2013 - IEEE Fellow For contributions to image and video compression
Her primary areas of study are Artificial intelligence, Computer vision, Algorithm, Decoding methods and Inpainting. The various areas that Christine Guillemot examines in her Artificial intelligence study include Coding and Pattern recognition. In the subject of general Computer vision, her work in Video tracking, Video compression picture types, Scalable Video Coding and Harmonic wavelet transform is often linked to Context, thereby combining diverse domains of study.
Her study looks at the intersection of Algorithm and topics like Theoretical computer science with Hamming code, Hamming distance and Encoder. Her Decoding methods course of study focuses on Error detection and correction and Turbo code, Communication channel, Viterbi decoder, Bit error rate and Soft-decision decoder. Her study explores the link between Image and topics such as Computation that cross with problems in Embedding, Representation, Reduction, Feature vector and k-nearest neighbors algorithm.
Her scientific interests lie mostly in Artificial intelligence, Algorithm, Computer vision, Decoding methods and Pattern recognition. Her Light field, Data compression, Pixel, Inpainting and Image compression investigations are all subjects of Artificial intelligence research. Her Algorithm research integrates issues from Theoretical computer science and Coding.
Her study on Computer vision is mostly dedicated to connecting different topics, such as Encoder. Her studies in Decoding methods integrate themes in fields like Error detection and correction and Communication channel. Her Pattern recognition study incorporates themes from Artificial neural network, Embedding and Template matching.
The scientist’s investigation covers issues in Artificial intelligence, Light field, Computer vision, Algorithm and Pattern recognition. Her biological study spans a wide range of topics, including Low-rank approximation, Inpainting, Iterative reconstruction and Compressed sensing. Her Computer vision study frequently links to other fields, such as Coding.
Her Algorithm research incorporates themes from High dynamic range, Tone mapping, Image frame and Backward compatibility. Her Pattern recognition study combines topics from a wide range of disciplines, such as Embedding, Binary tree and Linear map. Her Compression research is multidisciplinary, incorporating elements of Decoding methods, Representation, Fourier transform and Encoding.
Her primary areas of study are Artificial intelligence, Computer vision, Light field, Pattern recognition and Compression. Her study connects Low-rank approximation and Artificial intelligence. In general Computer vision study, her work on Pixel often relates to the realm of Term, thereby connecting several areas of interest.
Her studies deal with areas such as Image resolution, Inpainting, Decoding methods, Range and Iterative reconstruction as well as Light field. Her Pattern recognition research is multidisciplinary, incorporating perspectives in Artificial neural network and Embedding. The study incorporates disciplines such as Algorithm, Graph size and Ground truth in addition to Segmentation.
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Low-complexity single-image super-resolution based on nonnegative neighbor embedding
Marco Bevilacqua;Aline Roumy;Christine Guillemot;Marie Line Alberi Morel.
british machine vision conference (2012)
Image Inpainting : Overview and Recent Advances
Christine Guillemot;Olivier Le Meur.
IEEE Signal Processing Magazine (2014)
Perceptually-Friendly H.264/AVC Video Coding Based on Foveated Just-Noticeable-Distortion Model
Zhenzhong Chen;Christine Guillemot.
IEEE Transactions on Circuits and Systems for Video Technology (2010)
Optimal Reconstruction in Wyner-Ziv Video Coding with Multiple Side Information
D. Kubasov;J. Nayak;C. Guillemot.
multimedia signal processing (2007)
Distributed Monoview and Multiview Video Coding
C. Guillemot;F. Pereira;L. Torres;T. Ebrahimi.
IEEE Signal Processing Magazine (2007)
Polynomial transform computation of the 2-D DCT
P. Duhamel;C. Guillemot.
international conference on acoustics, speech, and signal processing (1990)
Examplar-based inpainting based on local geometry
Olivier Le Meur;Josselin Gautier;Christine Guillemot.
international conference on image processing (2011)
Joint source-channel turbo decoding of entropy-coded sources
A. Guyader;E. Fabre;C. Guillemot;M. Robert.
IEEE Journal on Selected Areas in Communications (2001)
Distributed video coding: Selecting the most promising application scenarios
Fernando Pereira;Luis Torres;Christine Guillemot;Touradj Ebrahimi.
Signal Processing-image Communication (2008)
Hierarchical Super-Resolution-Based Inpainting
Olivier Le Meur;Mounira Ebdelli;Christine Guillemot.
IEEE Transactions on Image Processing (2013)
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