Biggar, S.[Stuart]
* 2014: Landsat-8 Operational Land Imager Radiometric Calibration and Stability
* 2015: Ground-Based Absolute Radiometric Calibration of Landsat 8 OLI, The
Biggar, S.F.[Stuart F.]
* 1997: Radiometric Calibration of LANDSAT
* 2001: generalized approach to the vicarious calibration of multiple Earth observation sensors using hyperspectral data, A
* 2001: Unified approach to absolute radiometric calibration in the solar-reflective range
* 2003: Cross Comparison of EO-1 Sensors and Other Earth Resources Sensors to Landsat-7 ETM+ Using Railroad Valley Playa
* 2003: Vicarious Radiometric Calibration of EO-1 Sensors by Reference to High-Reflectance Ground Targets
* 2020: Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations
Includes: Biggar, S.F.[Stuart F.] Biggar, S.F.
Biggi, A.[Alberto]
* 2012: method for the visual analysis of early-stage Parkinson's disease based on virtual MRI-derived SPECT images, A
Biggin, M.D.[Mark D.]
* 2013: Volumetric Semantic Segmentation Using Pyramid Context Features
Biggio, B.[Battista]
* 2007: Image Spam Filtering Using Visual Information
* 2008: Adversarial Pattern Classification Using Multiple Classifiers and Randomisation
* 2011: Robustness Evaluation of Biometric Systems under Spoof Attacks
* 2011: survey and experimental evaluation of image spam filtering techniques, A
* 2012: Poisoning Adaptive Biometric Systems
* 2012: Security evaluation of biometric authentication systems under real spoofing attacks
* 2013: Pattern Recognition Systems under Attack
* 2014: Poisoning Complete-Linkage Hierarchical Clustering
* 2015: Adversarial Biometric Recognition: A review on biometric system security from the adversarial machine-learning perspective
* 2015: Fast Image Classification with Reduced Multiclass Support Vector Machines
* 2015: Super-Sparse Regression for Fast Age Estimation from Faces at Test Time
* 2016: On Security and Sparsity of Linear Classifiers for Adversarial Settings
* 2017: Is Deep Learning Safe for Robot Vision? Adversarial Examples Against the iCub Humanoid
* 2017: Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems
* 2018: Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
* 2021: Adversarial Machine Learning: Attacks From Laboratories to the Real World
* 2022: Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers
* 2023: Adversarial Attacks Against Uncertainty Quantification
* 2023: ImageNet-Patch: A dataset for benchmarking machine learning robustness against adversarial patches
* 2023: Minimizing Energy Consumption of Deep Learning Models by Energy-Aware Training
* 2023: Phantom Sponges: Exploiting Non-Maximum Suppression to Attack Deep Object Detectors
Includes: Biggio, B.[Battista] Biggio, B.
21 for Biggio, B.
Biggs, B.[Benjamin]
* 2018: Creatures Great and SMAL: Recovering the Shape and Motion of Animals from Video
* 2020: Who Left the Dogs Out? 3d Animal Reconstruction with Expectation Maximization in the Loop
* 2021: On the Road to Large-Scale 3D Monocular Scene Reconstruction using Deep Implicit Functions
* 2023: Shape of You: Precise 3D shape estimations for diverse body types
Biggs, D.S.C.
* 1997: Iterative Blind Deconvolution of Extended Objects
Biggs, E.
* 2016: Deep Learning Guided Partitioned Shape Model for Anterior Visual Pathway Segmentation
* 2016: Renal Segmentation From 3D Ultrasound via Fuzzy Appearance Models and Patient-Specific Alpha Shapes
Biggs, J.
* 2021: Detecting Ground Deformation in the Built Environment Using Sparse Satellite InSAR Data With a Convolutional Neural Network
* 2022: Ground and Satellite-Based Methods of Measuring Deformation at a UK Landslide Observatory: Comparison and Integration
* 2022: Routine Processing and Automatic Detection of Volcanic Ground Deformation Using Sentinel-1 InSAR Data: Insights from African Volcanoes
Includes: Biggs, J. Biggs, J.[Juliet]
Biggs, T.[Trent]
* 2022: Global Evapotranspiration Datasets Assessment Using Water Balance in South America