Dr. Cosimo Distante Profile

Dr. Cosimo Distante

at CNR-ISASI Lecce Unit

SPIE Involvement:

Author

Publications (19)

Proceedings Article | 9 August 2023 Presentation + Paper

Synthetic data generation for improvement of machine learning-based optical quality control: a practical approach in the welding context

Omar De Mitri, Andreas Frommknecht, Marco Huber, Felix Müller-Graf, Cosimo Distante

Proceedings Volume 12621, 126210Y (2023) https://doi.org/10.1117/12.2682047

KEYWORDS: Image segmentation, Data modeling, Image quality, Quality control, Machine learning, Porosity, Neural networks

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Proceedings Article | 9 August 2023 Presentation + Paper

Deep learning-enabled far-infrared active imaging and video-surveillance through fire

Vittorio Bianco, Jaromír Běhal, Pier Luigi Mazzeo, Marika Valentino, Paolo Spagnolo, Lisa Miccio, Cosimo Distante, Pietro Ferraro

Proceedings Volume 12621, 126210W (2023) https://doi.org/10.1117/12.2674990

KEYWORDS: Flame, Infrared imaging, Fire, Infrared sensors, Sensors, Imaging systems, Infrared lasers, Infrared cameras, Bandpass filters, Active imaging

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Proceedings Article | 20 June 2021 Presentation + Paper

Holographic fingerprint as a morphological marker to identify micro-plastics

Vittorio Bianco, Pasquale Memmolo, Daniele Pirone, Pierluigi Carcagnì, Francesco Merola, Lisa Miccio, Melania Paturzo, Cosimo Distante, Pietro Ferraro

Proceedings Volume 11782, 117820J (2021) https://doi.org/10.1117/12.2593832

KEYWORDS: Holography, Water, Ocean optics, Digital holography, Scanning electron microscopy, Machine learning, Imaging systems, Atomic force microscopy, Analytical research, Testing and analysis

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Proceedings Article | 20 June 2021 Paper

An investigation on deep learning approaches for diatoms classification

Pierluigi Carcagni, Andouglas Goncalves da Silva, Pasquale Memmolo, Vittorio Bianco, Francesco Merola, Luiz Marcos Garcia Goncalves, Andouglas Silva, Pietro Ferraro, Cosimo Distante

Proceedings Volume 11785, 117850Q (2021) https://doi.org/10.1117/12.2593252

KEYWORDS: Water, Taxonomy, Oceanography, Holography, Environmental monitoring, Neural networks, Microscopy, Machine vision, Image classification, Holograms

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Proceedings Article | 21 June 2019 Paper

Characterization of microplastics by holographic features for automatic detection in heterogeneous samples

V. Bianco, P. Memmolo, F. Merola, P. Carcagni, M. Paturzo, C. Distante, P. Ferraro

Proceedings Volume 11060, 110601E (2019) https://doi.org/10.1117/12.2527496

KEYWORDS: Oceanography, Holography, Water, Holograms, Sensors, Microscopy, Coherence imaging, Digital holography, Positron emission tomography, CCD image sensors

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Proceedings Article | 21 June 2019 Presentation + Paper

Convolutional neural networks for recognition and segmentation of aluminum profiles

Pier Luigi Mazzeo, Arturo Argentieri, Federico De Luca, Paolo Spagnolo, Cosimo Distante, Marco Leo, Pierluigi Carcagnì

Proceedings Volume 11059, 110590S (2019) https://doi.org/10.1117/12.2525687

KEYWORDS: Image segmentation, Aluminum, Neural networks, Computer aided design, Data modeling, Convolutional neural networks, Solid modeling, Cameras, Network architectures, Feature extraction

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Learning to automatically recognize objects in the real world is a very important and stimulating challenge. This work deals with the problem of detecting aluminium profiles within images, using hierarchical representations such as those based on deep learning methods. The use of regional CNN, a conceptually simple, flexible, and general framework for object instance segmentation, allows to exceed the previous state-of-the-art results. This approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. Neural network training uses ResNet networks of depth 50 or 101 layers. In particular, the training dataset consists of synthetic data generated by CAD files. The Dataset creation process is fundamental: experimental results show that trivial datasets lead to poor detection performance. A rich dataset, instead, including more complex images, allows the network to learn more and better guaranteeing excellent results. How to get more data, if you do not have more data? To get more data, we just need to make minor alterations such as flips, scale or rotations to existing dataset. This process is known as Data augmentation. The performance of the proposed system strongly depends on the dataset used for training and on the backbone architecture used. This why, adopting a strategy that generates a priori a very large dataset the time required to create the annotation file grows almost exponentially. The validation and test dataset, on the other hand, consists of real images captured by cameras. The distinctiveness of this work consists to train a deep neural network with synthetic data (aluminium CAD files) and verify if it on real data. Experiments show that the implementation of architecture, as described above, leads to good performance in automatic detection and classification. Future work will be addressed to improve the network training process together with the architecture, the algorithms and the dataset creation process. The latter is proved to be fundamental for the balance and optimization of the whole process. The way is to develop not much augmented datasets focusing on online data augmentation during network training.

Proceedings Article | 21 June 2019 Presentation + Paper

Image acquisition, evaluation and segmentation of thermal cutting edges using a mobile device

Omar De Mitri, Janek Stahl, Christian Jauch, Cosimo Distante

Proceedings Volume 11059, 110590U (2019) https://doi.org/10.1117/12.2527993

KEYWORDS: Image quality, Image segmentation, Mobile devices, Image processing, Convolutional neural networks, Image processing algorithms and systems

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Proceedings Article | 21 June 2019 Presentation + Paper

Identification and classification of biological micro-organisms by holographic learning

Pasquale Memmolo, Vittorio Bianco, Pierluigi Carcagnì, Andouglas Goncalves da Silva Junior , Luiz Marcos Garcia Goncalves, Francesco Merola, Melania Paturzo, Cosimo Distante, Pietro Ferraro

Proceedings Volume 11060, 110600H (2019) https://doi.org/10.1117/12.2527484

KEYWORDS: Digital holography, Holography, Holograms, Microorganisms, Intelligence systems, Feature extraction, Phase imaging, Machine learning, Image classification, Applied sciences

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Proceedings Article | 21 June 2019 Presentation + Paper

How holographic imaging can improve machine learning

Pasquale Memmolo, Vittorio Bianco, Pierluigi Carcagnì, Francesco Merola, Melania Paturzo, Cosimo Distante, Pietro Ferraro

Proceedings Volume 11059, 1105908 (2019) https://doi.org/10.1117/12.2527480

KEYWORDS: Holography, Digital holography, Machine learning, Microscopy, Holograms, Microscopes, Intelligence systems, Refractive index, 3D image reconstruction, Digital imaging

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Proceedings Article | 4 March 2019 Presentation + Paper

High-accuracy identification of micro-plastics by holographic microscopy enabled support vector machine

V. Bianco, P. Memmolo, F. Merola, P. Carcagni, C. Distante, P. Ferraro

Proceedings Volume 10887, 108870F (2019) https://doi.org/10.1117/12.2509515

KEYWORDS: Holography, Microscopy, Oceanography, Water, Machine learning, Holograms, Microscopes, Digital holography, Intelligence systems, Liquids

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Proceedings Article | 22 June 2015 Paper

Color holograms synthesis framework for three-dimensional scene reconstruction

Pasquale Memmolo, Marco Leo, Cosimo Distante, Melania Paturzo, Pietro Ferraro

Proceedings Volume 9525, 95250L (2015) https://doi.org/10.1117/12.2185017

KEYWORDS: Holograms, Digital holography, Holography, Chromatic aberrations, Speckle, Denoising, Multiplexing, Computer generated holography, Reconstruction algorithms, Photography

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Proceedings Article | 22 June 2015 Paper

A FragTrack algorithm enhancement for total occlusion management in visual object tracking

F. Adamo, P. Mazzeo, P. Spagnolo, C. Distante

Proceedings Volume 9530, 95300R (2015) https://doi.org/10.1117/12.2185174

KEYWORDS: Detection and tracking algorithms, Optical tracking, Video, Visualization, Video surveillance, Statistical analysis, Radon, Machine vision, Associative arrays, Data modeling

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SPIE Journal Paper | 26 June 2014

Multilevel bidimensional empirical mode decomposition: a new speckle reduction method in digital holography

Marco Leo, Roberta Piccolo, Cosimo Distante, Pasquale Memmolo, Melania Paturzo, Pietro Ferraro

OE, Vol. 53, Issue 11, 112314, (June 2014) https://doi.org/10.1117/12.10.1117/1.OE.53.11.112314

KEYWORDS: Speckle, Image filtering, Image processing, Digital filtering, Denoising, Image quality, Digital holography, Holograms, Signal processing, 3D image reconstruction

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SPIE Journal Paper | 25 September 2013

Unsupervised approach for the accurate localization of the pupils in near-frontal facial images

Marco Leo, Dario Cazzato, Tommaso De Marco, Cosimo Distante

JEI, Vol. 22, Issue 03, 033033, (September 2013) https://doi.org/10.1117/12.10.1117/1.JEI.22.3.033033

KEYWORDS: Eye, Databases, Light sources and illumination, Video, Image processing, Eye models, Glasses, Facial recognition systems, Cameras, Mirrors

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Proceedings Article | 15 May 2012 Paper

An autofocusing algorithm for digital holograms

P. Ferraro, P. Memmolo, C. Distante, M. Paturzo, A. Finizio, B. Javidi

Proceedings Volume 8384, 838408 (2012) https://doi.org/10.1117/12.920000

KEYWORDS: Holograms, Digital holography, 3D image reconstruction, Reconstruction algorithms, Holography, Algorithm development, Error analysis, Image restoration, Reliability, Speckle

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Proceedings Article | 27 May 2011 Paper

An algorithm for the estimation of the in-focus distance for speckle holograms

P. Memmolo, C. Distante, M. Paturzo, A. Finizio, P. Ferraro, B. Javidi

Proceedings Volume 8082, 808209 (2011) https://doi.org/10.1117/12.889534

KEYWORDS: Holograms, Digital holography, 3D image reconstruction, Reconstruction algorithms, Speckle, Holography, Algorithm development, Image restoration, 3D displays, Algorithms

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Proceedings Article | 15 May 2007 Paper

Detection of unburned fuel as contaminant in engine oil by a gas microsensor array

Simonetta Capone, Marzia Zuppa, Dominique Presicce, Mauro Epifani, Luca Francioso, Pietro Siciliano, C. Distante

Proceedings Volume 6589, 658911 (2007) https://doi.org/10.1117/12.722007

KEYWORDS: Sensors, Contamination, Neurons, Principal component analysis, Statistical analysis, Gas sensors, Microsensors, Metals, Chemical analysis, Brain mapping

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Proceedings Article | 5 October 2001 Paper

Rail defect classification by adaptive self-organized map

Massimiliano Nitti, Clelia Mandriota, Cosimo Distante, Ettore Stella

Proceedings Volume 4572, (2001) https://doi.org/10.1117/12.444219

KEYWORDS: Image filtering, Neurons, Cameras, Image segmentation, Image classification, Sensors, Machine learning, Optical inspection, Inspection, Diagnostics

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Proceedings Article | 6 October 1998 Paper

Vision-based approach for learning an elementary navigation behavior

Tiziana D'Orazio, Grazia Cicirelli, Cosimo Distante

Proceedings Volume 3522, (1998) https://doi.org/10.1117/12.325778

KEYWORDS: Sensors, Robots, Visualization, Computing systems, Environmental sensing, Fuzzy logic, Navigation systems, Complex systems, Visual process modeling, Cameras

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Showing 5 of 19 publications

Conference Committee Involvement (3)

Multimodal Sensing and Artificial Intelligence: Technologies and Applications III

27 June 2023 | Munich, Germany

Multimodal Sensing and Artificial Intelligence: Technologies and Applications II

21 June 2021 | Online Only, Germany

Multimodal Sensing and Artificial Intelligence: Technologies and Applications

26 June 2019 | Munich, Germany

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