Search for dissertations about: "Convolutional Neural Networks"

Showing result 1 - 5 of 11 swedish dissertations containing the words Convolutional Neural Networks.

  1. 1. Deep Neural Networks and Image Analysis for Quantitative Microscopy

    University dissertation from Uppsala : Acta Universitatis Upsaliensis

    Author : Sajith Kecheril Sadanandan; Uppsala universitet.; Uppsala universitet.; [2017]
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Deep neural networks; convolutional neural networks; image analysis; quantitative microscopy; bright-field microscopy; Computerized Image Processing; Datoriserad bildbehandling;

    Abstract : Understanding biology paves the way for discovering drugs targeting deadly diseases like cancer, and microscopy imaging is one of the most informative ways to study biology. However, analysis of large numbers of samples is often required to draw statistically verifiable conclusions. READ MORE

  2. 2. Convolutional Network Representation for Visual Recognition

    University dissertation from KTH Royal Institute of Technology

    Author : Ali Sharif Razavian; KTH.; [2017]
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Convolutional Network; Visual Recognition; Transfer Learning; Datalogi; Computer Science;

    Abstract : Image representation is a key component in visual recognition systems. In visual recognition problem, the solution or the model should be able to learn and infer the quality of certain visual semantics in the image. READ MORE

  3. 3. Modeling Music Studies of Music Transcription, Music Perception and Music Production

    University dissertation from Stockholm : KTH Royal Institute of Technology

    Author : Anders Elowsson; KTH.; [2018]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Music Information Retrieval; MIR; Music; Music Transcription; Music Perception; Music Production; Tempo Estimation; Beat Tracking; Polyphonic Pitch Tracking; Polyphonic Transcription; Music Speed; Music Dynamics; Long-time average spectrum; LTAS; Algorithmic Composition; Deep Layered Learning; Convolutional Neural Networks; Rhythm Tracking; Ensemble Learning; Perceptual Features; Representation Learning;

    Abstract : This dissertation presents ten studies focusing on three important subfields of music information retrieval (MIR): music transcription (Part A), music perception (Part B), and music production (Part C).In Part A, systems capable of transcribing rhythm and polyphonic pitch are described. READ MORE

  4. 4. Estimation and Classification of Non-Stationary Processes : Applications in Time-Frequency Analysis

    University dissertation from Centre for Mathematical Sciences, Lund University

    Author : Johan Brynolfsson; Lunds universitet.; Lund University.; Lund University.; Lunds universitet.; Lund University.; [2019-05-17]
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Time-Frequency Estimation; Parameter Estimation; Reassignment method; Non-Stationary Processes; Smooth spectral estimation; Neural Networks;

    Abstract : This thesis deals with estimation and classification problems of non-stationary processes in a few special cases.In paper A and paper D we make strong assumptions about the observed signal, where a specific model is assumed and the parameters of the model are estimated. READ MORE

  5. 5. Learning based Word Search and Visualisation for Historical Manuscript Images

    University dissertation from Uppsala : Acta Universitatis Upsaliensis

    Author : Tomas Wilkinson; Uppsala universitet.; Uppsala universitet.; [2019]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Word Spotting; Convolutional Neural Networks; Deep Learning; Region Proposals; Historical Manuscripts; Computer Vision; Image Analysis; Visualisation; Document Analysis; Computerized Image Processing; Datoriserad bildbehandling;

    Abstract : Today, work with historical manuscripts is nearly exclusively done manually, by researchers in the humanities as well as laypeople mapping out their personal genealogy. This is a highly time consuming endeavour as it is not uncommon to spend months with the same volume of a few hundred pages. READ MORE