Crivelari Costa, P.M.[Patricia Monique]
* 2023: Changes in Carbon Dioxide Balance Associated with Land Use and Land Cover in Brazilian Legal Amazon Based on Remotely Sensed Imagery
Includes: Crivelari Costa, P.M.[Patricia Monique] Crivelari-Costa, P.M.[Patrícia Monique]
Crivellari, A.[Alessandro]
* 2018: Beyond Spatial Proximity: Classifying Parks and Their Visitors in London Based on Spatiotemporal and Sentiment Analysis of Twitter Data
* 2019: From Motion Activity to Geo-Embeddings: Generating and Exploring Vector Representations of Locations, Traces and Visitors through Large-Scale Mobility Data
* 2021: CrimeVec: Exploring Spatial-Temporal Based Vector Representations of Urban Crime Types and Crime-Related Urban Regions
* 2022: Mapping Dwellings in IDP/Refugee Settlements Using Deep Learning
Crivellaro, A.[Alberto]
* 2014: Robust 3D Tracking with Descriptor Fields
* 2015: Dense Image Registration and Deformable Surface Reconstruction in Presence of Occlusions and Minimal Texture
* 2015: Novel Representation of Parts for Accurate 3D Object Detection and Tracking in Monocular Images, A
* 2018: Robust 3D Object Tracking from Monocular Images Using Stable Parts
Includes: Crivellaro, A.[Alberto] Crivellaro, A.
Crivellaro, M.[Marta]
* 2024: Characterization of Active Riverbed Spatiotemporal Dynamics through the Definition of a Framework for Remote Sensing Procedures
Crivelli, T.
* 2006: Mixed-State Markov Random Fields for Motion Texture Modeling and Segmentation
* 2008: Simultaneous Motion Detection and Background Reconstruction with a Mixed-State Conditional Markov Random Field
* 2009: Learning mixed-state Markov models for statistical motion texture tracking
* 2010: Mixed-state causal modeling for statistical KL-based motion texture tracking
* 2010: Model Distribution Dependant Complexity Estimation on Textures
* 2011: Simultaneous Motion Detection and Background Reconstruction with a Conditional Mixed-State Markov Random Field
* 2012: From optical flow to dense long term correspondences
* 2012: Multi-step flow fusion: Towards accurate and dense correspondences in long video shots
* 2013: Dense motion estimation between distant frames: Combinatorial multi-step integration and statistical selection
* 2013: Motion Textures: Modeling, Classification, and Segmentation Using Mixed-State Markov Random Fields
* 2015: Background-foreground tracking for video object segmentation
* 2015: Robust Optical Flow Integration
* 2016: Determining Occlusions from Space and Time Image Reconstructions
* 2016: Discovering motion hierarchies via tree-structured coding of trajectories
* 2016: Hierarchical motion decomposition for dynamic scene parsing
* 2016: Multi-reference combinatorial strategy towards longer long-term dense motion estimation
* 2016: Object-guided motion estimation
* 2016: Visual object trapping
* 2017: ROAM: A Rich Object Appearance Model with Application to Rotoscoping
* 2020: ROAM: A Rich Object Appearance Model with Application to Rotoscoping
Includes: Crivelli, T. Crivelli, T.[Tomás] Crivelli, T.[Tomas]
20 for Crivelli, T.