What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Computer vision
Robert M. Haralick spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Image processing and Algorithm.
His Computer vision research includes themes of Training set and Position.
His Pattern recognition research is multidisciplinary, relying on both Histogram and Feature.
The concepts of his Pattern recognition study are interwoven with issues in Document layout analysis, Satellite imagery, Texton, Texture Descriptor and Test data.
His work on Co-occurrence matrix as part of general Image texture study is frequently linked to Structural element, therefore connecting diverse disciplines of science.
The various areas that Robert M. Haralick examines in his Contextual image classification study include Image resolution, Panchromatic film, Aerial photography, Texture filtering and Test set.
His most cited work include:
- Textural Features for Image Classification (15288 citations)
- Statistical and structural approaches to texture (4620 citations)
- Image Analysis Using Mathematical Morphology (2070 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Pixel.
His study involves Image processing, Edge detection, Image, Image segmentation and Pattern recognition, a branch of Artificial intelligence.
His Computer vision study frequently intersects with other fields, such as Set.
His Algorithm study combines topics in areas such as Structuring element, Binary image, Mathematical optimization and Binary number.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Data mining and Cluster analysis.
Pixel and Ground truth are frequently intertwined in his study.
He most often published in these fields:
- Artificial intelligence (59.24%)
- Computer vision (31.93%)
- Algorithm (22.69%)
What were the highlights of his more recent work (between 1999-2021)?
- Artificial intelligence (59.24%)
- Pattern recognition (21.22%)
- Algorithm (22.69%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Pattern recognition, Algorithm, Cluster analysis and Computer vision are his primary areas of study.
Robert M. Haralick works mostly in the field of Artificial intelligence, limiting it down to concerns involving Natural language processing and, occasionally, Probabilistic logic.
He interconnects Contextual image classification, Data mining and Image retrieval in the investigation of issues within Pattern recognition.
His Algorithm research integrates issues from Histogram, Estimator, Mathematical optimization and Entropy.
Within one scientific family, Robert M. Haralick focuses on topics pertaining to Projection pursuit under Cluster analysis, and may sometimes address concerns connected to Matching pursuit.
His work on Photogrammetry and Shape regression as part of general Computer vision study is frequently connected to End systole and End diastole, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Between 1999 and 2021, his most popular works were:
- Feature normalization and likelihood-based similarity measures for image retrieval (324 citations)
- A statistical, nonparametric methodology for document degradation model validation (100 citations)
- Greedy Algorithm for Error Correction in Automatically Produced Boundaries from Low Contrast Ventriculograms (74 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Algorithm
His main research concerns Artificial intelligence, Pattern recognition, Algorithm, Data mining and Edge detection.
His studies deal with areas such as Estimator and Computer vision as well as Artificial intelligence.
His biological study spans a wide range of topics, including Image noise, Constant false alarm rate and Image retrieval.
His Algorithm study integrates concerns from other disciplines, such as Clustering high-dimensional data, Cluster analysis, Thresholding, Ground truth and Real image.
His research on Data mining also deals with topics like
- Document layout analysis which intersects with area such as Table and Pattern recognition,
- Test data which is related to area like Computation, Optimization problem, Probability measure and Iterative method.
His Edge detection research includes themes of Assignment problem and Pixel.
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