What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Optics
David A. Clausi mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Image segmentation and Segmentation.
His research in Artificial intelligence intersects with topics in Remote sensing and Markov chain.
His studies in Pattern recognition integrate themes in fields like Entropy, Statistic and L-estimator.
His Feature and Edge detection study are his primary interests in Computer vision.
His work in the fields of Scale-space segmentation overlaps with other areas such as Initialization.
The study incorporates disciplines such as Synthetic aperture radar, Markov process and Image processing in addition to Segmentation.
His most cited work include:
- An analysis of co-occurrence texture statistics as a function of grey level quantization (703 citations)
- Designing Gabor filters for optimal texture separability (306 citations)
- Intra-retinal layer segmentation in optical coherence tomography images (178 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Image segmentation and Synthetic aperture radar.
His research related to Segmentation, Feature extraction, Image texture, Pixel and Feature might be considered part of Artificial intelligence.
His study in Robustness extends to Computer vision with its themes.
His Pattern recognition research is multidisciplinary, incorporating elements of Contextual image classification, Histogram and Salient.
His work deals with themes such as k-means clustering and Markov process, which intersect with Image segmentation.
The various areas that David A. Clausi examines in his Synthetic aperture radar study include Speckle noise, Speckle pattern and Radar imaging.
He most often published in these fields:
- Artificial intelligence (76.78%)
- Computer vision (56.40%)
- Pattern recognition (42.65%)
What were the highlights of his more recent work (between 2014-2019)?
- Artificial intelligence (76.78%)
- Computer vision (56.40%)
- Pattern recognition (42.65%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Pattern recognition, Pixel and Feature extraction.
The concepts of his Computer vision study are interwoven with issues in Monte Carlo method and Reflectivity.
His work deals with themes such as Image registration, Sampling and Histogram, which intersect with Pattern recognition.
His work in Pixel tackles topics such as Spectral density which are related to areas like Acoustics, Near-infrared spectroscopy and Sensor fusion.
His Feature extraction study combines topics in areas such as Text mining, Computation and Decision support system.
His Synthetic aperture radar research is multidisciplinary, incorporating perspectives in Classifier and Speckle noise.
Between 2014 and 2019, his most popular works were:
- Lung Nodule Classification Using Deep Features in CT Images (168 citations)
- Sea Ice Concentration Estimation During Melt From Dual-Pol SAR Scenes Using Deep Convolutional Neural Networks: A Case Study (82 citations)
- High-Level Intuitive Features (HLIFs) for Intuitive Skin Lesion Description (66 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Optics
David A. Clausi mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Waveform.
His studies in Artificial intelligence integrate themes in fields like Cancer and Calibration.
His work carried out in the field of Computer vision brings together such families of science as Frame and Light-emitting diode.
His studies deal with areas such as Noise and Skin cancer as well as Pattern recognition.
David A. Clausi interconnects Transfer of learning, Pose, Feature and Optical flow in the investigation of issues within Feature extraction.
His research integrates issues of Segmentation, Manual interpretation and Convolutional neural network in his study of Synthetic aperture radar.
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