Sökning: "Statistical image and signal processing"

Visar resultat 1 - 5 av 29 avhandlingar innehållade orden Statistical image and signal processing.

  1. 1. Time-domain Reconstruction Methods for Ultrasonic Array Imaging : A Statistical Approach

    Författare :Fredrik Lingvall; Tadeusz Stepinski; Karl-Jörg Langenberg; Uppsala universitet; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Signalbehandling; Ultrasonic array imaging; synthetic aperture imaging; maximum a posteriori estimation; linear minimum mean squared error estimation; SAFT; Bayesian image reconstruction; Signalbehandling; Signal processing; Signalbehandling; Signal Processing; signalbehandling;

    Sammanfattning : This thesis is concerned with reconstruction techniques for ultrasonic array imaging based on a statistical approach. The reconstruction problem is posed as the estimation of an image consisting of scattering strengths. LÄS MER

  3. 2. Group-Sparse Regression : With Applications in Spectral Analysis and Audio Signal Processing

    Författare :Ted Kronvall; Statistical Signal Processing Group; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; sparse regression; group-sparsity; statistical modeling; regularization; hyperparameter-selection; spectral analysis; audio signal processing; classification; localization; multi-pitch estimation; chroma; convex optimization; ADMM; cyclic coordinate descent; proximal gradient;

    Sammanfattning : This doctorate thesis focuses on sparse regression, a statistical modeling tool for selecting valuable predictors in underdetermined linear models. By imposing different constraints on the structure of the variable vector in the regression problem, one obtains estimates which have sparse supports, i.e. LÄS MER

  4. 3. Computerized Cell and Tissue Analysis

    Författare :Azadeh Fakhrzadeh; Gunilla Borgefors; Cris Luengo Hendriks; Lena Holm; Nasir Rajpoot; Uppsala universitet; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; Image processing; Cell; Tissue; Segmentation; Classification; Histology; Computerized Image Processing; Datoriserad bildbehandling;

    Sammanfattning : The latest advances in digital cameras combined with powerful computer software enable us to store high-quality microscopy images of specimen. Studying hundreds of images manually is very time consuming and has the problem of human subjectivity and inconsistency. LÄS MER

  5. 4. Quantitative image analysis : a focus on automated characterization of structures in optical microscopy of iron ore pellets

    Författare :Frida Nellros; Carolina Wählby; Luleå tekniska universitet; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Signalbehandling; Signal Processing;

    Sammanfattning : Sintering occurs in many types of material such as iron, ceramics and snow, typically during thermal treatment, and aects the material properties, particularly the strength, by the bonding of particles into a coherent structure. In order to improve the mechanical strength in magnetite iron ore pellets it is important to be able to characterize and quantitatively measure the degree of sintering and features that impact the process of sintering. LÄS MER

  7. 5. Generalization under Model Mismatch and Distributed Learning

    Författare :Martin Hellkvist; Ayca Özcelikkale; Anders Ahlén; Martin Jaggi; Uppsala universitet; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Machine learning; Signal processing; Generalization error; Training error; Double-descent; Double descent; Distributed learning; Distributed optimization; Learning over networks; Model mismatch; Model misspecification; Fake features; Missing features; linear regression; regularization; Machine learning; Maskininlärning;

    Sammanfattning : Machine learning models are typically configured by minimizing the training error over a given training dataset. On the other hand, the main objective is to obtain models that can generalize, i.e., perform well on data unseen during training. LÄS MER