What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Statistics
- Algorithm
Sheila S. Hemami focuses on Artificial intelligence, Computer vision, Human visual system model, Algorithm and Wavelet.
She combines topics linked to Pattern recognition with her work on Artificial intelligence.
In the field of Pattern recognition, her study on Image segmentation overlaps with subjects such as Cognitive neuroscience of visual object recognition.
In her study, which falls under the umbrella issue of Computer vision, Interpolation and Iterative reconstruction is strongly linked to Lossy compression.
The various areas that she examines in her Wavelet study include Bicubic interpolation, Image compression, Bilinear interpolation and Mathematical analysis.
The Edge detection study combines topics in areas such as Pixel and Segmentation.
Her most cited work include:
- Frequency-tuned salient region detection (2817 citations)
- VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images (919 citations)
- Regularity-preserving image interpolation (287 citations)
What are the main themes of her work throughout her whole career to date?
Her primary areas of study are Artificial intelligence, Computer vision, Data compression, Algorithm and Wavelet.
Her study on Artificial intelligence is mostly dedicated to connecting different topics, such as Pattern recognition.
The study incorporates disciplines such as Pixel, Estimator and Edge detection in addition to Pattern recognition.
Her Computer vision study which covers Lossy compression that intersects with Lossless compression.
Her biological study spans a wide range of topics, including Mean squared error and Theoretical computer science.
Her Wavelet research incorporates themes from Quantization, Spatial frequency and Masking.
She most often published in these fields:
- Artificial intelligence (58.96%)
- Computer vision (45.52%)
- Data compression (29.85%)
What were the highlights of her more recent work (between 2010-2019)?
- Artificial intelligence (58.96%)
- Computer vision (45.52%)
- Pattern recognition (22.39%)
In recent papers she was focusing on the following fields of study:
Artificial intelligence, Computer vision, Pattern recognition, Encoder and Data compression are her primary areas of study.
Her Artificial intelligence study frequently involves adjacent topics like Coding.
In her work, she performs multidisciplinary research in Computer vision and Psychophysics.
Her Pattern recognition study incorporates themes from Estimator and No reference.
Sheila S. Hemami interconnects Uncompressed video, Control, Multi-objective optimization, Algorithm and Video quality in the investigation of issues within Encoder.
The concepts of her Data compression study are interwoven with issues in Intelligibility, Speech recognition, Focus and Visual communication.
Between 2010 and 2019, her most popular works were:
- Perceptual Visual Signal Compression and Transmission (54 citations)
- A Computational Intelligibility Model for Assessment and Compression of American Sign Language Video (17 citations)
- Estimating the usefulness of distorted natural images using an image contour degradation measure. (17 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Computer vision
Sheila S. Hemami mostly deals with Artificial intelligence, Computer vision, Data compression, Visual communication and Light field.
Her study in Artificial intelligence is interdisciplinary in nature, drawing from both Estimator and Pattern recognition.
Her Computer vision research is multidisciplinary, incorporating perspectives in Parametric statistics and Quality assessment.
Her research in Data compression intersects with topics in Signal compression, Video compression picture types, Video quality and Human visual system model.
She has researched Visual communication in several fields, including Intelligibility, Speech recognition, Encoder and Videoconferencing.
Her studies deal with areas such as Homography, Compression and Approximation theory as well as Light field.
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.
