@conference {167,
	title = {Procrustes-DTW: Dynamic Time Warping Variant for the Recognition of Sign Language Utterances},
	booktitle = {2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)},
	year = {2023},
	abstract = {[color=$\#$333333; font-family: {\textquoteright}HelveticaNeue Regular{\textquoteright}, sans-serif; font-size: 18px; font-variant-ligatures: normal; orphans: 2; widows: 2; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial]In this paper we present a method for classifying sign language videos using a variation of dynamic time warping with the Procrustes distance, which is a shape similarity measure appropriate for handshape recognition. We initially extract features from the dominant hand. We then classify the signs using a nearest-neighbor scheme which includes a dynamic time warping variant, which we dub Procrustes-DTW and which is suitable for extracting a similarity measure of sequences of handshapes only. To reduce computational costs it is combined with a curvature-based summarization scheme with a reasonable sacrifice in accuracy. We verify experimentally our approach on a custom dataset and we show its merit compared to the conventional dynamic time warping approach.[/color]},
	doi = {10.1109/ICASSPW59220.2023.10193012},
	author = {Arvanitis, Nikolaos and Sartinas, Evangelos and Kosmopoulos, Dimitrios}
}
@proceedings {146,
	title = {PupilTAN: A Few-Shot Adversarial Pupil Localizer},
	journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, GAZE 2021: Gaze Estimation and Prediction in the Wild },
	year = {2021},
	pages = {3134-3142},
	publisher = {BEST PAPER AWARD},
	abstract = {<p class="p1" style="margin-top: 0px; margin-bottom: 0px; font-variant-numeric: normal; font-variant-east-asian: normal; font-stretch: normal; font-size: 10px; line-height: normal; font-family: Helvetica;">The eye center localization is a challenging problem faced by many computer vision applications. The challenges typically stem from the scene variability, such as, the wide range of shapes, the lighting conditions, the view angles and the occlusions. Nowadays, the increasing interest on deep neural networks requires a large volume of training data. However, a significant issue is the dependency on labeled data, which are expensive to obtain and susceptible to errors. To address these issues, we propose a deep network, dubbed PupilTAN, that performs image to-heatmap Translation and an Adversarial training framework that solves the eye localization problem in a few-shot unsupervised way. The key idea is to estimate, by using only a few ground-truth shots, the heatmaps centers{\textquoteright} pdf and use it as a generator to create random heatmaps that follow the same probability distribution of the real ones. We showcase that training the deep network with these artificial heatmaps in an adversarial framework not only makes us less dependent on labeled data, but also leads to a significant accuracy improvement. The proposed network achieves realtime performance in a general-purpose computer environment and improves the state-of-the-art accuracy for both MUCT and BioID datasets, even compared with supervised techniques. Furthermore, our model is robust even in the case of reducing its size of up to 1/16 of the original network (0.2M parameters), demonstrating comparable accuracy to the state-of-the-art with high practical value to real-time applications.</p>},
	author = {Nikolaos Poulopoulos and Emmanouil Z. Psarakis and Dimitrios Kosmopoulos}
}
@proceedings {130,
	title = {A Prototype Towards Modeling Visual Data Using Decentralized Generative Adversarial Networks},
	journal = {IEEE International Conference on Image Processing},
	year = {2018},
	month = {in press},
	abstract = {<p class="p1">Decentralized computation is crucial for training on large data sets, which are stored in various locations. This paper proposes a method for collaboratively training generative adversarial networks (GANs) on several nodes to approach this problem. Each node has access to its local private dataset and model, but can influence and can be influenced by neighboring nodes. Such an approach avoids the need of a fusion center, offers better network load balance, higher robustness to network errors and improves data privacy. This work proposes an initial approach for decentralized GAN network optimization which is based on discriminator exchange among neighbors using an information gain criterion. We present our initial experiments to verify whether such a decentralized architecture can provide useful results.</p>},
	author = {Dimitrios I. Kosmopoulos}
}
@inbook {124,
	title = {Plant Leaf Recognition Using Zernike Moments and Histogram of Oriented Gradients},
	booktitle = {Artificial Intelligence: Methods and Applications},
	series = {Lecture Notes in Computer Science},
	volume = {8445},
	year = {2014},
	pages = {406-417},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {A method using Zernike Moments and Histogram of Oriented Gradients for classification of plant leaf images is proposed in this paper. After preprocessing, we compute the shape features of a leaf using Zernike Moments and texture features using Histogram of Oriented Gradients and then the Sup-port Vector Machine classifier is used for plant leaf image classification and recognition. Experimental results show that using both Zernike Moments and Histogram of Oriented Gradients to classify and recognize plant leaf image yields accuracy that is comparable or better than the state of the art. The method has been validated on the Flavia and the Swedish Leaves datasets as well as on a combined dataset.},
	keywords = {Histogram of oriented gradients, Leaf recognition, Support vector machine, Zernike moments},
	isbn = {978-3-319-07063-6},
	doi = {10.1007/978-3-319-07064-3_33},
	url = {http://dx.doi.org/10.1007/978-3-319-07064-3_33},
	author = {Tsolakidis, DimitrisG. and Kosmopoulos, DimitriosI. and Papadourakis, George},
	editor = {Likas, Aristidis and Blekas, Konstantinos and Kalles, Dimitris}
}
