Cosimo DISTANTE
Docente a contratto
Image Processing
Computer Vision and Pattern Recognition
tel 0832 1975300
Image Processing
Computer Vision and Pattern Recognition
tel 0832 1975300
Curriculum Vitae
Cosimo Distante PhD – Institute of Applied Sciences and Intelligent Systems - ISASI CNR
Master in Computer Science at the University Bari, and PhD in Engineering at University of Salento (Italy). His main expertise are in the field of Artificial Intelligent, in particular Computer Vision and Pattern Recognition, Image Processing, Machine Learning and Robotics. Particular emphasis is devoted to the development of Deep Learning algorithms. He has been a visiting researcher at the Computer Science Department of the University of Massachusetts (Amherst, MA) where has carried out research activities under robotics, artificial neural networks and vision. Dr. Distante joined the Faculty of the University of Massachusetts as a Teaching Assistant, for the "Artificial Intelligence" 683 class of the Master in Sciences 1998. He received the Ph.D. in Material Engineering from the University of Salento. Dr. Distante joined in 2001 the Italian National Research Council of Italy CNR, focusing his research activity in the context of robot learning, visual grasping and pattern recognition applied to array of chemical sensors. Dr. Distante has been awarded with several fellowships, among them the CNR-NATO in which he has collaborated with the University of Manchester (Prof. Krishna Persaud) for the study of pattern recognition techniques applied to the context of gas sensor arrays for parameter drift reduction. Since 2003 he is Contract Professor for the courses of Pattern Recognition and Image Processing in Computer Engineering at the University of the Salento IT. Since 2010 he started a technology transfer activity by establishing a startup spinoff company named Taggalo.
In 2011, Dr. Distante has been awarded with the national innovation Prize working capital PNI-Cube TelecomItalia with the Taggalo project. He won the ChallengUP (Cisco, Intel and Deutch Telekom acceleration program) contest in 2015 with Taggalo. He served as general chair of several established conferences, among them the IEEE AVSS 2017 that focus on videosurveillance, the Video analytics for Audience mearuserement that focus on computer vision tech. for retail and digital signage. He is coordinating several joint research lab between CNR and Leonardo-Finmeccanica for security technology that focus on face recognition in the wild and videosurveillance, and between CNR and a pool of SMEs for the health sector. He is the unit manager of the Institute of Applied sciences and Intelligent system of the CNR where leads a group of researchers, PhD students, coordinates two industrial joint labs (security and health).
He is expert member in innovation for the Ministry of Education, Research and University (MIUR), as well as the Ministry of economic development (MISE) and a few regional agencies for technology and innovation.
II Semester
Didattica
A.A. 2023/2024
COMPUTER VISION E DEEP LEARNING
Corso di laurea INGEGNERIA INFORMATICA
Tipo corso di studio Laurea Magistrale
Lingua ITALIANO
Crediti 9.0
Ripartizione oraria Ore totali di attività frontale: 81.0
Anno accademico di erogazione 2023/2024
Per immatricolati nel 2022/2023
Anno di corso 2
Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE
Percorso Intelligenza artificiale
Sede Lecce
A.A. 2021/2022
COMPUTER VISION
Degree course COMPUTER ENGINEERING
Course type Laurea Magistrale
Language INGLESE
Credits 9.0
Teaching hours Ore totali di attività frontale: 81.0
Year taught 2021/2022
For matriculated on 2021/2022
Course year 1
Structure DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE
Subject matter PERCORSO COMUNE
Location Lecce
A.A. 2020/2021
COMPUTER VISION
Degree course COMPUTER ENGINEERING
Course type Laurea Magistrale
Language INGLESE
Credits 9.0
Teaching hours Ore totali di attività frontale: 81.0
Year taught 2020/2021
For matriculated on 2020/2021
Course year 1
Structure DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE
Subject matter PERCORSO COMUNE
Location Lecce
A.A. 2019/2020
COMPUTER VISION
Degree course COMPUTER ENGINEERING
Course type Laurea Magistrale
Language INGLESE
Credits 9.0
Teaching hours Ore totali di attività frontale: 81.0
Year taught 2019/2020
For matriculated on 2019/2020
Course year 1
Structure DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE
Subject matter PERCORSO COMUNE
Location Lecce
A.A. 2018/2019
IMAGE PROCESSING
Degree course COMPUTER ENGINEERING
Course type Laurea Magistrale
Language INGLESE
Credits 9.0
Teaching hours Ore totali di attività frontale: 81.0
Year taught 2018/2019
For matriculated on 2018/2019
Course year 1
Structure DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE
Subject matter PERCORSO COMUNE
Location Lecce
COMPUTER VISION E DEEP LEARNING
Corso di laurea INGEGNERIA INFORMATICA
Settore Scientifico Disciplinare ING-INF/03
Tipo corso di studio Laurea Magistrale
Crediti 9.0
Ripartizione oraria Ore totali di attività frontale: 81.0
Per immatricolati nel 2022/2023
Anno accademico di erogazione 2023/2024
Anno di corso 2
Semestre Secondo Semestre (dal 04/03/2024 al 14/06/2024)
Lingua ITALIANO
Percorso Intelligenza artificiale (A202)
Sede Lecce
No prior experience with computer vision is assumed, although previous knowledge of visual computing or signal processing will be helpful. The following skills are necessary for this class:
- Math: Linear algebra, vector calculus, and probability. Linear algebra is the most important.
- Data structures: Students will write code that represents images as feature and geometric constructions.
- Programming: A good working knowledge. All lecture code and project starter code will be Python, and Pytorch for Deep Learning, but student familiar with other frameworks such as tensorflow is ok.
Computer Vision today is everywhere in our society and images have become pervasive, with applications in several sectors; just to mention some in: apps, drones, healthcare and precision medicine, precision agricolture, searching, understanding, control in robotics and self-driving cars.
The course introduces the basics of image formation, reconstruction and inferring motion models, as well as camera calibration theory and practice.
Recent developments in neural networks (Deep Learning) have considerably boosted the performance of the visual recognition systems in tasks such as: classification, localisation, detection, segmentation etc. Students will learn the building blocks of a general convolutional neural network, the way how it is trained and optimized, how to prepare a dataset and how to measure the final performance.
Upon completion of this course, students will:
- Be familiar with both the theoretical and practical aspects of computing with images;
- Have described the foundation of image formation, measurement, and analysis;
- Have implemented common methods for robust image matching and alignment;
- Understand the geometric relationships between 2D images and the 3D world;
- Have gained exposure to object and scene recognition and categorization from images;
- Grasp the principles of state-of-the-art deep neural networks; and
- Developed the practical skills necessary to build computer vision applications.
Teaching is based on theoretical and practical lectures. The student will write in python algorithms taught in class
Oral session. The student will explain the developed project and shall answer two or more questions regarding theoretical aspects of the studied topics
The student must develop a project by choosing a practical simple application with some algorithms done during the course. The choice is at total disposal of the student, as well as the fact of developing it in group os solo. In group setting the students must proof their own activities developed in the common project application.
The final examination is based on oral assessment of the topics covered during lectures.
For the LAB practice, students may use for the deep learning development the Google Colab or Cloud Platform.
Introduction to Computer Vision
Camera models and colors
Image Filtering
Fourier - image pyramids and blending
Detecting Corners
2D and 3D geometric primitives - Projections
Operations with images
Image Alignment - warping, homography estimation direct linear transform robust motion estimation with Ransac - perspective n point problem. Registration examples: face recognition, medical imaging
Camera Calibration - distortion models and compensations - linear methods for camera parameters. Calibration with a checkerboard
LAB - SIFT and camera calibration
Multiview geometry - Epipolar geometry, position error estimation, stereo rig, Essential matrix estimation, rectification, Reconstruction, correspondense problem, weak calibration and ransac estimation of fundamental matrix
Image Classification - Key nearest neighbor, linear classifiers
LAB - Canny edge detection, Hough Transform
Image Classification - loss functions, optimization with stochastic gradient descent
neural networks
LAB - Introduction to Pytorch framework
backpropagation, computational graphs and gradient estimation
Image Classification - Convolutional Neural Network architecture
Normalization; Image Classification - CNN architectures (Alexnet, VGG, GoogleNet, ResNET, DenseNet, SENet, EfficientNet), Siamese Architectures (applications to face verification, people and vehicle re-identification)
LAB - CNN
Recurrent networks- RNN, LSTM, GRU
Language modeling
Sequence-to-sequence
Image captioning
Attention Multimodal attention
Self-Attention
Transformers
Object detection Transfer learning
Object detection task
R-CNN detector
Non-Max Suppression (NMS)
Mean Average Precision (mAP)
Single-stage vs two-stage detectors
YOLO
Region Proposal Networks (RPN), Anchor Boxes
Two-Stage Detectors: Fast R-CNN, Faster R-CNN
Feature Pyramid Networks
LAB - Object detection
Object segmentation - Single-Stage Detectors: RetinaNet, FCOS
Semantic segmentation
Instance segmentation
Keypoint estimation
LAB - Deep Learning application to segmentation
Generative Models
Supervised vs Unsupervised learning
Discriminative vs Generative models
Autoregressive models
Variational Autoencoders
Motion estimation, Optical flow
Diffusion models
3D Vision - 3D shape representations
Depth estimation
3D shape prediction
Voxels, Pointclouds, SDFs, Meshes
Implicit functions, NeRF
Videos
Video classification
Early / Late fusion
3D CNNs
Two-stream networks
Transformer-based models
Reinforcement learning
There is no requirement to buy a book. The goal of the course is to be self contained, but sections from the following textbooks will be suggested for more formalization and information.
The primary course text will be Rick Szeliski’s draft Computer Vision: Algorithms and Applications 2nd Edition 2022; we will use an online copy (fill the form) at this link.
We will be using Piazza for all course notes, homework and final project.
A copy and link will be provided in website.
A textbook for Deep Learning with Pytorch script can be accessed at this link
Deep Learning, MIT Press book, Ian Goodfellow and Yoshua Bengio and Aaron Courville
COMPUTER VISION E DEEP LEARNING (ING-INF/03)
COMPUTER VISION
Degree course COMPUTER ENGINEERING
Subject area ING-INF/03
Course type Laurea Magistrale
Credits 9.0
Teaching hours Ore totali di attività frontale: 81.0
For matriculated on 2021/2022
Year taught 2021/2022
Course year 1
Semestre Secondo Semestre (dal 01/03/2022 al 10/06/2022)
Language INGLESE
Subject matter PERCORSO COMUNE (999)
Location Lecce
No prior experience with computer vision is assumed, although previous knowledge of visual computing or signal processing will be helpful. The following skills are necessary for this class:
- Math: Linear algebra, vector calculus, and probability. Linear algebra is the most important.
- Data structures: Students will write code that represents images as feature and geometric constructions.
- Programming: A good working knowledge. All lecture code and project starter code will be Python, and Pytorch for Deep Learning, but student familiar with other frameworks such as tensorflow is ok.
Computer Vision today is everywhere in our society and images have become pervasive, with applications in several sectors; just to mention some in: apps, drones, healthcare and precision medicine, precision agricolture, searching, understanding, control in robotics and self-driving cars.
The course introduces the basics of image formation, reconstruction and inferring motion models, as well as camera calibration theory and practice.
Recent developments in neural networks (Deep Learning) have considerably boosted the performance of the visual recognition systems in tasks such as: classification, localisation, detection, segmentation etc. Students will learn the building blocks of a general convolutional neural network, the way how it is trained and optimized, how to prepare a dataset and how to measure the final performance.
Upon completion of this course, students will:
- Be familiar with both the theoretical and practical aspects of computing with images;
- Have described the foundation of image formation, measurement, and analysis;
- Have implemented common methods for robust image matching and alignment;
- Understand the geometric relationships between 2D images and the 3D world;
- Have gained exposure to object and scene recognition and categorization from images;
- Grasp the principles of state-of-the-art deep neural networks; and
- Developed the practical skills necessary to build computer vision applications.
Teaching is based on theoretical and practical lectures. The student will write in python algorithms taught in class
Oral session. The student will explain the developed project and shall answer two or more questions regarding theoretical aspects of the studied topics
The student must develop a project by choosing a practical simple application with some algorithms done during the course. The choice is at total disposal of the student, as well as the fact of developing it in group os solo. In group setting the students must proof their own activities developed in the common project application.
The final examination is based on oral assessment of the topics covered during lectures.
For the LAB practice, students may use for the deep learning development the Google Colab or Cloud Platform.
Introduction to Computer Vision
Image Formation
2D and 3D geometric primitives - Projections
Color perception, color spaces and processing
Image Filtering
LAB Introduction to Python and Operations with images
interpolation, optimization, image pyramids and blending
Machine learning
LAB (pytorch basics? Dataloaders, ML?, T-sne?)
loss functions, optimization with stochastic gradient descent
backpropagation and neural networks, computational graphs and gradient estimation
Convolutional Neural Network, CNN activation functions, data preprocessing, weight normalization, batch normalization, monitoring the learning process, hyperparameter optimization, Regularization (Dropout, drop connect, fractional pooling, cotout, mixup)
CNN architectures (Alexnet, VGG, GoogleNet, ResNET, DenseNet, SENet, EfficientNet), Siamese Architectures (applications to face verification, people and vehicle re-identification)
LAB CNN
Recurrent neural networks, Attention mechanisms
Object detection and segmentation
LAB - object detection - segmentation
Generative Models
edges, feature matching Ransac and alignment
Optical flow, 3D, Depth perception and stereo
SLAM/SfM
Camera Calibration - distortion models and compensations - linear methods for camera parameters. Calibration with a checkerboard
LAB camera calib
3D shapes
There is no requirement to buy a book. The goal of the course is to be self contained, but sections from the following textbooks will be suggested for more formalization and information.
The primary course text will be Rick Szeliski’s draft Computer Vision: Algorithms and Applications 2nd Edition 2022; we will use an online copy (fill the form) at this link.
We will be using Piazza for all course notes, homework and final project.
A copy and link will be provided in website.
A textbook for Deep Learning with Pytorch script can be accessed at this link
Deep Learning, MIT Press book, Ian Goodfellow and Yoshua Bengio and Aaron Courville
COMPUTER VISION (ING-INF/03)
COMPUTER VISION
Degree course COMPUTER ENGINEERING
Subject area ING-INF/03
Course type Laurea Magistrale
Credits 9.0
Teaching hours Ore totali di attività frontale: 81.0
For matriculated on 2020/2021
Year taught 2020/2021
Course year 1
Semestre Primo Semestre (dal 22/09/2020 al 18/12/2020)
Language INGLESE
Subject matter PERCORSO COMUNE (999)
Location Lecce
No prior experience with computer vision is assumed, although previous knowledge of visual computing or signal processing will be helpful. The following skills are necessary for this class:
- Math: Linear algebra, vector calculus, and probability. Linear algebra is the most important.
- Data structures: Students will write code that represents images as feature and geometric constructions.
- Programming: A good working knowledge. All lecture code and project starter code will be Python, and Pytorch for Deep Learning, but student familiar with other frameworks such as tensorflow is ok.
Computer Vision today is everywhere in our society and images have become pervasive, with applications in several sectors; just to mention some in: apps, drones, healthcare and precision medicine, precision agricolture, searching, understanding, control in robotics and self-driving cars.
The course introduces the basics of image formation, reconstruction and inferring motion models, as well as camera calibration theory and practice.
Recent developments in neural networks (Deep Learning) have considerably boosted the performance of the visual recognition systems in tasks such as: classification, localisation, detection, segmentation etc. Students will learn the building blocks of a general convolutional neural network, the way how it is trained and optimized, how to prepare a dataset and how to measure the final performance.
Upon completion of this course, students will:
- Be familiar with both the theoretical and practical aspects of computing with images;
- Have described the foundation of image formation, measurement, and analysis;
- Have implemented common methods for robust image matching and alignment;
- Understand the geometric relationships between 2D images and the 3D world;
- Have gained exposure to object and scene recognition and categorization from images;
- Grasp the principles of state-of-the-art deep neural networks; and
- Developed the practical skills necessary to build computer vision applications.
Teaching is based on theoretical and practical lectures. The student will write in python algorithms taught in class
Oral session. The student will explain the developed project and shall answer two or more questions regarding theoretical aspects of the studied topics
The student must develop a project by choosing a practical simple application with some algorithms done during the course. The choice is at total disposal of the student, as well as the fact of developing it in group os solo. In group setting the students must proof their own activities developed in the common project application.
The final examination is based on oral assessment of the topics covered during lectures.
For the LAB practice, students may use for the deep learning development the Google Colab or Cloud Platform.
Introduction to Computer Vision
Image Formation
2D and 3D geometric primitives - Projections
image enhancement
LAB Introduction to Python and Operations with images
Color perception, color spaces and processing
Image Filtering
image pyramids and blending
Local feature detector
LAB SIFT with MatLab Find image rotation & scale SURF Object Detection
Image Alignment I- warping, homography estimation direct linear transform
Image Alignment II- robust motion estimation with Ransac - perspective n point problem. Registration examples: face recognition, medical imaging
Camera Calibration - distortion models and compensations - linear methods for camera parameters. Calibration with a checkerboard
LAB Mosaicking with SURF Face Detection and Tracking (Nose Skin) Face Detection and Tracking withKanade - Lucas - Tomasi feature tracker
Motion Analysis and background modelling, application to intelligent videosurveillance
Multiview geometry - Epipolar geometry, position error estimation, stereo rig, Essential matrix estimation, rectification, Reconstruction, correspondense problem, weak calibration and ransac estimation of fundamental matrix
LAB - Camera calibration
Image Classification - Key nearest neighbor, linear classifiers
Image Classification - loss functions, optimization with stochastic gradient descent
LAB - Stereo calibration and reconstruction
Image Classification - backpropagation and neural networks, computational graphs and gradient estimation
Image Classification - Convolutional Neural Network architecture
Image Classification - CNN activation functions, data preprocessing, weight normalization, batch normalization, monitoring the learning process, hyperparameter optimization, Regularization (Dropout, drop connect, fractional pooling, cotout, mixup)
Image Classification - CNN activation functions, data preprocessing, weight normalization, batch normalization, monitoring the learning process, hyperparameter optimization, Regularization (Dropout, drop connect, fractional pooling, cotout, mixup)
Image Classification - Object detection and Image segmentation with Convolutional neural networks. Introduction to auto-encoders
LAB - Introduction to Pytorch framework
LAB - Deep learning applications to object detection (Yolo and Faster R-CNN)
LAB - Deep Learning application to segmentation with mark R-CNN
There is no requirement to buy a book. The goal of the course is to be self contained, but sections from the following textbooks will be suggested for more formalization and information.
The primary course text will be Rick Szeliski’s draft Computer Vision: Algorithms and Applications; we will use an online copy of the June 19th draft. A copy and link will be provided in website. The secondary text is Forsyth and Ponce, Computer Vision: A Modern Approach (new Edition coming out in 2020)
Deep Learning, MIT Press book, Ian Goodfellow and Yoshua Bengio and Aaron Courville
COMPUTER VISION (ING-INF/03)
COMPUTER VISION
Degree course COMPUTER ENGINEERING
Subject area ING-INF/03
Course type Laurea Magistrale
Credits 9.0
Teaching hours Ore totali di attività frontale: 81.0
For matriculated on 2019/2020
Year taught 2019/2020
Course year 1
Semestre Primo Semestre (dal 23/09/2019 al 20/12/2019)
Language INGLESE
Subject matter PERCORSO COMUNE (999)
Location Lecce
No prior experience with computer vision is assumed, although previous knowledge of visual computing or signal processing will be helpful. The following skills are necessary for this class:
- Math: Linear algebra, vector calculus, and probability. Linear algebra is the most important.
- Data structures: Students will write code that represents images as feature and geometric constructions.
- Programming: A good working knowledge. All lecture code and project starter code will be Python, and Pytorch for Deep Learning, but student familiar with other frameworks such as tensorflow is ok.
Computer Vision today is everywhere in our society and images have become pervasive, with applications in several sectors; just to mention some in: apps, drones, healthcare and precision medicine, precision agricolture, searching, understanding, control in robotics and self-driving cars.
The course introduces the basics of image formation, reconstruction and inferring motion models, as well as camera calibration theory and practice.
Recent developments in neural networks (Deep Learning) have considerably boosted the performance of the visual recognition systems in tasks such as: classification, localisation, detection, segmentation etc. Students will learn the building blocks of a general convolutional neural network, the way how it is trained and optimized, how to prepare a dataset and how to measure the final performance.
Upon completion of this course, students will:
- Be familiar with both the theoretical and practical aspects of computing with images;
- Have described the foundation of image formation, measurement, and analysis;
- Have implemented common methods for robust image matching and alignment;
- Understand the geometric relationships between 2D images and the 3D world;
- Have gained exposure to object and scene recognition and categorization from images;
- Grasp the principles of state-of-the-art deep neural networks; and
- Developed the practical skills necessary to build computer vision applications.
Teaching is based on theoretical and practical lectures. The student will write in python algorithms taught in class
Oral session. The student will explain the developed project and shall answer two or more questions regarding theoretical aspects of the studied topics
The student must develop a project by choosing a practical simple application with some algorithms done during the course. The choice is at total disposal of the student, as well as the fact of developing it in group os solo. In group setting the students must proof their own activities developed in the common project application.
The final examination is based on oral assessment of the topics covered during lectures.
For the LAB practice, students may use for the deep learning development the Google Colab or Cloud Platform.
Introduction to Computer Vision
Image Formation
2D and 3D geometric primitives - Projections
image enhancement
LAB Introduction to Python and Operations with images
Color perception, color spaces and processing
Image Filtering
image pyramids and blending
Local feature detector
LAB SIFT with MatLab Find image rotation & scale SURF Object Detection
Image Alignment I- warping, homography estimation direct linear transform
Image Alignment II- robust motion estimation with Ransac - perspective n point problem. Registration examples: face recognition, medical imaging
Camera Calibration - distortion models and compensations - linear methods for camera parameters. Calibration with a checkerboard
LAB Mosaicking with SURF Face Detection and Tracking (Nose Skin) Face Detection and Tracking withKanade - Lucas - Tomasi feature tracker
Motion Analysis and background modelling, application to intelligent videosurveillance
Multiview geometry - Epipolar geometry, position error estimation, stereo rig, Essential matrix estimation, rectification, Reconstruction, correspondense problem, weak calibration and ransac estimation of fundamental matrix
LAB - Camera calibration
Image Classification - Key nearest neighbor, linear classifiers
Image Classification - loss functions, optimization with stochastic gradient descent
LAB - Stereo calibration and reconstruction
Image Classification - backpropagation and neural networks, computational graphs and gradient estimation
Image Classification - Convolutional Neural Network architecture
Image Classification - CNN activation functions, data preprocessing, weight normalization, batch normalization, monitoring the learning process, hyperparameter optimization, Regularization (Dropout, drop connect, fractional pooling, cotout, mixup)
Image Classification - CNN activation functions, data preprocessing, weight normalization, batch normalization, monitoring the learning process, hyperparameter optimization, Regularization (Dropout, drop connect, fractional pooling, cotout, mixup)
Image Classification - Object detection and Image segmentation with Convolutional neural networks. Introduction to auto-encoders
LAB - Introduction to Pytorch framework
LAB - Deep learning applications to object detection (Yolo and Faster R-CNN)
LAB - Deep Learning application to segmentation with mark R-CNN
There is no requirement to buy a book. The goal of the course is to be self contained, but sections from the following textbooks will be suggested for more formalization and information.
The primary course text will be Rick Szeliski’s draft Computer Vision: Algorithms and Applications; we will use an online copy of the June 19th draft. A copy and link will be provided in website. The secondary text is Forsyth and Ponce, Computer Vision: A Modern Approach (new Edition coming out in 2020)
Deep Learning, MIT Press book, Ian Goodfellow and Yoshua Bengio and Aaron Courville
COMPUTER VISION (ING-INF/03)
IMAGE PROCESSING
Degree course COMPUTER ENGINEERING
Subject area ING-INF/03
Course type Laurea Magistrale
Credits 9.0
Teaching hours Ore totali di attività frontale: 81.0
For matriculated on 2018/2019
Year taught 2018/2019
Course year 1
Semestre Secondo Semestre (dal 04/03/2019 al 04/06/2019)
Language INGLESE
Subject matter PERCORSO COMUNE (999)
Location Lecce
IMAGE PROCESSING (ING-INF/03)
IMAGE PROCESSING
Degree course COMPUTER ENGINEERING
Subject area ING-INF/03
Course type Laurea Magistrale
Credits 9.0
Teaching hours Ore totali di attività frontale: 0.0
For matriculated on 2017/2018
Year taught 2017/2018
Course year 1
Semestre Secondo Semestre (dal 01/03/2018 al 01/06/2018)
Language INGLESE
Subject matter PERCORSO COMUNE (999)
Location Lecce
The goal of image processing class is to provide the fundamentals of developing an intelligent machine vision system. The goal is to study and analyse images and videos to understand their content and derive 3D information. Problems in this field concern the identification of 3D shapes of an acquired scene, to determine how objects move, and recognize objects through the analysis of still images or a sequence of them (ie through static and / or time-varying information). The course provides an introduction to classical image processing techniques and end up to introduce the Deep Learning methodologies that are nowadays at the basis of all the disrupting innovations in several sectors: self-driving cars, security for face recognition and behaviour understanding, precision medicine and agricolture etc
at the end of the course the student will be able to:
be familiar with the theoretical and practical aspects of image processing; have acquired the basics of the image formation process and understand the relationships between the 2D and 3D world; have acquired the essential ingredients to develop a processing pipeline to locate, recognize and track objects of interest.
Having acquired the basic principles of Deep Neural Networks (Deep Learning) and transfer learning in order to build intelligent vision systems
Introduzione ai sistemi di visione artificiale (2 ore); Formazione dell’immagine (3 ore); Geometria proiettiva 2D e 3D (3 ore); Miglioramento della qualità delle immagini (2 ore); analisi delle immagini a colori (2 ore); Filtraggio nello spazio e nel dominio delle frequenze (4 ore); Piramidi Gaussiane e Laplaciane (3 ore); Local feature detector (4 ore); Allineamento (4 ore); Segmentazione (3 ore); analisi della tessitura (2 ore); analisi del movimento (4 ore); structure from motion (2 ore); multi-view geometry (2); Riconoscimento automatico (2) Deep Learning (8 ore); Tracking (2 ore).
[1] Richard Szeliski, Computer Vision: Algorithms and Applications, Springer 2010.
[2] Deep Learning, by Goodfellow, Bengio, and Courville.
[2] Dictionary of Computer Vision and Image Processing, by Fisher et al. Note: Full text is available in 'Online Resources' section.
IMAGE PROCESSING (ING-INF/03)
IMAGE PROCESSING
Degree course COMPUTER ENGINEERING
Subject area ING-INF/03
Course type Laurea Magistrale
Credits 9.0
Teaching hours Ore totali di attività frontale: 81.0
For matriculated on 2016/2017
Year taught 2016/2017
Course year 1
Semestre Secondo Semestre (dal 01/03/2017 al 02/06/2017)
Language INGLESE
Subject matter PERCORSO COMUNE (999)
Location Lecce
IMAGE PROCESSING (ING-INF/03)
IMAGE PROCESSING
Degree course COMPUTER ENGINEERING
Subject area ING-INF/03
Course type Laurea Magistrale
Credits 9.0
Teaching hours Ore totali di attività frontale: 81.0
For matriculated on 2015/2016
Year taught 2015/2016
Course year 1
Semestre Primo Semestre (dal 21/09/2015 al 18/12/2015)
Language INGLESE
Subject matter PERCORSO COMUNE (999)
Location Lecce
IMAGE PROCESSING (ING-INF/03)
IMAGE PROCESSING
Corso di laurea COMPUTER ENGINEERING
Settore Scientifico Disciplinare ING-INF/03
Tipo corso di studio Laurea Magistrale
Crediti 9.0
Ripartizione oraria Ore totali di attività frontale: 0.0
Per immatricolati nel 2014/2015
Anno accademico di erogazione 2014/2015
Anno di corso 1
Semestre Primo Semestre (dal 29/09/2014 al 13/01/2015)
Lingua
Percorso PERCORSO COMUNE (999)
Sede Lecce - Università degli Studi
IMAGE PROCESSING (ING-INF/03)
IMAGE PROCESSING
Corso di laurea COMPUTER ENGINEERING
Settore Scientifico Disciplinare ING-INF/03
Tipo corso di studio Laurea Magistrale
Crediti 9.0
Ripartizione oraria Ore totali di attività frontale: 0.0
Per immatricolati nel 2013/2014
Anno accademico di erogazione 2013/2014
Anno di corso 1
Semestre Primo Semestre (dal 30/09/2013 al 21/12/2013)
Lingua
Percorso PERCORSO COMUNE (999)
Sede Lecce - Università degli Studi