Tesi Lab Unveiled: A Comprehensive Guide to Mastering the Academic Thesis Journey


Embarking on the thesis journey is a pivotal milestone in any student's academic career. Understanding the intricacies of the process, from submitting a request to defending your work, is crucial for success. In this comprehensive guide, we will delve into the Tesi Lab, exploring the steps involved in requesting a thesis, potential challenges, and tips for a successful defense. Whether you are a seasoned researcher or a novice, this guide aims to demystify the thesis process and equip you with the Wisdom needed for a seamless experience.

Table of Contents:

How to Submit a Thesis Request
Understanding the Procedure
Timelines and Deadlines
Required Documentation
Approval Process

Dealing with Rejection: What If Your Thesis Request is Not Approved?

Modifying Your Thesis Request
Communicating with the Thesis Committee
Addressing Common Rejection Issues
Accessing the Thesis Defense Session
Meeting Academic Requirements
Clearing Financial Obligations
Approval from Advisors
Submission Deadline for the Thesis
Communication of the Defense Date
Administrative Checks and Balances
Scheduling the Final Defense
Notification Process
Understanding the Essence of a Thesis
Defining a Thesis
Types of Theses: Compilative vs. Experimental
Tailoring Your Approach to the Audience
Balancing Technicality and Accessibility
Choosing the Right Writing Software: A Focus on LaTeX
Advantages of LaTeX for Thesis Writing
Overleaf: A Convenient Platform
Tips for Effortless Collaboration and Backups
Troubleshooting Potential Issues
Structuring Your Thesis: A Step-by-Step Guide
Frontispiece and Dedication
Creating a Well-Organized Table of Contents
Incorporating Figures and Tables
Crafting a Captivating Introduction
Conducting a Literature Review: The "State of the Art"
Materials and Methods: Detailing Your Experiment
Presenting Results Effectively
Engaging in a Thoughtful Discussion
Concluding Your Thesis
Building a Comprehensive Bibliography
Appendices and Acknowledgments

Frequently Asked Questions (FAQ)

Q1: What is the recommended timeline for submitting a thesis request?
A1: Ideally, candidates should submit their thesis requests at least two months before the scheduled defense session.
Q2: Can I modify my thesis request after submission?
A2: Yes, modifications are possible, especially in cases where the request faces initial rejection. Candidates can submit a modification request to address necessary changes.
Q3: Is it necessary to have completed all exams before defending the thesis?
A3: Yes, candidates must have concluded all required exams before being eligible for thesis defense.
Q4: Can I use software other than LaTeX for thesis writing?
A4: While LaTeX is recommended, candidates can choose other writing software. However, using Overleaf for collaborative writing and cloud-based backup is highly advised.
Q5: How long should the introduction of a thesis be?
A5: The introduction should be concise, typically around one page, and focus on presenting the general problem and the specific issue addressed in the thesis.
Q6: Can I submit my thesis in a language other than Italian?
A6: Depending on the guidelines of your Establishment you may be allowed to submit your thesis in a language other than Italian. It's crucial to confirm this with your academic advisor.
In this guide, we'll address these questions and more, providing a comprehensive resource for navigating the Tesi Lab journey.


Body pose estimation is a critical aspect of computer vision, specifically the ability to detect and track human joints while a person is in motion. This skill is essential for various applications, including action recognition, healthcare, and long-term people reidentification. While there are numerous algorithms for single-body pose estimation, leveraging multiple cameras can significantly enhance the final body-pose quality. This proposal focuses on the design and implementation of a multi-view approach for real-time multi-people body pose estimation.
Project Overview: Final Project (ED3D)
Test different single-view algorithms.
Thesis Goals:
Study and compare different multi-view approaches.
Combine these approaches to achieve superior results.
Contributor: Marco Carraro
OpenPTrack: Enhancing People Detection and Tracking Algorithm
OpenPTrack stands as an open-source people detection and tracking algorithm using RGB-D data. The algorithm detects people by classifying different clusters of points after an initial ground plane removal phase. The objective of this project is to improve people detection under various conditions, such as people being close to other objects or in unconventional poses.
Project Tasks: Final Project (ED3D)
Make OpenPTrack compatible with Ubuntu 16.04.
Write an installation guide for online publication.
Compare different people detection algorithms at the state of the art.
Thesis Objectives:
Fuse different approaches or create a new, more robust approach for people detection.
Contributors: Marco Carraro, Yongheng Zhao
Deep Learning for Re-identification: Soft Biometrics and Dataset Challenges
The focus here is on re-identification, recognizing the same person observed in different instances or locations. The approach involves using soft biometric features, a set or cascade of features providing a detailed description of a person. This project involves developing new body models for managing soft biometric data and applying these features to reidentification using deep learning techniques.
Thesis Goals:
Apply deep learning techniques to reidentification.
Create new features for extraction.
Emphasize the critical role of datasets in this process.
Contributor: Stefano Ghidoni
Detecting People Lying on the Floor: Algorithm Optimization and Real-world Testing
The algorithm for detecting people lying on the floor requires improvements to find a suitable trade-off between runtimes and detection rates. The project involves identifying bottlenecks, testing different parameter combinations to enhance the pipeline's speed, and validating the algorithm on a dataset acquired in a real apartment. New fast features, calculated on RGB or infrared images, will be exploited to reduce the number of false positives.
Tesina and Tesi Objectives:
Identify bottlenecks and test different parameter combinations for the algorithm.
Test the algorithm on a real apartment dataset.
Utilize new fast features to reduce false positives.
Contributor: Morris Antonello
Architectural Restoration: Blending Tradition with Modern Techniques
In this section, we shift focus to architectural restoration, exploring theoretical aspects, operational practices, methodologies, and current trends in architectural recovery. Critical analysis of exemplary cases is presented, covering the origin, motivations, and trends in architectural recovery. The section also delves into the theoretical and methodological aspects of architectural recovery, emphasizing the significance of techniques, types, and structures in architectural design.
Section Overview:
Theoretical aspects of architectural recovery.
Critical analysis of exemplary cases.
The origin

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