Richard J. Radke mainly focuses on Artificial intelligence, Computer vision, Feature extraction, Segmentation and Pattern recognition. His research in Artificial intelligence intersects with topics in Matching and Machine learning. His work on Ranking as part of general Machine learning study is frequently connected to Protocol, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His work on Camera resectioning, Re identification and Image editing is typically connected to Prior probability as part of general Computer vision study, connecting several disciplines of science. His Camera resectioning research includes elements of Smart camera and Bundle adjustment. Richard J. Radke has researched Feature extraction in several fields, including Feature, Object detection, Motion estimation, Motion field and Trajectory.
Richard J. Radke spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Mathematical optimization and Machine learning. Segmentation, Feature extraction, Image segmentation, Re identification and Feature are the core of his Artificial intelligence study. His research on Computer vision frequently connects to adjacent areas such as Lidar.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Bilinear interpolation and Metric. In Mathematical optimization, Richard J. Radke works on issues like Dimensionality reduction, which are connected to Radiation treatment planning. His work in Machine learning covers topics such as Analytics which are related to areas like Ranking, Benchmark and Grid.
His primary areas of investigation include Artificial intelligence, Machine learning, Re identification, Key and Human–computer interaction. The various areas that Richard J. Radke examines in his Artificial intelligence study include Natural language processing, Computer vision and Pattern recognition. His Computer vision research includes themes of Space, Range, Sparse array and Light source.
His work in Machine learning tackles topics such as Analytics which are related to areas like Benchmark, Ranking and Grid. His biological study deals with issues like Categorization, which deal with fields such as Anomaly detection and Visualization. His study in the field of Immersive technology is also linked to topics like Web page, Overhead and Spatial interaction.
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Feature extraction, Analytics and Re identification. His Artificial intelligence study incorporates themes from Control system and Sparse array. His study on Machine learning is mostly dedicated to connecting different topics, such as Linear discriminant analysis.
His Feature extraction study also includes
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Image change detection algorithms: a systematic survey
R.J. Radke;S. Andra;O. Al-Kofahi;B. Roysam.
IEEE Transactions on Image Processing (2005)
Automated Cell Lineage Construction: A Rapid Method to Analyze Clonal Development Established with Murine Neural Progenitor Cells
Omar Al-Kofahi;Richard J Radke;Susan K Goderie;Qin Shen.
Cell Cycle (2006)
Person Re-Identification with Discriminatively Trained Viewpoint Invariant Dictionaries
Srikrishna Karanam;Yang Li;Richard J. Radke.
international conference on computer vision (2015)
Model-based segmentation of medical imagery by matching distributions
D. Freedman;R.J. Radke;Tao Zhang;Yongwon Jeong.
IEEE Transactions on Medical Imaging (2005)
A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets
Srikrishna Karanam;Mengran Gou;Ziyan Wu;Angels Rates-Borras.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2019)
Re-Identification With Consistent Attentive Siamese Networks
Meng Zheng;Srikrishna Karanam;Ziyan Wu;Richard J. Radke.
computer vision and pattern recognition (2019)
Viewpoint Invariant Human Re-Identification in Camera Networks Using Pose Priors and Subject-Discriminative Features
Ziyan Wu;Yang Li;Richard J. Radke.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2015)
Detecting Dominant Motions in Dense Crowds
A.M. Cheriyadat;R.J. Radke.
IEEE Journal of Selected Topics in Signal Processing (2008)
Sparse re-id: Block sparsity for person re-identification
Srikrishna Karanam;Yang Li;Richard J. Radke.
computer vision and pattern recognition (2015)
Distributed metric calibration of ad hoc camera networks
Dhanya Devarajan;Richard J. Radke;Haeyong Chung.
ACM Transactions on Sensor Networks (2006)
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