Machine Learning and Perception Lab

ACHIEVE-ITN aims at training a new generation of scientists through a research programme on highly integrated hardware-software components for the implementation of ultra-efficient embedded vision systems as the basis for innovative distributed vision applications.

The course introduces the student to the classic theme of computer vision for automation. The course requires coding skills since half of it is about lab. exercise. Let’s make robot see.

The course offers an introduction to Machine Learning techniques. It is offered within the Master Degree in Multimedia Communication and Information Technology of the University of Udine.

Research

The focus of the lab. is conducting innovative research in the field of machine learning and deep learning. Latest topics cover deep learning for video surveillance and food recognition, reinforcement learning for video tracking, deep tree neural structures for image retrieval and many more.

it’s a pleasure to meet you here. I hope you can find out what we are doing and maybe ideas to cooperate with us. We are always open to collaborations either you are a student or a researcher.

Open Positions

We are always looking for new proactive memebers. Just mail us

Thesis

There are many available thesis in the field of machine learning. If you can, cam to our lab., meet us and let discuss together about your ideas and ambitions for the thesis. We’ll find out something that will make you happy to achieve and that will be usefull for the lab. and its members.

Internships

The lab. accepts internships from both bachelor and master students. Internship is a moment when the student gets in contact with a group and its habits in pursuing research results. It’s a good way to understand what someone watns to achieve in life.

Machine Learning

The course offers an introduction to Machine Learning techniques. It is offered within the Master Degree in Multimedia Communication and Information Technology of the University of Udine.

Machine Vision

The course introduces the student to the classic theme of computer vision for automation. The course requires coding skills since half of it is about lab. exercise. Let’s make robot see.

Jorge Garcia Castano

I worked with the group of MLP to develop a Person Re-Identification solution. The time spend in Udine with the group was fantastic

18 Oct

The MLP team wins the VOT2021 Long-Term Challenge!

We are excited to announce that our team has been awarded as the best solution for the Visual Object Tracking VOT2021 Long-Term tracking challenge. This is the most important competition of such a kind in the computer vision community, and it sees the participation of the most cutting-edge solutions in the field. The VOT challenge…

27 Aug

Two Papers at ECCV 2020

Our team is present at the European Conference on Computer Vision (ECCV) 2020 with two workshop papers! Both will be presented on Friday 28th August. The first “Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution”, by Rao Muhammad Umer and Christian Micheloni, will be presented at the Advances in Image Manipulation Workshop…

13 Jun

New Paper at CVPR 2020

Our team will present the paper titled “Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-Resolution” in the NTIRE 2020 workshop which is held in conjunction with CVPR 2020. Here is the abstract: Most current deep learning based single image super-resolution (SISR) methods focus on designing deeper / wider models to learn the non-linear mapping…

The lab. is situated in the scientific pole of “Rizzi”. Its fascinating structure allows to test many machine learning and perception solutions.

Research

Research

Message From Director

Machine Learning

Machine Vision

Prof. Christian Micheloni

Prof. Niki Martinel

Dr. Matteo Dunnhofer

Dr. Vitalii Emelianov

Dr. Zaira Manigrasso

Prof. Gian Luca Foresti

Asif Hussain Khan

Mubashara Rehman

Nasar Iqbal

Moritz Nottebaum

Jorge Garcia Castano

The MLP team wins the VOT2021 Long-Term Challenge!

Two Papers at ECCV 2020

New Paper at CVPR 2020

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