2018 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to 3D computer vision and image analysis
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.
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.
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.
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.
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A four-step camera calibration procedure with implicit image correction
J. Heikkila;O. Silven.
computer vision and pattern recognition (1997)
Geometric camera calibration using circular control points
IEEE Transactions on Pattern Analysis and Machine Intelligence (2000)
Blur Insensitive Texture Classification Using Local Phase Quantization
Ville Ojansivu;Janne Heikkilä.
international conference on image and signal processing (2008)
Segmenting salient objects from images and videos
Esa Rahtu;Juho Kannala;Mikko Salo;Janne Heikkilä.
european conference on computer vision (2010)
A real-time system for monitoring of cyclists and pedestrians
Janne Heikkilä;Olli Silvén.
Recognition of blurred faces using Local Phase Quantization
T. Ahonen;E. Rahtu;V. Ojansivu;J. Heikkila.
international conference on pattern recognition (2008)
Calibration procedure for short focal length off-the-shelf CCD cameras
J. Heikkila;O. Silven.
international conference on pattern recognition (1996)
Deep learning for magnification independent breast cancer histopathology image classification
Neslihan Bayramoglu;Juho Kannala;Janne Heikkila.
international conference on pattern recognition (2016)
A Texture-based Method for Detecting Moving Objects
Marko Heikkilä;Matti Pietikäinen;Janne Heikkilä.
british machine vision conference (2004)
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)
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