Sökning: "computer vision"
Visar resultat 1 - 5 av 557 uppsatser innehållade orden computer vision.
- Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik
Sammanfattning : Large and diverse data is crucial to train object detection systems properly andachieve satisfactory prediction performance. However, in some areas, such as ma rine science, gathering sufficient data is challenging and sometimes even infeasible.Working with limited data can result in overfitting and poor performance. LÄS MER
2. Automating Feature-Extraction for Camera Calibration Through Machine Learning and Computer VisionMaster-uppsats, Lunds universitet/Institutionen för elektro- och informationsteknik
Sammanfattning : Machine learning as a field has expanded in an explosive manner, with more companies interested in using the technology. One of these companies, Spiideo, uses Machine learning to automatically stream and record sports, highlighting key events - all automatically without a cameraman. LÄS MER
3. Detecting Slag Formation with Deep Learning Methods : An experimental study of different deep learning image segmentation modelsMaster-uppsats, Linköpings universitet/Datorseende
Sammanfattning : Image segmentation through neural networks and deep learning have, in the recent decade, become a successful tool for automated decision-making. For Luossavaara-Kiirunavaara Aktiebolag (LKAB), this means identifying the amount of slag inside a furnace through computer vision. LÄS MER
- Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)
Sammanfattning : The information era has led the manufacturer of trucks and logistics solution providers are inclined towards software as a service (SAAS) based solutions. With advancements in software technologies like artificial intelligence and deep learning, the domain of computer vision has achieved significant performance boosts that it competes with hardware based solutions. LÄS MER
5. Image-to-Image Translation for Improvement of Synthetic Thermal Infrared Training Data Using Generative Adversarial NetworksMaster-uppsats, Linköpings universitet/Datorseende
Sammanfattning : Training data is an essential ingredient within supervised learning, yet time con-suming, expensive and for some applications impossible to retrieve. Thus it isof interest to use synthetic training data. However, the domain shift of syntheticdata makes it challenging to obtain good results when used as training data fordeep learning models. LÄS MER