object categories and 9 state classes. Second, using a standard deep learning framework used for Object Detection (OD), we conduct a number of appropriately designed experiments, towards an in- depth study of the behavior of the SD problem. This study enables the setup of a baseline on the performance of SD, as well as its relative performance in comparison to OD, in a variety of scenarios. Overall, the experimental outcomes confirm that SD is harder than OD and that tailored SD methods need to be developed for addressing effectively this significant problem.

Exploitation of Noisy Automatic Data Annotation and Its Application to Hand Posture Classification

Topics: Event and Human Activity Recognition; Transfer Learning

Authors: Georgios Lydakis ^{1

;

2} , Iason Oikonomidis ¹ , Dimitrios Kosmopoulos ³ and Antonis A. Argyros ^{1

;

2}

Affiliations: ¹ Computer Science Department, University of Crete, Greece , ² Foundation for Research and Technology - Hellas (FORTH), Greece , ³ University of Patras, Greece

Digital Object Identifier: 10.5220/0010906200003124

Proceedings of VISAPP 2022, ISBN: 978-989-758-555-5

Quick Abstract

The success of deep learning in recent years relies on the availability of large amounts of accurately annotated training data. In this work, we investigate a technique for utilizing automatically annotated data in classification problems. Using a small number of manually annotated samples, and a large set of data that feature automatically created, noisy labels, our approach trains a Convolutional Neural Network (CNN) in an iterative manner. The automatic annotations are combined with the predictions of the network in order to gradually expand the training set. In order to evaluate the performance of the proposed approach, we apply it to the problem of hand posture recognition from RGB images. We compare the results of training a CNN classifier with and without the use of our technique. Our method yields a significant increase in average classification accuracy, and also decreases the deviation in class accuracies, thus indicating the validity and the usefulness of the proposed approach.

Segregational Soft Dynamic Time Warping and Its Application to Action Prediction

Topics: Categorization and Scene Understanding; Event and Human Activity Recognition

Authors: Victoria Manousaki ^{1

;

2} and Antonis Argyros ^{1

;

2}

Affiliations: ¹ Computer Science Department, University of Crete, Heraklion, Greece , ² Institute of Computer Science, Foundation for Research and Technology - Hellas (FORTH), Heraklion, Greece

Digital Object Identifier: 10.5220/0010882300003124

Proceedings of VISAPP 2022, ISBN: 978-989-758-555-5

Quick Abstract

Aligning the execution of complete actions captured in segmented videos has been a problem explored by Dynamic Time Warping (DTW) and Soft Dynamic Time Warping (S-DTW) algorithms. The limitation of these algorithms is that they cannot align unsegmented actions, i.e., actions that appear between other actions. This limitation is mitigated by the use of two existing DTW variants, namely the Open-End DTW (OE-DTW) and the Open-Begin-End DTW (OBE-DTW). OE-DTW is designed for aligning actions of known begin point but unknown end point, while OBE-DTW handles continuous, completely unsegmented actions with unknown begin and end points. In this paper, we combine the merits of S-DTW with those of OE-DTW and OBE-DTW. In that direction, we propose two new DTW variants, the Open-End Soft DTW (OE-S-DTW) and the Open-Begin-End Soft DTW (OBE-S-DTW). The superiority of the proposed algorithms lies in the combination of the soft-minimum operator and the relaxation of the boundary constraints of S-DTW, with the segregational capabilities of OE-DTW and OBE-DTW, resulting in better and differentiable action alignment in the case of continuous, unsegmented videos. We evaluate the proposed algorithms on the task of action prediction on standard datasets such as MHAD, MHAD101-v/-s, MSR Daily Activities and CAD-120. Our experimental results show the superiority of the proposed algorithms to existing video alignment methods.

Novelty Detection for Person Re-identification in an Open World

Topic: Machine Learning Technologies for Vision

Authors: George Galanakis ¹ , Xenophon Zabulis ² and Antonis A. Argyros ¹

Affiliations: ¹ Computer Science Department, University of Crete, Greece, Institute of Computer Science, FORTH and Greece , ² Institute of Computer Science, FORTH and Greece

Digital Object Identifier: 10.5220/0007368304010411

Proceedings of VISAPP 2019, ISBN: 978-989-758-354-4

Quick Abstract

A fundamental assumption in most contemporary person re-identification research, is that all query persons that need to be re-identified belong to a closed gallery of known persons, i.e., they have been observed and a representation of their appearance is available. For several real-world applications, this closed-world assumption does not hold, as image queries may contain people that the re-identification system has never observed before. In this work, we remove this constraining assumption. To do so, we introduce a novelty detection mechanism that decides whether a person in a query image exists in the gallery. The re-identification of persons existing in the gallery is easily achieved based on the persons representation employed by the novelty detection mechanism. The proposed method operates on a hybrid person descriptor that consists of both supervised (learnt) and unsupervised (hand-crafted) components. A series of experiments on public, state of the art datasets and in comparison with state of the art methods shows that the proposed approach is very accurate in identifying persons that have not been observed before and that this has a positive impact on re-identification accuracy.

Evaluating Method Design Options for Action Classification based on Bags of Visual Words

Topic: Event and Human Activity Recognition

Authors: Victoria Manousaki , Konstantinos Papoutsakis and Antonis Argyros

Affiliation: University of Crete and FORTH, Greece

Digital Object Identifier: 10.5220/0006544201850192

Proceedings of VISAPP 2018, ISBN: 978-989-758-290-5

Quick Abstract

The Bags of Visua lWords (BoVWs) framework has been applied successfully to several computer vision tasks. In this work we are particularly interested on its application to the problem of action recognition/classification. The key design decisions for a method that follows the BoVWs framework are (a) the visual features to be employed, (b) the size of the codebook to be used for representing a certain action and (c) the classifier applied to the developed representation to solve the classification task. We perform several experiments to investigate a variety of options regarding all the aforementioned design parameters. We also propose a new feature type and we suggest a method that determines automatically the size of the codebook. The experimental results show that our proposals produce results that are competitive to the outcomes of state of the art methods.

A Robot-based Application for Physical Exercise Training

Topics: Multi-modal Interaction ; Systems to Encourage Healthy Lifestyles; Interacting Robots at Home; Motion Detection; Robots for Eldercare

Authors: Michalis Foukarakis , Ilia Adami , Danae Ioannidi , Asterios Leonidis , Damien Michel , Ammar Qammaz , Konstantinos Papoutsakis , Margherita Antona and Antonis Argyros

Affiliation: Foundation for Research & Technology – Hellas (FORTH), Greece

Digital Object Identifier: 10.5220/0005800300450052

Proceedings of ICT4AWE 2016/ICT4AGEINGWELL, ISBN: 978-989-758-180-9

Quick Abstract

According to studies, performing physical exercise is beneficial for reducing the risk of falling in the elderly and prolonging their stay at home. In addition, regular exercising helps cognitive function and increases posi-tive behaviour for seniors with cognitive impairment and dementia. In this paper, a fitness application integrat-ed into a service robot is presented. Its aim is to motivate the users to perform physical training by providing relevant exercises and useful feedback on their progress. The application utilizes the robot vision system to track and recognize user movements and activities and supports multimodal interaction with the user. The pa-per describes the design challenges, the system architecture, the user interface and the human motion capturing module. Additionally, it discusses some results from user testing in laboratory and home-based trials.