Sökning: "Visuell inspektion"
Visar resultat 6 - 10 av 35 uppsatser innehållade orden Visuell inspektion.
6. An empirical comparison of generative capabilities of GAN vs VAE
Kandidat-uppsats, KTH/DatavetenskapSammanfattning : Generative models are a family of machine learning algorithms that are aspire to enable computers to understand the real world. Their capability to understand the underlying distribution of data enables them to generate synthetic data from the data they are trained on. LÄS MER
7. Exploring Diversity of Spectral Data in Cloud Detection with Machine Learning Methods : Contribution of Near Infrared band in improving cloud detection in winter images
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Cloud detection on satellite imagery is an essential pre-processing step for several remote sensing applications. In general, machine learning based methods for cloud detection perform well, especially the ones based on deep learning as they consider both spatial and spectral features of the input image. LÄS MER
8. Automating a robot cell welding process
Kandidat-uppsats, Lunds universitet/Industriell elektroteknik och automationSammanfattning : Today, the importance of sustainable and effective energy usage is rapidly growing. SWEP International AB leads the manufacturing of brazed plate heat exchangers that offer effective heating and cooling applications used in a wide range of systems and industries. LÄS MER
9. An evaluation of U-Net’s multi-label segmentation performance on PDF documents in a medical context
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : The Portable Document Format (PDF) is an ideal format for viewing and printing documents. Today many companies store their documents in a PDF format. However, the conversion from a PDF document to any other structured format is inherently difficult. LÄS MER
10. Analysis of the effect of latent dimensions on disentanglement in Variational Autoencoders
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Disentanglement is a subcategory to Representaton learning where we, apart from believing that useful properties can be extracted from the data in a more compact form, also envision that the data itself is constituted from a lower-dimensional subset of explanatory factors. Explanatory factors are an ambiguous concept and what they portray varies with the dataset. LÄS MER