N
Nathan S. Netanyahu
Researcher at Bar-Ilan University
Publications - 150
Citations - 12080
Nathan S. Netanyahu is an academic researcher from Bar-Ilan University. The author has contributed to research in topics: Image registration & Deep learning. The author has an hindex of 27, co-authored 144 publications receiving 11131 citations. Previous affiliations of Nathan S. Netanyahu include Universities Space Research Association & University of Maryland, College Park.
Papers
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Proceedings ArticleDOI
Spectral unmixing of remotely sensed imagery using maximum entropy
TL;DR: This paper applies MAXENT to obtain the fractions of ground cover classes present in a pixel and shows its clear numerical superiority over conventional methods.
Journal ArticleDOI
Mission to Planet Earth: AI views the world
Nicholas M. Short,Robert F. Cromp,William J. Campbell,James C. Tilton,Jacqueline LeMoigne,G. Fekete,Nathan S. Netanyahu,K. Wichmann,Iii. W.B. Ligon +8 more
TL;DR: In the late 1990s, NASA will launch a series of satellites to study the Earth as a dynamic system and the enormous size and complexity of the resulting data holdings pose several challenges and promise to test the limits of practical AI techniques.
Book ChapterDOI
A framework for inter-camera association of multi-target trajectories by invariant target models
TL;DR: A novel framework for associating multi-target trajectories across multiple non-overlapping views (cameras) by constructing an invariant model per each observed target by generating synthetic images that simulate how targets would be seen from different viewpoints is proposed.
Proceedings ArticleDOI
Multi-Sensor Registration of Earth Remotely Sensed Imagery
Jacqueline Le Moigne,Arlene Cole-Rhodes,Roger D. Eastman,Kisha Johnson,J. Morisette,Nathan S. Netanyahu,Harold S. Stone,Ilya Zavorin +7 more
TL;DR: This paper develops automatic image registration methods for multi-sensor data with the goal of achieving sub-pixel accuracy and presents five algorithms where the features are either original gray levels or wavelet-like features and the feature matching is based on gradient descent optimization, statistical robust matching, and mutual information.
Book ChapterDOI
Blockage detection in pawn endings
TL;DR: A blockage-detection method is introduced, which manages to detect a large set of blockage positions in pawn endgames, with practically no additional overhead.