Sökning: "dense"
Visar resultat 1 - 5 av 902 uppsatser innehållade ordet dense.
1. Using NeRF- and Mesh-Based Methods to Improve Visualisation of Point Clouds
Master-uppsats, Lunds universitet/Matematik LTHSammanfattning : In recent years, the field of generating synthetic images from novel view points has seen some major improvements. Most importantly with the publication of Neural Radiance Fields allowing for extremely detailed and accurate 3D novel views. LÄS MER
2. Trädplantering i den hårdgjorda staden : utmaningar och lösningsalternativ
Kandidat-uppsats, SLU/Dept. of Landscape Architecture, Planning and Management (from 130101)Sammanfattning : Träd är viktiga delar av våra städer. De bidrar med många ekosystemtjänster, som klimatreglering, dagvattenhantering och positiva hälsoeffekter. De är också viktiga identitetsskapande element. LÄS MER
3. Land cover classification using machine-learning techniques applied to fused multi-modal satellite imagery and time series data
Master-uppsats, Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskapSammanfattning : Land cover classification is one of the most studied topics in the field of remote sensing, involving the use of data from satellite sensors to analyze and categorize different land surface types. There are numerous satellite products available, each offering different spatial, spectral, and temporal resolutions. LÄS MER
4. The visibility of stellar transients in the Galactic Centre
Kandidat-uppsats, Lunds universitet/Fysiska institutionen; Lunds universitet/AstrofysikSammanfattning : A semi-analytical simulation was developed to evaluate the K-band magnitudes of Red Giants [0.96 - 1.02 M⊙] orbiting the supermassive black hole in the galactic centre. The model assumed star formation between 10-12 Gyrs ago and by following the IMF and applying a random age condition, a stellar population was created. LÄS MER
5. Virtual H&E Staining Using PLS Microscopy and Neural Networks
Master-uppsats, Lunds universitet/Matematik LTHSammanfattning : Histopathological examination, crucial in diagnosing diseases such as cancer, traditionally relies on time- and resource-consuming, poorly standardized chemical staining for tissue visualization. This thesis presents a novel digital alternative using generative neural networks and a point light source (PLS) microscope to transform unstained skin tissue images into their stained counterparts. LÄS MER