Sökning: "deep video models"

Visar resultat 6 - 10 av 13 avhandlingar innehållade orden deep video models.

1. 6. Automation of Navigation During the Short-loading Cycle Using Machine Vision

   Författare :Carl Borngrund; Ulf Bodin; Fredrik Sandin; Jerker Delsing; Henrik Andreasson; Luleå tekniska universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Automation; Short-loading cycle; Construction equipment; Deep Learning; Computer Vision; Cyber-Physical Systems; Cyberfysiska system;

   Sammanfattning : Earth-moving machines are machines used in a wide range of industries, such as the construction industry, to perform tasks related to earthworks.Currently, the vast majority of earth-moving machines are human-operated where expert operators perform these industry vital tasks. LÄS MER

3. 7. Computer Vision for Automated Traffic Safety Assessment : A Machine Learning Approach

   Författare :Martin Ahrnbom; Mathematical Imaging Group; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Computer vision; Deep learning; Machine learning; Traffic Surveillance; Traffic Safety; Object detection; Camera Calibration; Multi-Target Tracking; Convolutional Neural Networks; Smart cities;

   Sammanfattning : Traffic safety is a complex and important research area with the potential to save many lives in the future. Two key problems are considered, namely the gathering of reliable and detailed road user statistics which can be used to estimate the safety of a traffic environment and taking advantage of surveillance infrastructure to guide and assist vehicles in real time, primarily autonomous ones. LÄS MER

4. 8. Modeling of Magnetic Fields and Extended Objects for Localization Applications

   Författare :Niklas Wahlström; Fredrik Gustafsson; Thomas B. Schön; Simon Maskell; Linköpings universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Localization; magnetic tracking; extended target tracking; signal processing; machine learning; Gaussian processes; deep dynamical model; discretization;

   Sammanfattning : The level of automation in our society is ever increasing. Technologies like self-driving cars, virtual reality, and fully autonomous robots, which all were unimaginable a few decades ago, are realizable today, and will become standard consumer products in the future. LÄS MER

5. 9. Multi-LSTM Acceleration and CNN Fault Tolerance

   Författare :Stefano Ribes; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Compression; SVD; LSTMs; CNNs; Fault Tolerance; Machine Learning; FPGA; Roofline Model; HLS; Caffe;

   Sammanfattning : This thesis addresses the following two problems related to the field of Machine Learning: the acceleration of multiple Long Short Term Memory (LSTM) models on FPGAs and the fault tolerance of compressed Convolutional Neural Networks (CNN). LSTMs represent an effective solution to capture long-term dependencies in sequential data, like sentences in Natural Language Processing applications, video frames in Scene Labeling tasks or temporal series in Time Series Forecasting. LÄS MER

7. 10. NLP methods for improving user rating systems in crowdsourcing forums and speech recognition of less resourced languages

   Författare :Yonas Demeke Woldemariam; Henrik Björklund; Suna Bensch; Anssi Yli-Jyra; Umeå universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NLP-algorithms; speech-recognition; transfer-learning; syntax-semantics; computational linguistic model; Amharic-NLP; cloud-NLP architecture; question-answering; crowdsourcing; user-rating; less-resourced languages; Computer Science; datalogi;

   Sammanfattning : We develop NLP and ASR methods (e.g. LÄS MER