What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
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 most cited work include:
- Low-complexity single-image super-resolution based on nonnegative neighbor embedding (832 citations)
- Image Inpainting : Overview and Recent Advances (344 citations)
- Perceptually-Friendly H.264/AVC Video Coding Based on Foveated Just-Noticeable-Distortion Model (155 citations)
What are the main themes of her work throughout her whole career to date?
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.
She most often published in these fields:
- Artificial intelligence (50.53%)
- Algorithm (38.25%)
- Computer vision (32.28%)
What were the highlights of her more recent work (between 2015-2021)?
- Artificial intelligence (50.53%)
- Light field (13.68%)
- Computer vision (32.28%)
In recent papers she was focusing on the following fields of study:
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.
Between 2015 and 2021, her most popular works were:
- Light Field Compression With Homography-Based Low-Rank Approximation (49 citations)
- Light field compression using depth image based view synthesis (26 citations)
- Autoencoder Based Image Compression: Can the Learning be Quantization Independent? (24 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Algorithm
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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