- Primality tests The object is to explore different methods for testing if a number is prime, among them the famous so called AKS primality test. This algorithm runs in time that is a polynomial in the length of the number being tested.
- kamerakalibrering för fotformsskattning För att skatta formen och mäta ett par fötter är det nödvändigt att ha en exakt uppskattning av kamerapositionen (inklusive skala). I detta projekt kommer vi att arbeta med krävande scener med lite struktur och potentiellt snabb rörelse. Målet är att förbättra skattningen av kameran och robustheten att uppfylla kraven i industrin. Detta kan göras genom att använda mer sensordata och / eller använda information om scenen som kommer att gälla för vår inställning.
- Single View Foot Estimation This project will focus on estimating the shape of the foot given a single image. From a top view perspective, can we accurately estimate the width and length of the foot? What can be inferred about height etc using machine learning techniques? In this exciting project we are going investigate what can be said about a foot from just a single image. The project will involve both statistics, optimization and computer vision.
- Multiple View End-To-End Learning Based Shoe Recommendation Can modern machine learning techniques and computer vision be used to infer shoe fit from just a few images? Volumental dispose a large set of 3D scanned feet and customer data of bought shoes. Can we formulate this as a ML-problem and directly use imagery to find a perfect shoe fit? The goal of this project is to develop a neural network that can use multiple images as input and infer the correct shape parameters of a parametric foot model.
- Bildigenkänning som verktyg för att upptäcka föroreningar Testa om enkla övervakningskameror tillsammans med biligenkänningsteknik kan detektera och varna vid allvarliga föroreningar i dagvattensystemet.
- Predictions of the next solar cycle maximum TBA
- Multiscale modeling for materials science TBA
- Surface growth models TBA
- Maskininlärning för att tolka 11 453 sidor från Älvsborgs lösen Recent breakthroughs in image analysis and text recognition, fuelled by a rapid progress in machine learning, means that software for the first time can compete with, and sometimes even surpass, humans in tasks such as classifying images and reading handwritten documents. In this project you will develop method for interpreting handwritten documents from the 16th century and evaluate how they work on 11 453 pages from Älvsborgs Ransom. The text is written by several scribes. The consistency of style and layout within the pages of one scribe is high, but there is significant differences between scribes. This is an excellent example of a machine learning problem, where the use of context (the scribe), could be used. This is a research area which is of theoretical interest, with applications in many areas. In the project one could also study how to extract the structure of the documents automatically using convolutional neural networks.
- AI for Precision farming This master's thesis project concerns the development of methods to model and predict how harvest yield depends on data. In the project we will evaluate potential methods and algorithms to build a predicative model, that can be used for optimization of agriculture inputs and planning. In this project we work with Hushållningssällskapet in Skåne and with T-Kartor. We have harvest data (one point every fifth seconds) that can be used as evaluation data. We have georeferenced spatiotemporal and spectral data that can be used as potential indata both as points and imagery, but also data from soil samples, satellite sensor data, terrain model, rainfall etc. Data will be available from selected fields in Skåne from 2020.
- Recover scale in Structure from Motion using IMU data In structure from motion the goal is to recover both the scene structure and the movement of the camera. Traditionally this has been done using imagery only with the well known scale-ambiguity. In practical applications, the scale is of great importance in order to actually measure stuff. A modern smart-phone have more sensors than just the camera, for example an accelerometer and a gyroscope. This gives extra information about the distance traveled between the cameras and how the camera was oriented. This project aims to combine image information with IMU data in order to find both the scale and improve the camera trajectory.
- Master Thesis work with focus on image processing with an interest in object recognition and a passion for the environment We’re looking for one or two master degree students, with an interest in exploring the capacity of an algorithm in an applied environment, able to segment one or multiple objects. When waste objects are distributed on the sorting robot’s conveyor belt, they can end up on top of each other. By identifying each object, both gripping the objects and sorting can be optimized. The task for this thesis work is to successfully segment objects of various shapes that can only partially been seen by the camera.
- The cocktail party problem Natural listening situations that require listeners to selectively attend to a talker of interest in noisy environments with multiple competing talkers are among the most challenging situations encountered by hearing impaired listeners. The goal of this project is to study the effects of hearing impairment on auditory attention which is needed in order to further advance hearing aids. Data will be provided by Eriksholm Research Centre where participants were instructed to attend to one of two simultaneous talkers in the foreground mixed with multi-talker babble noise in the background.
- Respiratory and pulse monitoring The goal of this project is to achieve respiratory and/or pulse monitoring using novel radar technology. The project will be done at Acconeer using their 60 GHz pulsed coherent radar which originates from research at LTH. Prerequisites: FMSF10/MASC04, (FMSN45/MASM17, FMSN35/MASM26)