It has been active in the Department of Electrical, Computer and Biomedical Engineering of the University of Pavia since the early 70s.

The group’s initial research activities concentrated on image enhancement and restoration techniques, with a particular focus on medical imagery.

Subsequently, the group’s main efforts have been devoted to more advanced image processing functions, involving scene segmentation and shape characterization; a broad background has been acquired on low-level and intermediate-level vision tasks involving grey-level statistics and structural descriptions.

ADDRESS

Computer Vision and Multimedia Lab

(Laboratorio di Visione Artificiale e Multimedia)

Dipartimento di Ingegneria Industriale e dell’Informazione

Università degli Studi di Pavia

Via Ferrata 5 - 27100 Pavia, ITALY

Tel: +39 0382 985372/985486 - Fax: +39 0382 985373

Email: web-vision@unipv.it - Website: vision.unipv.it

WHO

• 6 staff people

• Virginio Cantoni – full professor and director

• Luca Lombardi, Marco Porta – associate professors

• Giuseppe Lisanti, Mauro Mosconi – assistantprofessors

• Alessandra Setti – technician

• 2 post-doc researchers

• Piercarlo Dondi, Mirto Musci

• 2 contract professors

• Roberto Marmo, Marco Piastra

• 2 Phd Students

• Gianluca Gerard, Haochen Wang

Recently new research areas have been activated on

Deep Learning in Computer Vision, Deep Reinforcement Learning, Eye Tracking, Proteomics, Social Media Analysis, Mobile Mapping, Digital Heritage, Digital Humanities.

Since the early 80s a new stream of research has been actively followed in the field of parallel architectures for vision and image processing.

The group has meanwhile developed skills in high-level image processing domains, such as the management of knowledge description and learning capabilities for vision tasks.

Deep Learning in Computer Vision

• Several applications:

Image Colorization

Image Enhancement

Pixel Inference

?

Face Recognition

• Learn a discriminative model to recognize persons. Collect thousands images for thousands persons and train a deep neural network (data driven approach):

Deep Convolutional Neural Network

Deep Reinforcement Learning for Collaborative Robotics

Virtualization of a real-world robot

Complete 3D mesh Movable parts Joints and direct kinematic chain

Learning obstacle avoidance in a virtualized simulation environment

Moving obstacle

Target

Incrementalautonomous

learning

Robustavoidance

strategy

Eye TrackingExplicit and Implicit Gaze-Based Communication

Soft Biometrics Identifying or verifying the identity of people from the way they look at specific stimuli (e.g., faces)

E-LearningUnderstanding learners’ behavior and detecting possible comprehension problems

AutomotiveStudying the driver’s performance through cheap eye tracking solutions

Gaze Input Using eye tracking as an assistive technology or as an additional input channel (besides keyboard, mouse, etc.)

ProteomicsGeometrical motif extraction in the secondary structure of proteins

Protein structure define its biological behavior.We work at second level to identify recurrent motifs.

We view proteins as a cloud of segments, we expect big data. Goal: discover new motifs with an innovative geometrical method.

Using Hough Transform we developed the Cross Motif Search algorithm with MP and MPI for parallelism.

Motif Visualizer: Open Source OpenGL GUI to improve usability Collaboration and validation with biologists.

Social Media Analysis

• Network analysis

• Visual object recognition

• Analytics and insights

• Social commerce

• Customer segmentation

• Malware detection

• Social Media Mining

• Sentiment analysis, opinion mining

Mobile Mapping

Vehicle-mounted high-accuracy mobile 3D measurement system:

• Road and railway sign recognition

• Tunnel inspection

• Intelligent vehicle

• Laser scanner

• Georeferenced images

Digital Heritage

Acquisition

Classification and Labeling

Kinect interactive presentation

UVF images

Visible images

UVF classified images

Author 1

Reference Letters Author 1

Matching and

attribution

Documents

Not Author 1

Unsure

3D scan and measurement of historical instruments

Analysis and presentation of visible and UV induced fluorescence (UVF) images

Handwriting analysis of historical documents

Digital Humanities

Tactile imagesThe battle of Pavia, attack of the French

3D modelingA Certosa portal bas-relief

Augmented realityThe city of Pavia in the Renaissance

LEGEND

Francis I F

his horse F

Bonnivet G

des Prez N

Mont-Jehan W

Sanseverino O

Charles III C

Imperial cavalry Band B

Imperial infantryE and E

The Samaritan woman at the Jacob’s well

An application in the area of Digital HumanitiesVirtual reconstruction of Pavia in the 16th century

The realized 3D reconstruction has three goals:1. creation of an interactive virtual environment of 16th century Pavia in which buildings, churches

and other landmarks are individually represented;2. reproduction of different views of the city so that the viewer can move around freely, touring the

city as depicted in the fresco;3. development of an app for smartphones and tablets providing access to information about the

area displayed, which, in particular, compares still existing buildings to those which have been reconstructed virtually.

Videos have been shot on routes that show the main buildings modeled in 3D, so that you can walk through the city of Pavia in the Renaissance.

In particular, Route 9 (https://www.youtube.com/watch?v=pSFlBRgzECw&feature=youtu.be) point of view starts about 150 meters high, 500 meters South of the city, and it runs at 360° around Pavia, flying around the city.

Other videos show in details specific areas and salient buildings modeled in 3D as they were in the Renaissance.

An application in the area of Digital HumanitiesVirtual reconstruction of Pavia in the 16th century