Sökning: "Särdragsextraktion"

Visar resultat 1 - 5 av 12 uppsatser innehållade ordet Särdragsextraktion.

1. 1. Exploring the Use of Attention for Generation Z Fashion Style Recognition with User Annotations as Labels

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

   Författare :Niki Samakovlis; [2023]
   Nyckelord :Attention mechanism; CNN; Deep Learning; Fashion Style Recognition; Feature Extraction; Generation Z; Uppmärksamhetsmekanism; Faltningsnätverk; Djupinlärning; Igenkänning av klädstilar; Särdragsextraktion; Generation Z;

   Sammanfattning : As e-commerce and online shopping have increased worldwide, the interest and research of intelligent fashion systems have expanded. Given the competitive nature of the fashion market business, digital marketplaces depend on determining customer preferences. LÄS MER

3. 2. Feature extraction with self-supervised learning on eye-tracking data from Parkinson’s patients and healthy individuals

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

   Författare :Leo Bergman; [2022]
   Nyckelord :Eye-tracking; Representation learning; Self-supervised learning; Parkinson’s disease; Feature extraction; Clustering analysis; Ögonspårning; Särdragsextraktion; Parkinsonssjukdom; Representationsinlärning; Maskininlärning; Klustring;

   Sammanfattning : Eye-tracking is a method for monitoring and measuring eye movements. The technology has had a significant impact so far and new application areas are emerging. Today, the technology is used in the gaming industry, health industry, self-driving cars, and not least in medicine. LÄS MER

4. 3. Analysis of Brain Signals from Patients with Parkinson’s Disease using Self-Supervised Learning

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

   Författare :Emma Lind; [2022]
   Nyckelord :Machine Learning; Self-supervised learning; Feature extraction; Parkinson’s Disease; Magnetoencephalography; Electroencephalogram; Maskininlärning; Självlärande inlärning; Särdragsextraktion; Parkinsons sjukdom; Magnetoencefalografi; Elektroencefalografi;

   Sammanfattning : Parkinson’s disease (PD) is one of the most common neurodegenerative brain disorders, commonly diagnosed and monitored via clinical examinations, which can be imprecise and lead to a delayed or inaccurate diagnosis. Therefore, recent research has focused on finding biomarkers by analyzing brain networks’ neural activity to find abnormalities associated with PD pathology. LÄS MER

5. 4. Deep Visual Inertial-Aided Feature Extraction Network for Visual Odometry : Deep Neural Network training scheme to fuse visual and inertial information for feature extraction

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

   Författare :Franco Serra; [2022]
   Nyckelord :Feature extraction network; Visual Odometry; IMU; Neural Network; Pose estimation; Feature extraction; Visuell Odometri; IMU; Neuralt nätverk; Poseuppskattning;

   Sammanfattning : Feature extraction is an essential part of the Visual Odometry problem. In recent years, with the rise of Neural Networks, the problem has shifted from a more classical to a deep learning approach. This thesis presents a fine-tuned feature extraction network trained on pose estimation as a proxy task. LÄS MER

7. 5. Classification of Affective Emotion in Musical Themes : How to understand the emotional content of the soundtracks of the movies?

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

   Författare :Paula Diaz Banet; [2021]
   Nyckelord :Music emotion recognition; Deep learning; Feature extraction; VGGish; Mel-frequency Cepstral Coefficients.; Music emotion recognition; Deep learning; Särdragsextraktion; VGGish; Mel-frequency Cepstral Coefficients;

   Sammanfattning : Music is created by composers to arouse different emotions and feelings in the listener, and in the case of soundtracks, to support the storytelling of scenes. The goal of this project is to seek the best method to evaluate the emotional content of soundtracks. LÄS MER