Abb. 1: Signalverarbeitung mit MATLAB^® und Künstlicher Intelligenz

Autor:	Ajay Paul
Art:	bachelor thesis
Starttermin:	TBD
Abgabetermin:	TBD
Betreuer:	Prof. Dr.-Ing. Schneider
Zweitprüfer:	Mirek Göbel

Introduction

Signal processing has long been a basis in the analysis and manipulation of data in various domains such as telecommunications, radar, audio processing, and imaging. Signal processing has traditionally focused on analyzing, filtering, and interpreting signals in both the time and frequency domains. With the integration of AI, especially deep learning, the possibilities expand to include noise reduction and more. It is essential in many modern applications such as communications, audio and image processing, biomedical signal analysis, and control systems.

Task list

Solve the Image Processing Tasks with classic algorithms.
Research on AI Solutions.
Evaluation of the solutions using a morphological box (Zwicky box)
Solve the Image Processing Tasks with AI algorithms.
Compare the different approaches according to scientific criteria
Scientific documentation

Getting startes

You are invited to the image processing class in MATLAB Grader.
Choose the examples from the categories in table 1. Start with 1 end with 6.
Solve the tasks with classic algorithms.
Solve the tasks with AI.
Save your results in SVN.
Use MATLAB R2025b for the algorithms.

Image Processing Tasks with classic algorithms

Image Processing: Recovery and restoration of information

Table 1: Image Processing Categories
#	Categorie	Grader Lecture
1	Recovery and restoration of information	5
2	Image enhancement	6
3	Noise Reduction	7
4	Data based segmentation	8
5	Model based segmentation	9
6	Classification	10

SVN Repository:

https://svn.hshl.de/svn/MATLAB_Vorkurs/trunk/Signalverarbeitung_mit_Kuenstlicher_Intelligenz

Analysis of AI-Driven Image Classification: Methodologies, Architectures, and Implementation

Computer vision has changed a lot in the last twenty years. Before, it mostly used hand-made signal processing methods, but now it is mainly based on data-driven artificial intelligence. For a bachelor thesis that combines engineering and computer science, this change creates many methods to choose from, but it can also be confusing. The move from Shallow Learning, where features are manually designed and classical classifiers are used, to Deep Learning, where the system learns features automatically from raw data, is the main story of modern image processing.

In academic research, choosing the right tools is very important. MATLAB is a high-level technical computing language that is widely used for this kind of work. Compared to other frameworks that need a lot of setup code, MATLAB provides an integrated environment for handling data, developing algorithms, and using hardware acceleration.

This report gives a detailed technical analysis of image classification methods. It explains the theory behind both classical and modern AI methods, compares their performance, and uses system engineering tools, such as the Zwicky Box, to organize and support the model selection process.