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Sildomar T. Monteiro
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2020 – today
- 2024
- [j9]Andrea Soltoggio
, Eseoghene Ben-Iwhiwhu
A collective AI via lifelong learning and sharing at the edge. Nat. Mac. Intell. 6(3): 251-264 (2024) - [c29]Nicholas Rober, Karan Mahesh
Online Data-Driven Safety Certification for Systems Subject to Unknown Disturbances. ICRA 2024: 9939-9945 - [i6]Karan Mahesh, Tyler M. Paine, Max L. Greene, Nicholas Rober, Steven Lee, Sildomar T. Monteiro, Anuradha Annaswamy, Michael R. Benjamin, Jonathan P. How:
Safe Autonomy for Uncrewed Surface Vehicles Using Adaptive Control and Reachability Analysis. CoRR abs/2410.01038 (2024) - 2023
- [i5]Nicholas Rober, Karan Mahesh, Tyler M. Paine, Max L. Greene, Steven Lee, Sildomar T. Monteiro, Michael R. Benjamin, Jonathan P. How:
Online Data-Driven Safety Certification for Systems Subject to Unknown Disturbances. CoRR abs/2310.19256 (2023)
2010 – 2019
- 2019
- [j8]Utsav B. Gewali
Gaussian Processes for Vegetation Parameter Estimation from Hyperspectral Data with Limited Ground Truth. Remote. Sens. 11(13): 1614 (2019) - [j7]Utsav B. Gewali
Spectral Super-Resolution with Optimized Bands. Remote. Sens. 11(14): 1648 (2019) - 2018
- [c28]Gefei Yang, Utsav B. Gewali, Emmett J. Ientilucci, Michael G. Gartley, Sildomar T. Monteiro:
Dual-Channel Densenet for Hyperspectral Image Classification. IGARSS 2018: 2595-2598 - [i4]Utsav B. Gewali, Sildomar T. Monteiro:
A Tutorial on Modeling and Inference in Undirected Graphical Models for Hyperspectral Image Analysis. CoRR abs/1801.08268 (2018) - [i3]Utsav B. Gewali, Sildomar T. Monteiro, Eli Saber:
Machine learning based hyperspectral image analysis: A survey. CoRR abs/1802.08701 (2018) - 2017
- [c27]Yansong Liu, Sankaranarayanan Piramanayagam, Sildomar T. Monteiro, Eli Saber:
Dense Semantic Labeling of Very-High-Resolution Aerial Imagery and LiDAR with Fully-Convolutional Neural Networks and Higher-Order CRFs. CVPR Workshops 2017: 1561-1570 - [c26]Utsav B. Gewali, Sildomar T. Monteiro:
Using Bayesian optimization to jointly tune the classifier and the random field for spatial-spectral hyperspectral classification. IGARSS 2017: 3692-3695 - [c25]Yansong Liu, Sankaranarayanan Piramanayagam, Sildomar T. Monteiro, Eli Saber:
Semantic segmentation of remote sensing data using Gaussian processes and higher-order CRFS. IGARSS 2017: 5454-5457 - 2016
- [c24]Yang Hu, Sildomar T. Monteiro, Eli Saber:
Super pixel based classification using conditional random fields for hyperspectral images. ICIP 2016: 2202-2205 - [c23]Utsav B. Gewali, Sildomar T. Monteiro:
A novel covariance function for predicting vegetation biochemistry from hyperspectral imagery with Gaussian processes. ICIP 2016: 2216-2220 - [c22]Yilong Liang, Sildomar T. Monteiro, Eli S. Saber:
Gaussian Processes for Object Detection in High Resolution Remote Sensing Images. ICMLA 2016: 998-1003 - [c21]Yansong Liu, Sildomar T. Monteiro, Eli Saber:
Vehicle detection from aerial color imagery and airborne LiDAR data. IGARSS 2016: 1384-1387 - [c20]Utsav B. Gewali, Sildomar T. Monteiro:
Spectral angle based unary energy functions for spatial-spectral hyperspectral classification using Markov random fields. WHISPERS 2016: 1-6 - [c19]Utsav B. Gewali, Sildomar T. Monteiro:
Multitask learning of vegetation biochemistry from hyperspectral data. WHISPERS 2016: 1-5 - [i2]Utsav B. Gewali, Sildomar T. Monteiro:
Spectral Angle Based Unary Energy Functions for Spatial-Spectral Hyperspectral Classification using Markov Random Fields. CoRR abs/1610.06985 (2016) - [i1]Utsav B. Gewali, Sildomar T. Monteiro:
Multitask Learning of Vegetation Biochemistry from Hyperspectral Data. CoRR abs/1610.06987 (2016) - 2015
- [j6]Thierry Peynot, Sildomar T. Monteiro
Editorial: Special issue on Alternative Sensing Techniques for Robot Perception. J. Field Robotics 32(1): 1-2 (2015) - [c18]Trevor M. Sands, Deep Tayal, Matthew E. Morris, Sildomar T. Monteiro:
Robust stock value prediction using support vector machines with particle swarm optimization. CEC 2015: 3327-3331 - [c17]Yang Hu, Eli Saber, Sildomar T. Monteiro
Classification of hyperspectral images based on conditional random fields. Image Processing: Machine Vision Applications 2015: 940510 - [c16]Yang Hu, Sildomar T. Monteiro, Eli Saber:
Comparing inference methods for conditional random fields for hyperspectral image classification. WHISPERS 2015: 1-4 - 2014
- [j5]Richard J. Murphy, Sven Schneider, Sildomar T. Monteiro
Mapping Layers of Clay in a Vertical Geological Surface Using Hyperspectral Imagery: Variability in Parameters of SWIR Absorption Features under Different Conditions of Illumination. Remote. Sens. 6(9): 9104-9129 (2014) - [j4]Richard J. Murphy, Sven Schneider, Sildomar T. Monteiro
Consistency of Measurements of Wavelength Position From Hyperspectral Imagery: Use of the Ferric Iron Crystal Field Absorption at ~900 nm as an Indicator of Mineralogy. IEEE Trans. Geosci. Remote. Sens. 52(5): 2843-2857 (2014) - 2013
- [c15]Sildomar T. Monteiro
Combining strong features for registration of hyperspectral and lidar data from field-based platforms. IGARSS 2013: 1210-1213 - [c14]Oliver M. Cliff
Evaluating techniques for learning a feedback controller for low-cost manipulators. IROS 2013: 704-709 - [c13]Seong H. Lee, Sildomar T. Monteiro, Steven J. Scheding:
Submodular volume simplex analysis: A greedy algorithm for hyperspectral unmixng. WHISPERS 2013: 1-4 - 2012
- [j3]Richard J. Murphy, Sildomar T. Monteiro
Evaluating Classification Techniques for Mapping Vertical Geology Using Field-Based Hyperspectral Sensors. IEEE Trans. Geosci. Remote. Sens. 50(8): 3066-3080 (2012) - [c12]Hang Zhou, Peter Hatherly, Sildomar T. Monteiro
Automatic rock recognition from drilling performance data. ICRA 2012: 3407-3412 - 2011
- [c11]Anthony S. J. Tjiong, Sildomar T. Monteiro
Feature selection with PSO and kernel methods for hyperspectral classification. IEEE Congress on Evolutionary Computation 2011: 1762-1769 - [c10]Sildomar T. Monteiro
Embedded feature selection of hyperspectral bands with boosted decision trees. IGARSS 2011: 2361-2364 - [c9]Sildomar T. Monteiro
Learning 3D Geological Structure from Drill-Rig Sensors for Automated Mining. IJCAI 2011: 2500-2506 - 2010
- [j2]Ali Kadkhodaie-Ilkhchi
Rock Recognition From MWD Data: A Comparative Study of Boosting, Neural Networks, and Fuzzy Logic. IEEE Geosci. Remote. Sens. Lett. 7(4): 680-684 (2010) - [c8]Hang Zhou, Sildomar T. Monteiro
Automated rock recognition with wavelet feature space projection and Gaussian Process classification. ICRA 2010: 4444-4450 - [c7]Sildomar T. Monteiro
Calibrating probabilities for hyperspectral classification of rock types. IGARSS 2010: 2800-2803 - [c6]Juan I. Nieto, Sildomar T. Monteiro
3D geological modelling using laser and hyperspectral data. IGARSS 2010: 4568-4571
2000 – 2009
- 2009
- [c5]Sildomar T. Monteiro
Conditional Random Fields for Rock Characterization Using Drill Measurements. ICMLA 2009: 366-371 - 2008
- [c4]Sildomar Takahashi Monteiro
Hyperspectral Image Classification of Grass Species in Northeast Japan. IGARSS (4) 2008: 399-402 - 2007
- [j1]Sildomar Takahashi Monteiro
Particle Swarms for Feature Extraction of Hyperspectral Data. IEICE Trans. Inf. Syst. 90-D(7): 1038-1046 (2007) - [c3]Sildomar Takahashi Monteiro
A particle swarm optimization-based approach for hyperspectral band selection. IEEE Congress on Evolutionary Computation 2007: 3335-3340 - [c2]Sildomar Takahashi Monteiro, Yukio Kosugi:
Applying Particle Swarm Intelligence for Feature Selection of Spectral Imagery. ISDA 2007: 933-938 - 2006
- [c1]Sildomar Takahashi Monteiro
Prediction of Sweetness and Nitrogen Content in Soybean Crops from High Resolution Hyperspectral Imagery. IGARSS 2006: 2263-2266
Coauthor Index
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last updated on 2024-11-20 21:56 CET by the dblp team
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