Open Topics

Machine Learning:

Data Visualization / Human Computer Interaction

HDI - Human Data Interaction:

Image Processing:

Machine Learning:

Sportsanalytics for Rowing data

Within the AIROW (artificial intelligence in rowing) project (https://airow.univie.ac.at/) we are collaborating with sports scientists, the Austrian Rowing Federation, and other Data Scientists to collect, curate, and analyse data from top rowers on the training process, recovery, wellbeing and health.

Some of the main questions of the project is to better understand optimal training regimes as well as the impact of training, well-being, and other factors on the athlete's performance in order to optimize training load management. Within this project, we have a number of student projects that aim at: 

  • understanding the connection between external load (like physical power output or the training impulse, TRIMP) and internal load (i.e., the physiological response) as measured through athlete-provided subjective scores (i.e., the Rate of Perceived Exertion; RPE).
  • the relationship between external load (in Watt) and heart rate characteristics for low-intensity endurance workouts.
  • Identification of high intensity intervals within training sessions based on the analysis of performance curves, heart rate, or other metrics such as stroke frequency.

In order to participate, you need to have passed (and enjoyed) the FDA course (Foundations of Data Analysis). The supervision will be done by various members of the project team. For more information, please contact Torsten M.

Prerequisites: FDA

Contact: Torsten Möller, Christoph Thiem

 

 

Predicting solar thermal heat production

With heat accounting for 50% of global energy demand, solar-thermal plants could play a crucial role in the transition to renewable energy. One of the most important questions for operators is whether the current heat production matches expectations. However, predicting the expected yield is challenging due to the constantly changing weather and demand, which significantly influence heat production. Additionally, seasonal changes in operation add to the complexity.


Do you want to contribute to a greener future?
Leverage your knowledge of machine learning or physically-motivated algorithms to improve the current yield prediction. Join us in building on the successful collaboration between the University of Vienna and SOLID. Specifically, you would be embedded into the company through a (paid) internship.

Contacts: Lukas Feierl, Sebastian Tschiatschek, Torsten Möller

Fault Detection for solar thermal

With heat accounting for 50% of global energy demand, solar-thermal plants could play a crucial role in the transition to renewable energy. Effective monitoring is essential to quickly identify issues and maintain optimal performance. However, existing fault detection algorithms lack precision. The constantly changing operating conditions (e.g., weather, demand) and time-dependent factors (e.g., fluid heat-up) complicate fault detection for both experts and algorithms.


Do you want to contribute to a greener future?
Leverage your knowledge of machine learning or physically-motivated algorithms to improve fault detection. Join us in building on the successful collaboration between the University of Vienna and SOLID. Specifically, you would be embedded into the company through a (paid) internship.

Contacts: Lukas Feierl, Sebastian Tschiatschek, Torsten Möller

Density deconvolution on Gaia data: estimating accurate distances to millions of stars

Project Overview:

This project aims to tackle the challenge of identifying stellar clusters using data from the Gaia satellite, focusing on the application of density-based clustering methods. A significant limitation in this task arises from noise-corrupted samples, which obscure critical details and lead to less accurate clustering outcomes. The primary goal of this thesis will be to implement and refine a density deconvolution approach to recover the noise-free underlying distribution function common to all samples, enhancing the accuracy and reliability of stellar cluster identification.

 

Background:

Density estimation is a fundamental statistical task involving the estimation of a distribution's density from a finite set of measurements. In many scientific fields, researchers are often limited to working with noise-corrupted measurements. When the statistics of the noise are known, density deconvolution methods can be employed to approximate the density function of the unobserved, noise-free samples, as opposed to the noisy measurements themselves. The Gaia satellite's measurements present a scenario of additive noise, where the observed samples are the result of adding independent noise to unobserved (hidden) values.

 

Aims and Expected Outcomes:

To understand and implement existing deconvolution approaches on Gaia data. The successful completion of this project is expected to yield a comparison of existing deconvolution methods and optimally an extension of an existing methodology tailored to astronomical data. This work could significantly enhance the precision of astrophysical research and potentially lead to new discoveries in the field.

 

Suitable for Candidates Who:

- Have an interest in applied data science and a solid understanding of statistical and machine learning concepts.

- Possess strong programming skills in Python.

Contact: Sebastian Ratzenböck, Torsten Möller

Scalable Sampling with Lattices

The challenge is easy to describe:

Come up with an algorithm that takes as input N (the number of samples) and D (the dimension) and output a scale factor s and a rotation angle alpha (D-1 angles) that would fit exactly N samples of a Cartesian lattice in the unit box [0,1]^D.

Prerequisites: Math

Contact: Torsten Möller

Interpreting Deep Clustering Results

Deep embedded clustering also called deep clustering is a growing field that combines ideas from clustering and deep learning. The integration of these techniques makes it possible to learn features automatically from the data to increase clustering performance. Current deep clustering methods are hard to interpret,  making it difficult to understand how a clustering result was reached. 

The goal of this project is to develop an interactive visualization tool, e.g. a web based application, for exploring the predictions of deep clustering algorithms and helping to understand their decision making process. 
The student is expected to do a literature review of existing visualization techniques developed for (supervised) deep learning, e.g. feature visualizations, that could be applicable to interpreting unsupervised deep clustering algorithms. The identified methods should then be applied (if necessary adapted) to and compared for existing deep clustering algorithms.

Some research questions of interest that should be considered during the project would be:  How suitable are existing visualization techniques to interpret deep clustering results? How do the different parts of the multi-objective loss of deep clustering techniques relate to each other? Considering multiple clustering models, e.g. K-Means vs DBSCAN, how do the neural network visualizations differ for each of them?

Students working on this project need basic background knowledge in machine learning (e.g. Foundations of Data Analysis), visualisation (e.g. Visualisation and Visual Data Analysis), solid programming skills in Python, and desirably some background with PyTorch, deep learning and some visualization framework, like d3.

Prerequisites: VIS, FDA

Contact: Aleksandar Doknic, Torsten Möller, in collaboration with Claudia Plant & Lukas Miklautz

Visually exploring neural networks

We have a collection of 100,000 different neural networks from the Tensorflow Playground . The core goal of this project is to create a visual interface to understand some of the basic properties of neural networks. Enabling a user to explore should help answer questions like the relationship of number of neurons and number of hidden layers, the impact of batch size, activation functions and other parameters on the quality of the network. Your tasks include:

  • fast prototyping with Tableau
  • getting familiar with the data set
  • querying neural network users on what parameters they want to explore (requirement analysis)
  • development of low-fi and high-fi prototypes

 

Prerequisites: VIS, FDA

Contact: Torsten Möller

Visually Analyzing the Fault Tolerance of Deep Neural Networks

The main objective is to design and implement a good and efficient way of visually investigating the resilience of deep neural networks against silent data corruption (bit flips) based on given empirical measurements. There are many possible causes for such faults (e.g., cosmic radiation, increasing density in chips, lower voltage which implies lower signal charge, etc.), and their "incidence" is expected to increase with current trends in chip architecture.

Starting point for the project is a given example data set which contains information about the relationship between single bit flips across various locations of a certain neural network (which layer, which neuron, which weight, which position within the floating-point representation of a real number, etc.) and the resulting accuracy of the network.

The task is to develop a tool which supports answering various questions about the influence of a bit flip on the resulting accuracy.

Examples for interesting questions are the following:

  • (empirical) distribution of the influence of a bit flip on the resulting accuracy over the positions in the floating-point representation
  • (empirical) distribution of the influence of a bit flip on the resulting accuracy over the layers in the network architecture
  • (empirical) distribution of the influence of a bit flip on the resulting accuracy over the weights in a given layer in the network architecture

In order to answer these questions, an iterative design process is required to

  • start with a requirement analysis (task & data analysis)
  • low-fi prototypes
  • high-fi prototypes
  • refinement
  • constant evaluation of the visual analysis tool.

The data set, the problem setting and the details of the requirements are provided by Prof. Gansterer, the supervision in visual analysis aspects is provided by Prof. Möller.

Prerequisites: VIS, FDA

Contact: W. Gansterer | Torsten Möller

Library for visualization of slices

This is primarily a programming project. Slicing methods are a novel way of visualizing multi-dimensional data. However, there is no publicly-available library for R or Python that makes it easy to use these visualization techniques. The goal of these projects is to develop such a library. Students should have knowledge of Javascript and either R or Python.

 

Prerequisites: VIS
Programming languages: Javascript and (R or Python)

Contact:Torsten Möller|Thomas Torsney-Weir

Visualization for optimization problems

Can visualization beat traditional (offline) optimization problems? The goal of this project is to see how well visually guided optimization can compete with traditional optimization algorithms. Students will develop a visualization system to find optimum configurations of black box (i.e. unknown) algorithms from a contest.

Prerequisites: VIS, Mathematical Modeling
Programming languages: Javascript, (R or Python), and C++

Contact: Torsten Möller|Thomas Torsney-Weir

Understanding anomalies in high-dimensional spaces

The goal of this project is to visually explain how datasets with varying number of features (or dimensions) per data point behave differently. We understand that high-dimensional spaces behave differently than low-dimensional spaces, but it is difficult to develop an intuition of what these differences are and how they affect datasets.

Visualization could help us intuitively understand what happens to the distances, local geometry, projections, etc. when data points have 3, 10 or 50 dimensions. In other words: Show how the dimensionality of the data affects mathematical operations (dist(X,Y), PCA(X), etc.).

1. Create synthetic datasets to play around with

2. Validate with real datasets with different numbers of dimensions

3. Validate your approach with a user study

Optional: How does data dimensionality affect ML models?

Prerequisites: FDA, optional: VIS

Contact: Aleksandar Doknic, Torsten Möller 

 

Data Visualization/Human Computer Interaction:

iTuner: Touch interfaces for high-D visualization

 

In order to understand simulations, machine learning algorithms, and geometric objects we need to interact with them. This is difficult to perform with something like a mouse which only has 2 axes of movement. Multitouch interfaces let us develop novel interactions for multi-dimensional data. The goals of this project are:

  • Develop a touch-screen interface for navigating high-dimensional spaces.
  • User interface designed for a tablet (ipad) to be used in concert with a larger screen such as a monitor or television.

Prerequisites: VIS, HCI

Contact: Torsten Möller|Thomas Torsney-Weir

data entering made fun

No one likes to enter data, not even the medical staff at a doctors visit. But, correctly entering a number of relevant patient information is crucial for a proper treatment, but to also make the data relevant for research and post-hoc analysis. In this project we are developing ideas to incentivise clinical staff at various departments at the largest hospital of Austria (AKH) to properly enter patient data into appropriate fields of the data register. We are pursueing this goal by:

  • an interview study to better understand the different needs and hurdles for proper data entry at different deparments
  • an automated reporting tool / dashboard that makes visible the amount of work that different departments put into entering patient data over the past fixed timeframe (like the past week).
  • gamification approaches to incentice departments to not be last on a list of entry compliance.


This project is part of a new collaboration with the cardiac and rediology units of the AKH in Vienna. Part of your work is spent at the AKH.

Pre-requisites: HCI, VIS
Contact: Torsten Möller, Laura Koesten

Exploring Sensemaking in Interactive Maps

This project investigates how map readers interact with features such as adjustable layers and guided narratives in interactive crisis maps. It aims to analyze how these elements influence sensemaking activities or friction points during map exploration. The project will identify unique sensemaking activities and propose potential modifications to existing frameworks, which were originally based on static maps, to better reflect the engagement with interactive visualizations. A small-scale interview study will be conducted to gather insights on user interactions and preferences.

Key challenges include:

  • Designing or selecting interactive maps that integrate features like guided narratives or adjustable layers.

  • Conducting a user study and analyzing interview responses.

  • Identifying unique sensemaking activities and integrating them into an existing framework.

Background literature: Sensemaking Frameworks, such as

Prerequisites: VIS, FDA

Programming languages: Python or R

Contact: Laura Koesten

Objective versus subjective measures of interactions with data visualization

This project investigates the differences between objective and subjective evaluations of data visualizations, focusing on understanding and perceived understanding. You will conduct a study in which participants view a series of data visualizations and complete different tasks, including ones that measure their understanding. Participants will also provide feedback on perceived complexity, alongside other dimensions that describe their data visualization perception. You will also collect demographic information (age, sex) and experience factors (familiarity with data visualization, political leanings).

Key challenges include:

  • Comparing objective vs. subjective evaluations of complexity and understanding.

  • Designing an experimental set-up and conducting a study.

  • Analyzing how demographics and experience influence evaluations.

Prerequisites: VIS, FDA

Programming languages: Python or R

Contact: Laura Koesten, Torsten Möller

Visualization of team communication: focus on communication flows

Effective communication is crucial in team-based environments, especially in high-pressure situations such as emergency response or collaborative decision-making. This project aims to develop a module to extend an existing tool that can capture and visualize team communication patterns, helping users reflect on their interactions.

Key challenges include: 

  • Providing visual representations of communication flows / multi-party discourses and emotional patterns over time.

  • Allowing users to reflect on their communication style through interactive visualizations.

Background literature:

El-Assady, Mennatallah, et al. "ConToVi: Multi-party conversation exploration using topic-space views." Computer Graphics Forum. Vol. 35. No. 3. 2016.

Prerequisites: HCI, VIS, FDA

Programming languages: Python or JavaScript/Svelte

Contact: Laura Koesten

Data analysis of climate visualization perceptions

How do people perceive and interpret climate change visualizations? This project explores how individuals perceive and interpret data visualizations on climate change, based on data collected through an existing online survey. In this survey, participants provided insights into their experiences with visualizations, including reading accuracy, understandability, aesthetics, suggested improvements, or perceived take-away messages. You will develop your own research questions and explore the dataset to uncover patterns and relationships.

Key challenges:

  • Conducting exploratory data analysis of an existing survey dataset.

  • Identifying themes and key questions from survey data.

  • Performing quantitative analysis, including descriptive and inferential statistics.

  • Analyzing free-text responses (thematic analysis).

Prerequisites: FDA, VIS

Programming languages: Python or R

Contact: Regina Schuster, Laura Kösten

Adaptive proximity levels of data visualizations

This project will improve an existing prototype that was developed to improve chart visualizations using proximity-based techniques. Proximity techniques in data visualization include design decisions that use spatial, temporal, or contextual proximity to enhance user experience—for example, zooming into relevant map areas, highlighting recent data points, or using affective color to draw attention—creating a stronger connection between the user and the information. Currently, it includes basic functionalities, like selection options for different designs and the choice between two chart types (map and line chart). 

Key challenges:

  • Enhancing interactivity with advanced proximity-based techniques.

  • Balancing usability and complexity to maintain a clear, intuitive interface.

  • Evaluating effectiveness through user studies to assess improvements in user experience. 

Prerequisites: HCI, VIS

Programming languages: Python or R

Contact: Laura Koesten, Torsten Möller

Evaluating visualizations with different audiences

Understanding how different audiences interpret visualizations is challenging. This project will design a user-friendly feedback tool to help researchers and journalists collect insights on the understandability, trustworthiness, and user experience of their visualizations.

Using a user-centered design approach, students will create low- and high-fidelity prototypes of an interactive feedback system. Key challenges include designing intuitive UI and interaction flows, addressing diverse audience needs, and exploring how feedback can be effectively visualized for creators. This project will build on an initial requirement analysis.

Key challenges include:

  • Designing intuitive UI and interaction flows for easy feedback collection.

  • Ensuring the tool meets the needs of both creators and audiences.

  • Exploring how feedback can be effectively visualized for creators.

  • Conducting user testing to refine the design.

Background literature:

  • User-centered design principles in HCI

Prerequisites: HCI, VIS

Programming languages: Python or R, JavaScript

Contact: Laura Koesten, Christian Knoll

Building Interfaces for the Explanation of Public AI Systems

Overview

This project explores how interactive interfaces can help users understand public machine learning systems in public institutions. It builds on research into the explanation needs of different audiences and implements an approach developed in a recent study. The focus is on both technical implementation and empirical validation (qualitative and/or quantitative) in explainable AI (XAI) and HCI. A research paper could in principle be prepared and submitted.

Background

Public institutions increasingly use AI systems in decision-making, such as welfare fraud detection, student grading, and employment prediction. Understanding these systems is crucial for users, regulators, developers, and those affected. However, many systems lack transparency, making it difficult to trace and verify their decisions. Further, people should be able to understand how these systems are used and for which purpose, which is not covered by traditional explanation techniques (LIME, SHAP, etc.). The project is an implementation of an explanation approach covering this information.

Aims and Expected Outcomes

The project aims to develop a high-fidelity prototype or functional web interface to explain an AI system to various audiences. This involves requirement analysis, prototyping, validation through user studies, and a write-up (report, thesis, etc.). The process is flexible and guided by prior research, making it ideal for students interested in current research topics.

Suitable Candidates

Candidates should have an interest in AI explainability and user studies using HCI methods. Knowledge in web or interface development is advisable for the implementation. An interdisciplinary background in humanities or social sciences is also welcome.

Contact: Timothée Schmude, Torsten Möller

 

Visualizing Multiple Data Distributions Side by Side

Boxplots are often used to compare the distribution of multiple datasets. However, research1 has shown that boxplots are often misinterpreted even by experts and are, therefore, probably not the best type of visualization for the task. Other visualizations, such as violin plots2 or bean plots3, have been developed, but the question of which one to use is, while discussed in the community4, still open.

In this project, you will

  • conduct literature research on visualizations used to compare data distributions and create a comprehensive overview of currently available visualizations
  • brainstorm adaptations and/or new visualizations to compare data distributions
  • design and conduct a study to test different (existing and newly created) types of visualizations for comparing data distributions.

Literature:

(1) Lem, S.; Onghena, P.; Verschaffel, L.; Van Dooren, W. Experts’ Misinterpretation of Box Plots – a Dual Processing Approach. Psychologica Belgica 2014, 54 (4), 395–405.
https://doi.org/10.5334/pb.az

(2) Hintze, J. L.; Nelson, R. D. Violin Plots: A Box Plot-Density Trace Synergism. The American Statistician 1998, 52 , 181–184. 
https://www.stat.cmu.edu/~rnugent/PCMI2016/papers/ViolinPlots.pdf

(3) Kampstra, P. Beanplot: A Boxplot Alternative for Visual Comparison of Distributions. J. Stat. Soft. 2008, 28 (Code Snippet 1).
https://doi.org/10.18637/jss.v028.c01

(4) Desbarats, N. I’ve Stopped Using Box Plots. Should You? Nightingale – Journal of the Data Visualization Society 2021.
https://nightingaledvs.com/ive-stopped-using-box-plots-should-you/

 

Prerequisites: VIS

Contact: Laura Lotteraner, Torsten Möller

Dashboard for monitoring solar heat production

Imagine transforming the way solar-thermal plants are monitored. This project throws you headfirst into the challenge of building a user-centred, real-time monitoring dashboard. Current systems bury operators in data, hindering their ability to make critical decisions on the fly. You'll leverage human-computer interaction (HCI) principles and visual analysis methods (VIS) to tackle this head-on. It's time to turn overwhelming sensor data, plant layouts, and performance metrics into a clear, intuitive interface. Partnering with the company SOLID, you'll gain firsthand insights through in-depth interviews with plant engineers and operators. Develop and test a prototype dashboard through user studies, validating its ability to streamline plant operations and unlock peak efficiency. We will build on a previous successful collaboration between the University of Vienna and SOLID. Specifically, you would be embedded into the company through a (paid) internship.

Contact: Torsten Möller

Mistrust in data (visualizations)

Political and societal trust are declining, fueled by polarization and disinformation. Media studies often assume baseline trust in information sources, neglecting widespread distrust in the press. Visual representations of data are not exempt from such dynamics, and the number of decisions around data and visual design additionally interact with feelings of trust or mistrust. In this project, we want to explore ways to create intentional distrust in visualizations to facilitate critical thinking. This will be informed by the literature on uncertainty visualizations but go beyond that to create several prototypes that create distrust, compare those in an experimental study, and evaluate levels of mistrust amongst participants. 

Background literature:
Ge, L.W., Cui, Y. and Kay, M., 2023, April. CALVI: Critical Thinking Assessment for Literacy in Visualizations. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-18). https://dl.acm.org/doi/abs/10.1145/3544548.3581406

Pandey, S., McKinley, O.G., Crouser, R.J. and Ottley, A., 2023, October. Do You Trust What You See? Toward A Multidimensional Measure of Trust in Visualization. In 2023 IEEE Visualization and Visual Analytics (VIS) (pp. 26-30). IEEE. https://ieeexplore.ieee.org/abstract/document/10360907

Prerequisites: Vis, HCI
Contact: Laura Koesten, Torsten Möller
 

 

Visualizing results from a forced choice experiment

In forced-choice experiments, a participant is presented with several alternatives in a study in which stimuli are presented. The participant is forced to choose one stimulus over another (or over multiple) and cannot provide a neutral/custom response. 
This project will investigate how to visualize results from such experiments by exploring existing options and developing new or adapted visualization prototypes, which will be evaluated in a design study with users.

Prerequisites: Vis
Contact: Torsten Möller, Laura Koesten

 

HDI - Human Data Interaction

Multi-level emotional measurement

This project focuses on improving the Self-Assessment Manikin (SAM) by integrating nuanced methods to capture emotional dimensions such as dominance, arousal, and valence. We aim to design a new representation of an emotion scale using:

  • Glyph-based representations: Exploring visual ways to represent complex emotional combinations.
  • Haptic feedback: Incorporating force-feedback to enable tactile interaction and support emotional expression.
  • Potentially: Physiological signals / Biometrics like heart rate or skin conductance 

You will explore decision trees as a method to guide users through emotional assessment, emphasizing intuitive, explainable AI-supported systems.  The goal is to measure subjective reactions and use these as inputs to adapt system interactions.

Key challenges include: 

  • Designing representations for complex emotional blends; addressing difficulties in defining and articulating emotions; 
  • Developing multimodal metrics to enhance user interaction and system adaptability. 

Background literature:

Margaret M Bradley and Peter J Lang. Measuring emotion: the self-assessment manikin and the semantic differential. Journal of behavior therapy and experimental psychiatry, 25(1):49–59, 1994.

Contact: Laura Koesten

Explore novel interaction techniques - Chatbots for Data Analysis

Chatbots are becoming more prevalent and are actively used by many companies. They offer a voice or text interface to interact with a computer. An example of a chatbot is amazon’s alexa, which can tell the time when asked.
The goal of this project is to find new possible ways to interact with an exploratory data analysis tool. Developing new interaction techniques would allow the user to explore and understand the data in a new fashion. For example, it could be possible to have a chat window next to a scatterplot that enables the user to enter queries such as: ‘show me the average’, which would then be reflected in the scatterplot.

  • Learn about natural language processing
  • Understand and compare interaction techniques
  • Develop a ‘conversation’ with a data analysis tool

Prerequisites: VIS, HCI

Contact: Torsten Möller

Explaining descriptive statistics

With Anscombe's Quartet [1] it was demonstrated quite figuratively that summary statistics can be very misleading or, at least, hard to interpret. Just recently, this example has become quite playful with the Dinozaur Dozen [2]. However, there are a number of statistical measures, that don't have an easy (visual) explanation. One of them is Krippendorf's alpha [3], a very common measure in the social science for measuring the agreement between subjective coders (as in labeling text or documents). The challenge of this project will be to:

  • understand the measure
  • develop simple alternatives
  • develop different visual representations that "bring this measure to life", i.e. make it easy(er) to understand

pre-req: Vis

[1] Anscombe, F. J. (1973). "Graphs in Statistical Analysis". American Statistician. 27 (1): 17–21. doi:10.1080/00031305.1973.10478966. JSTOR 2682899, see also en.wikipedia.org/wiki/Anscombe%27s_quartet

[2] Justin Matejka, George Fitzmaurice (2017), "Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing," ACM SIGCHI Conference on Human Factors in Computing Systems. see also www.autodesk.com/research/publications/same-stats-different-graphs

[3] Krippendorff, Klaus (1970). Estimating the reliability, systematic error, and random error of interval data. Educational and Psychological Measurement, 30 (1), 61–70. see also en.wikipedia.org/wiki/Krippendorff%27s_alpha

Image Processing:

Usability Evaluation of Open Source Volume Analysis Software

open_iA enables users to perform general and specialized visual analysis and processing of volumetric datasets (such as from a computed tomography device). Since it has been developed mainly as a basis for research prototypes, the user interface so far was not developed with usability as first concern.

The goals of this project are:

  • To evaluate the usability of its general capabilities, and optionally of its advanced visual analysis tools. This could for example happen through usability interviews, or user studies comparing it to other (open source and commercially available) solutions.
  • To find innovative ways of overcoming the problems found in the evaluation.
  • Depending on time and interest, to implement some or all of these improvements.

Prerequisites: finished the Signal and Image Processing & the Human Computer Interaction class

Contact: Bernhard Fröhler, Torsten Möller

Smart Image Filter Preview

The analysis of large images (2D or 3D), requires applying filters like smoothing or denoising. Finding the most suitable parameters for a given analysis task through a trial-and-error approach can be  time-consuming. The goal of this project is to develop a tool for a smart preview over the possible outcome of some image processing filters for different parameters for a small region of the image; the outcome of different parameterizations could for example be presented in a matrix; the tool should also be evaluated regarding usability.

Prerequisites: SIP, HCI

Programming languages: Python, C++


Contact: Bernhard Fröhler | Torsten Möller