World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
44
Citations
45682
World Ranking
7336
National Ranking
3192

Overview

John Winn is affiliated with Microsoft in the United States. Their research spans multiple fields with notable contributions in computer science and medicine.

The main areas of study John Winn focuses on include:

  • Computer Science
  • Medicine

Their subfields consist of:

  • Computer Vision and Pattern Recognition
  • Pulmonary and Respiratory Medicine
  • Economics and Econometrics
  • Visual Arts and Performing Arts
  • Political Science and International Relations

John Winn's research topics encompass:

  • Cystic Fibrosis Research Advances
  • Inhalation and Respiratory Drug Delivery
  • Advanced Neural Network Applications
  • Human Pose and Action Recognition
  • Advanced Vision and Imaging
  • Cinema and Media Studies
  • Art History and Market Analysis

Their recent published papers include:

  • Learning Direct Optimization for scene understanding, 2020, published in Pattern Recognition
  • Machine Learning Predicts Acute Pulmonary Exacerbations in Cystic Fibrosis, 2020, published in SSRN Electronic Journal
  • The landscapes of western movies: a history of filming on location, 1900-1970, 2023, published in New Review of Film and Television Studies

John Winn has collaborated with multiple co-authors including:

  • D. Sutcliffe
  • Emem Ukor
  • Judy Ryan
  • Janet M. Allen
  • Karen Brown

Their contributions to publication venues are distributed among:

  • SSRN Electronic Journal
  • Pattern Recognition
  • New Review of Film and Television Studies

Best Publications

  • The Pascal Visual Object Classes (VOC) Challenge

    Mark Everingham;Luc Gool;Christopher K. Williams;John Winn

  • The Pascal Visual Object Classes Challenge: A Retrospective

    Mark Everingham;S. M. Eslami;Luc Gool;Christopher K. Williams

  • TextonBoost : joint appearance, shape and context modeling for multi-class object recognition and segmentation

    Jamie Shotton;John Winn;Carsten Rother;Antonio Criminisi

  • TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context

    Jamie Shotton;John Winn;Carsten Rother;Antonio Criminisi

  • Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses

    Oliver Stegle;Leopold Parts;Matias Piipari;John Winn

  • Object categorization by learned universal visual dictionary

    J. Winn;A. Criminisi;T. Minka

  • Epitomic location recognition

    Kai Ni;A. Kannan;A. Criminisi;J. Winn

  • Variational Message Passing

    John Winn;Christopher M. Bishop

  • LOCUS: learning object classes with unsupervised segmentation

    J. Winn;N. Jojic

  • A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies.

    Oliver Stegle;Oliver Stegle;Leopold Parts;Richard Durbin;John M. Winn

  • Beyond atopy: Multiple patterns of sensitization in relation to asthma in a birth cohort study

    Angela Simpson;Vincent Y. F. Tan;John Winn;Markus Svensén

  • Non-linear Bayesian Image Modelling

    Christopher M. Bishop;John M. Winn

  • The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects

    J. Winn;J. Shotton

  • The Fourth Paradigm: Data-Intensive Scientific Discovery

    Iain Buchan;John Winn;Christopher Bishop

  • Photo clip art

    Jean-François Lalonde;Derek Hoiem;Alexei A. Efros;Carsten Rother

  • Discriminative Object Class Models of Appearance and Shape by Correlatons

    S. Savarese;J. Winn;A. Criminisi

  • The Shape Boltzmann Machine: A Strong Model of Object Shape

    S. M. Eslami;Nicolas Heess;Christopher K. Williams;John Winn

  • 3D LayoutCRF for Multi-View Object Class Recognition and Segmentation

    D. Hoiem;C. Rother;J. Winn

  • Tree-based Classifiers for Bilayer Video Segmentation

    Pei Yin;A. Criminisi;J. Winn;M. Essa

  • Entangled decision forests and their application for semantic segmentation of CT images

    Albert Montillo;Jamie Shotton;John Winn;Juan Eugenio Iglesias

  • The Shape Boltzmann Machine: A strong model of object shape

    S. M. Ali Eslami;Nicolas Heess;John Winn

  • Gates

    Tom Minka;John Winn

Frequent Co-Authors

Antonio Criminisi
Antonio Criminisi Microsoft (United States)
Jamie Shotton
Jamie Shotton Microsoft (United States)
Christopher M. Bishop
Christopher M. Bishop Microsoft (United States)
Leopold Parts
Leopold Parts Wellcome Sanger Institute
Richard Durbin
Richard Durbin University of Cambridge
Nicolas Heess
Nicolas Heess DeepMind (United Kingdom)
Carsten Rother
Carsten Rother Heidelberg University
Oliver Stegle
Oliver Stegle German Cancer Research Center
Tom Minka
Tom Minka Microsoft (United States)
Tim D. Spector
Tim D. Spector King's College London

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