D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 31 Citations 7,195 123 World Ranking 7952 National Ranking 71

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Machine learning

His primary areas of study are Artificial intelligence, Computer vision, Pattern recognition, Convolutional neural network and Camera resectioning. His work is dedicated to discovering how Artificial intelligence, Machine learning are connected with Benchmark and other disciplines. His primary area of study in Computer vision is in the field of Contextual image classification.

His Pattern recognition research includes elements of Histogram, Feature detection and Thresholding. His Convolutional neural network research is multidisciplinary, incorporating elements of Feature extraction, Pose and Pyramid. Juho Kannala has included themes like Calibration, Planar, Noise and Angle of view in his Camera resectioning study.

His most cited work include:

  • A generic camera model and calibration method for conventional, wide-angle, and fish-eye lenses (513 citations)
  • Segmenting salient objects from images and videos (439 citations)
  • Fine-Grained Visual Classification of Aircraft (432 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Image and Convolutional neural network. In most of his Artificial intelligence studies, his work intersects topics such as Machine learning. His Motion blur, Camera resectioning and Depth map study, which is part of a larger body of work in Computer vision, is frequently linked to Odometry and Inertial frame of reference, bridging the gap between disciplines.

The various areas that he examines in his Pattern recognition study include Object detection, Autoencoder, Face and Image translation. His Image research includes themes of Training set, Feature, Semantic matching, Task and Function. His Convolutional neural network study combines topics from a wide range of disciplines, such as Transfer of learning, Object, RANSAC and Pose.

He most often published in these fields:

  • Artificial intelligence (85.79%)
  • Computer vision (48.09%)
  • Pattern recognition (28.96%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (85.79%)
  • Computer vision (48.09%)
  • Image (16.94%)

In recent papers he was focusing on the following fields of study:

Juho Kannala focuses on Artificial intelligence, Computer vision, Image, Machine learning and Pattern recognition. His Representation, Reinforcement learning, Visual localization, Autoencoder and Deep learning investigations are all subjects of Artificial intelligence research. His research in Computer vision intersects with topics in Artificial neural network and Inference.

His Image research integrates issues from Feature, Data domain and Sample. His Feature study deals with Depth map intersecting with Pixel and Source image. His Pattern recognition research is multidisciplinary, relying on both Supervised learning, Pascal, Object detection and Regularization.

Between 2019 and 2021, his most popular works were:

  • ICface: Interpretable and Controllable Face Reenactment Using GANs (19 citations)
  • Devon: Deformable Volume Network for Learning Optical Flow (17 citations)
  • Hierarchical Scene Coordinate Classification and Regression for Visual Localization (13 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Computer vision
  • Machine learning

His primary areas of investigation include Artificial intelligence, Pattern recognition, Computer vision, Artificial neural network and Deep learning. His work on Artificial intelligence deals in particular with Autoencoder, Normalization, Face identity, Image manipulation and Generative grammar. His Pattern recognition study incorporates themes from Object detection, Pascal, Supervised learning and Regularization.

His work on Image warping and Optical flow as part of general Computer vision research is frequently linked to Displacement, Volume and High resolution, bridging the gap between disciplines. His Artificial neural network research incorporates elements of Video editing, Image, Face and Animation. Juho Kannala interconnects Embedding, Speech recognition, Gaze and Code in the investigation of issues within Deep learning.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

A generic camera model and calibration method for conventional, wide-angle, and fish-eye lenses

J. Kannala;S.S. Brandt.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2006)

820 Citations

Fine-Grained Visual Classification of Aircraft

Subhransu Maji;Esa Rahtu;Juho Kannala;Matthew B. Blaschko.
arXiv: Computer Vision and Pattern Recognition (2013)

659 Citations

BSIF: Binarized statistical image features

Juho Kannala;Esa Rahtu.
international conference on pattern recognition (2012)

645 Citations

Segmenting salient objects from images and videos

Esa Rahtu;Juho Kannala;Mikko Salo;Janne Heikkilä.
european conference on computer vision (2010)

578 Citations

Joint Depth and Color Camera Calibration with Distortion Correction

C Daniel Herrera;Juho Kannala;Janne Heikkil #x E.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)

522 Citations

Interpolation Consistency Training for Semi-supervised Learning.

Vikas Verma;Alex Lamb;Juho Kannala;Yoshua Bengio.
international joint conference on artificial intelligence (2019)

255 Citations

Interpolation consistency training for semi-supervised learning.

Vikas Verma;Kenji Kawaguchi;Alex Lamb;Juho Kannala.
Neural Networks (2022)

235 Citations

Learning a category independent object detection cascade

Esa Rahtu;Juho Kannala;Matthew Blaschko.
international conference on computer vision (2011)

211 Citations

Deep learning for magnification independent breast cancer histopathology image classification

Neslihan Bayramoglu;Juho Kannala;Janne Heikkila.
international conference on pattern recognition (2016)

204 Citations

Generating Object Segmentation Proposals Using Global and Local Search

Pekka Rantalankila;Juho Kannala;Esa Rahtu.
computer vision and pattern recognition (2014)

200 Citations

If you think any of the details on this page are incorrect, let us know.

Contact us

Best Scientists Citing Juho Kannala

Christoph Busch

Christoph Busch

Norwegian University of Science and Technology

Publications: 77

Abdenour Hadid

Abdenour Hadid

University of Oulu

Publications: 29

Huchuan Lu

Huchuan Lu

Dalian University of Technology

Publications: 25

Luc Van Gool

Luc Van Gool

ETH Zurich

Publications: 24

Marc Pollefeys

Marc Pollefeys

ETH Zurich

Publications: 21

Christian Rathgeb

Christian Rathgeb

Darmstadt University of Applied Sciences

Publications: 19

Torsten Sattler

Torsten Sattler

Czech Technical University in Prague

Publications: 17

Ming-Ming Cheng

Ming-Ming Cheng

Nankai University

Publications: 17

Dacheng Tao

Dacheng Tao

University of Sydney

Publications: 17

Stefano Soatto

Stefano Soatto

University of California, Los Angeles

Publications: 16

Subhransu Maji

Subhransu Maji

University of Massachusetts Amherst

Publications: 16

Qi Tian

Qi Tian

Huawei Technologies (China)

Publications: 15

Gian Luca Marcialis

Gian Luca Marcialis

University of Cagliari

Publications: 15

Serge Belongie

Serge Belongie

Cornell University

Publications: 15

Ming-Hsuan Yang

Ming-Hsuan Yang

University of California, Merced

Publications: 14

Niki Trigoni

Niki Trigoni

University of Oxford

Publications: 14

Something went wrong. Please try again later.