2026 Stefano Mattoccia: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	43	8018	7791	215	213	164	7340

Stefano Mattoccia publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Stefano Mattoccia sits on this spectrum.

32 publications 991+

This scientist: 164 publications — 32nd percentile

32% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Stefano Mattoccia D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Stefano Mattoccia sits on this spectrum.

30 D-Index 131+

This scientist: 43 D-Index — 46th percentile

46% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Stefano Mattoccia is affiliated with the University of Bologna in Italy. Their research primarily focuses on fields related to Computer Science and Engineering, with a particular emphasis on Computer Vision and Pattern Recognition. They have also contributed to Media Technology and Aerospace Engineering, demonstrating a multidisciplinary approach to their work.

The scientist's research topics include Advanced Vision and Imaging, Optical Measurement and Interference Techniques, Image Processing Techniques and Applications, Advanced Image Processing Techniques, Robotics and Sensor-Based Localization, Image Enhancement Techniques, and Advanced Optical Sensing Technologies.

Stefano Mattoccia has published extensively, with recent papers covering diverse aspects of computer vision and image-based sensing technologies. Notable recent publications include:

"On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey" (2021) published in IEEE Transactions on Pattern Analysis and Machine Intelligence
"A computer vision approach based on deep learning for the detection of dairy cows in free stall barn" (2021) published in Computers and Electronics in Agriculture
"Semantisk stereomatchning i realtid" (2020) published in Publications (Konstfack University of Arts, Crafts, and Design)
"Real-Time Single Image Depth Perception in the Wild with Handheld Devices" (2020) published in MDPI (MDPI AG)
"Enabling Image-Based Streamflow Monitoring at the Edge" (2020) published in Remote Sensing

Their frequent coauthors include Matteo Poggi, Fabio Tosi, Filippo Aleotti, Luigi Di Stefano, and Pierluigi Zama Ramirez. Collaboration with these researchers reflects ongoing teamwork in advancing topics within the computer vision and imaging fields.

Stefano Mattoccia has contributed significantly to several publication venues, most frequently publishing in:

arXiv (Cornell University)
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Vision and Image Understanding

Best Publications

A fast area-based stereo matching algorithm

Luigi Di Stefano;Massimiliano Marchionni;Stefano Mattoccia

445
Citations
Segmentation-based adaptive support for accurate stereo correspondence

Federico Tombari;Stefano Mattoccia;Luigi Di Stefano

293
Citations
ZNCC-based template matching using bounded partial correlation

Luigi Di Stefano;Stefano Mattoccia;Federico Tombari

289
Citations
Classification and evaluation of cost aggregation methods for stereo correspondence

F. Tombari;S. Mattoccia;L. Di Stefano;E. Addimanda

285
Citations
MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer

Unknown

255
Citations
Real-Time Self-Adaptive Deep Stereo

Alessio Tonioni;Fabio Tosi;Matteo Poggi;Stefano Mattoccia

244
Citations
Learning Monocular Depth Estimation Infusing Traditional Stereo Knowledge

Fabio Tosi;Filippo Aleotti;Matteo Poggi;Stefano Mattoccia

224
Citations
On the Uncertainty of Self-Supervised Monocular Depth Estimation

Matteo Poggi;Filippo Aleotti;Fabio Tosi;Stefano Mattoccia

220
Citations
Fast template matching using bounded partial correlation

Luigi Di Stefano;Stefano Mattoccia

175
Citations
Learning Monocular Depth Estimation with Unsupervised Trinocular Assumptions

Matteo Poggi;Fabio Tosi;Stefano Mattoccia

163
Citations
Accurate and efficient cost aggregation strategy for stereo correspondence based on approximated joint bilateral filtering

Stefano Mattoccia;Simone Giardino;Andrea Gambini

159
Citations
Towards Real-Time Unsupervised Monocular Depth Estimation on CPU

Matteo Poggi;Filippo Aleotti;Fabio Tosi;Stefano Mattoccia

156
Citations
Performance Evaluation of Full Search Equivalent Pattern Matching Algorithms

Wanli Ouyang;F. Tombari;S. Mattoccia;L. Di Stefano

154
Citations
Linear stereo matching

Leonardo De-Maeztu;Stefano Mattoccia;Arantxa Villanueva;Rafael Cabeza

140
Citations
A wearable mobility aid for the visually impaired based on embedded 3D vision and deep learning

Matteo Poggi;Stefano Mattoccia

130
Citations
CompletionFormer: Depth Completion with Convolutions and Vision Transformers

Unknown

129
Citations
Geometry Meets Semantics for Semi-supervised Monocular Depth Estimation

Pierluigi Zama Ramirez;Matteo Poggi;Fabio Tosi;Stefano Mattoccia

128
Citations
On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey.

Matteo Poggi;Fabio Tosi;Konstantinos Batsos;Philippos Mordohai

125
Citations
Fast Full-Search Equivalent Template Matching by Enhanced Bounded Correlation

S. Mattoccia;F. Tombari;L. Di Stefano

122
Citations
Unsupervised Adaptation for Deep Stereo

Alessio Tonioni;Matteo Poggi;Stefano Mattoccia;Luigi Di Stefano

119
Citations
An efficient algorithm for exhaustive template matching based on normalized cross correlation

L. Di Stefano;S. Mattoccia;M. Mola

110
Citations
Learning from scratch a confidence measure.

Matteo Poggi;Stefano Mattoccia

108
Citations
Generative Adversarial Networks for Unsupervised Monocular Depth Prediction

Filippo Aleotti;Fabio Tosi;Matteo Poggi;Stefano Mattoccia

105
Citations

Frequent Co-Authors

Luigi Di Stefano University of Bologna

Federico Tombari Technical University of Munich

Pietro Zanuttigh University of Padua

Salvatore Grimaldi Tuscia University

Dongbo Min Ewha Womans University

Kwanghoon Sohn Yonsei University

Wanli Ouyang Shanghai AI Lab

Ghassan AlRegib Georgia Institute of Technology

Claudio Melchiorri University of Bologna

Gianluca Palli University of Bologna

External Links

Personal website of Stefano Mattoccia Google Scholar page

If you think any of the details on this page are incorrect, let us know.

Related Online Degrees & Career Pathways

Exploring online degrees in Computer Science or related fields can open up flexible and affordable education opportunities. Many students consider entry-level options like online associate degrees, which can be completed in as little as six months. These are great for building foundational skills or quickly entering the workforce.

For those seeking advanced expertise, pursuing cheap doctoral programs online allows students to earn terminal degrees while saving on tuition. Accelerated options such as 1 year edd programs online are also available, ideal for professionals who want to fast-track their education and career in academia or industry.

If you’re interested in widening your career options, consider online business degree programs accredited by recognized institutions. These programs provide business acumen valuable in tech leadership or management roles. Overall, online degrees offer diverse pathways to advance your education and career flexibly and affordably.