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 32 Citations 10,914 162 World Ranking 7255 National Ranking 55

Research.com Recognitions

Awards & Achievements

2018 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to 3D computer vision and image analysis

Overview

What is she best known for?

The fields of study she is best known for:

  • Artificial intelligence
  • Computer vision
  • Statistics

Janne Heikkilä spends much of her time researching Artificial intelligence, Computer vision, Pattern recognition, Histogram and Local binary patterns. Her research in the fields of Pixel, Image and Machine vision overlaps with other disciplines such as Microscopy. Her Computer vision research is mostly focused on the topic Camera resectioning.

As a part of the same scientific family, Janne Heikkilä mostly works in the field of Camera resectioning, focusing on Distortion and, on occasion, Calibration and Pinhole camera model. Her work on Image segmentation and Segmentation as part of general Pattern recognition study is frequently connected to Transverse measure, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. The study incorporates disciplines such as Background subtraction, Point spread function and Gabor filter bank in addition to Histogram.

Her most cited work include:

  • A four-step camera calibration procedure with implicit image correction (1624 citations)
  • Blur Insensitive Texture Classification Using Local Phase Quantization (753 citations)
  • Geometric camera calibration using circular control points (749 citations)

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

Janne Heikkilä mostly deals with Artificial intelligence, Computer vision, Pattern recognition, Image and Algorithm. Her study in Artificial intelligence concentrates on Segmentation, Histogram, Pixel, Image segmentation and Convolutional neural network. Her work deals with themes such as Deep learning and Training set, which intersect with Convolutional neural network.

Her study in Motion blur, Camera resectioning, Camera auto-calibration, Image restoration and Local binary patterns are all subfields of Computer vision. Her Pattern recognition research is multidisciplinary, relying on both Contextual image classification, Grayscale, Invariant and Affine transformation. Janne Heikkilä interconnects System of polynomial equations, Polynomial and Eigenvalues and eigenvectors in the investigation of issues within Algorithm.

She most often published in these fields:

  • Artificial intelligence (77.83%)
  • Computer vision (57.08%)
  • Pattern recognition (25.47%)

What were the highlights of her more recent work (between 2018-2021)?

  • Artificial intelligence (77.83%)
  • Computer vision (57.08%)
  • View synthesis (3.30%)

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

Her scientific interests lie mostly in Artificial intelligence, Computer vision, View synthesis, Segmentation and Image. Her study in the field of Pixel also crosses realms of Task. Her Pixel research is multidisciplinary, incorporating perspectives in Histogram, Local binary patterns, Feature extraction and Machine vision.

Computer vision is closely attributed to Convolutional neural network in her work. Her Segmentation research focuses on Visualization and how it relates to Motion blur. Her Image research includes themes of Parametrization and Focal length.

Between 2018 and 2021, her most popular works were:

  • Rethinking the Evaluation of Video Summaries (31 citations)
  • 3D Multi-Resolution Optical Flow Analysis of Cardiovascular Pulse Propagation in Human Brain (13 citations)
  • Dynamic Texture Classification Using Unsupervised 3D Filter Learning and Local Binary Encoding (7 citations)

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

  • Artificial intelligence
  • Computer vision
  • Statistics

Her primary areas of study are Artificial intelligence, Adversarial system, Representation, Function and Current. Her research in Artificial intelligence is mostly concerned with RANSAC. Her Adversarial system research integrates issues from Convergence, Generative grammar and View synthesis.

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 four-step camera calibration procedure with implicit image correction

J. Heikkila;O. Silven.
computer vision and pattern recognition (1997)

2944 Citations

Geometric camera calibration using circular control points

J. Heikkila.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2000)

1152 Citations

Blur Insensitive Texture Classification Using Local Phase Quantization

Ville Ojansivu;Janne Heikkilä.
international conference on image and signal processing (2008)

1143 Citations

Segmenting salient objects from images and videos

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

578 Citations

A real-time system for monitoring of cyclists and pedestrians

Janne Heikkilä;Olli Silvén.
Versus (1999)

420 Citations

Recognition of blurred faces using Local Phase Quantization

T. Ahonen;E. Rahtu;V. Ojansivu;J. Heikkila.
international conference on pattern recognition (2008)

368 Citations

Calibration procedure for short focal length off-the-shelf CCD cameras

J. Heikkila;O. Silven.
international conference on pattern recognition (1996)

206 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

A Texture-based Method for Detecting Moving Objects

Marko Heikkilä;Matti Pietikäinen;Janne Heikkilä.
british machine vision conference (2004)

200 Citations

Fast and efficient saliency detection using sparse sampling and kernel density estimation

Hamed Rezazadegan Tavakoli;Esa Rahtu;Janne Heikkilä.
scandinavian conference on image analysis (2011)

196 Citations

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