Sökning: "bilddata"

Visar resultat 16 - 20 av 50 uppsatser innehållade ordet bilddata.

1. 16. Monocular Visual Odometry for Autonomous Underwater Navigation : An analysis of learning-based monocular visual odometry approaches in underwater scenarios

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Andrea Caraffa; [2021]
   Nyckelord :Deep Learning; Monocular Visual Odometry; Computer Vision; Autonomous Underwater Navigation; Autonomous Underwater Vehicle; Djupinlärning; Monokulär Visuell Odometri; Datorseende; Autonom Undervattensnavigering; Autonomt Undervattensfordon;

   Sammanfattning : Visual Odometry (VO) is the process of estimating the relative motion of a vehicle by using solely image data gathered from the camera. In underwater environments, VO becomes extremely challenging but valuable since ordinary sensors for on-road localization are usually unpractical in these hostile environments. LÄS MER

3. 17. Exploring Deep Learning Approaches to Cleft Lip and Palate Speech

   Master-uppsats, Lunds universitet/Matematisk statistik

   Författare :Tofig Mamedov; Joel Bluhme; [2021]
   Nyckelord :Cleft Lip; Cleft Palate; Speech; CNN; RNN; LSTM; Deep Learning; Mathematics and Statistics;

   Sammanfattning : Cleft lip and palate belong to the most common deformities present at birth. The condition hampers normal speech development in children, and treatment involves both surgery and regular sessions with a speech pathologist. LÄS MER

4. 18. Evaluating Unsupervised Methods for Out-of-Distribution Detection on Semantically Similar Image Data

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Magnus Pierrau; [2021]
   Nyckelord :Out-of-distribution detection; anomaly detection; semantic similarity; image data; comparative evaluation; synthetic image data; Out-of-distribution detektion; anomali detektion; semantisk likhet; bilddata; jämförande utvärdering; syntetisk bilddata;

   Sammanfattning : Out-of-distribution detection considers methods used to detect data that deviates from the underlying data distribution used to train some machine learning model. This is an important topic, as artificial neural networks have previously been shown to be capable of producing arbitrarily confident predictions, even for anomalous samples that deviate from the training distribution. LÄS MER

5. 19. Evaluating automatic colour equalization to preprocess dermoscopic images for classification using a CNN

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Niklas Vatn; Julia Byström; [2021]
   Nyckelord :;

   Sammanfattning : Skin cancer is one of the most prevalent types of cancer and diagnosing of skin lesions are mostly done by visual inspection by a doctor. Lately, computer- aided diagnosis (CAD) has gained popularity and previous studies have with great results utilized a convolutional neural network (CNN) to classify dermoscopic images of different benign and malignant skin lesions. LÄS MER

7. 20. Impact of using different brain layer amounts on the accuracy of Convolutional Neural networks trained on MR-Images to identify Parkinson's Disease

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Benjamin Ronneling; Marcus Dypbukt Källman; [2021]
   Nyckelord :;

   Sammanfattning : Parkinson’s Disease (PD) is a neurodegenerative disease and brain disorder which affects the motor system and leads to shaking, stiffness, impaired balance and coordination. Diagnosing PD from Magnetic resonance images (MR-images) is difficult and often not possible for medical experts and therefore Convolutional neural networks (CNNs) are used instead. LÄS MER