What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Computer network
Bernd Girod mostly deals with Artificial intelligence, Computer vision, Data compression, Algorithm and Motion compensation.
His studies deal with areas such as Bitstream and Pattern recognition as well as Artificial intelligence.
His study connects Coding tree unit and Computer vision.
His work deals with themes such as Vector quantization, Transform coding, Discrete cosine transform, Decoding methods and Image compression, which intersect with Data compression.
His Algorithm study incorporates themes from Entropy, Theoretical computer science and Lagrange multiplier.
His research on Motion compensation also deals with topics like
- Codec and related Encoder and Block-matching algorithm,
- Motion estimation together with Linear prediction.
His most cited work include:
- Distributed Video Coding (1219 citations)
- Watermarking of uncompressed and compressed video (759 citations)
- Analysis of video transmission over lossy channels (750 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Algorithm, Computer network and Data compression.
His Artificial intelligence research includes themes of Coding and Pattern recognition.
His research integrates issues of Encoder and Theoretical computer science in his study of Algorithm.
His biological study spans a wide range of topics, including Distributed computing, Real-time computing and Video quality.
Bernd Girod combines subjects such as Decoding methods, Image compression and Light field with his study of Data compression.
His research investigates the connection with Motion compensation and areas like Multiview Video Coding which intersect with concerns in Scalable Video Coding and Video compression picture types.
He most often published in these fields:
- Artificial intelligence (42.65%)
- Computer vision (36.60%)
- Algorithm (18.95%)
What were the highlights of his more recent work (between 2010-2021)?
- Artificial intelligence (42.65%)
- Computer vision (36.60%)
- Pattern recognition (9.80%)
In recent papers he was focusing on the following fields of study:
Bernd Girod mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Image retrieval and Visual search.
His Artificial intelligence research focuses on subjects like Information retrieval, which are linked to Frame.
Bernd Girod interconnects Key and Virtual reality in the investigation of issues within Computer vision.
His Pattern recognition study also includes
- Histogram of oriented gradients that intertwine with fields like Local binary patterns,
- Histogram which is related to area like Image histogram.
His Data compression study combines topics in areas such as Decoding methods, Motion compensation and Video compression picture types.
As a part of the same scientific study, he usually deals with the Motion compensation, concentrating on Multiview Video Coding and frequently concerns with Video quality.
Between 2010 and 2021, his most popular works were:
- Robust text detection in natural images with edge-enhanced Maximally Stable Extremal Regions (340 citations)
- City-scale landmark identification on mobile devices (339 citations)
- Describing clothing by semantic attributes (330 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Computer network
His scientific interests lie mostly in Artificial intelligence, Computer vision, Image retrieval, Pattern recognition and Visual search.
His Artificial intelligence research integrates issues from Construct, Machine learning and Mobile device.
His study in the field of Augmented reality is also linked to topics like Omnidirectional antenna.
Bernd Girod works mostly in the field of Pattern recognition, limiting it down to concerns involving Robustness and, occasionally, Edge detection, Image warping, Authentication, Distributed source coding and Affine transformation.
His work carried out in the field of Feature brings together such families of science as Algorithm and Image.
His Codebook research is multidisciplinary, relying on both Decoding methods and Data compression.
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