Knob, P.[Paulo]
* 2019: Detecting personality and emotion traits in crowds from video sequences
Knobbe, J.
* 2006: Opto-mechanical combination of a line scanning camera and a micro laserscanner system
Knobel, L.[Lukas]
* 2023: Geometric Superpixel Representations for Efficient Image Classification with Graph Neural Networks
Knobelreiter, P.
* 2017: End-to-End Training of Hybrid CNN-CRF Models for Stereo
* 2017: Scalable Full Flow with Learned Binary Descriptors
* 2018: Learning Energy Based Inpainting for Optical Flow
* 2019: Learned Collaborative Stereo Refinement
* 2020: Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems
* 2020: BP-MVSNet: Belief-Propagation-Layers for Multi-View-Stereo
* 2021: Learned Collaborative Stereo Refinement
Includes: Knobelreiter, P. Knöbelreiter, P. (Maybe also Knoebelreiter, P.)Knöbelreiter, P.[Patrick] (Maybe also Knoebelreiter, P.)
7 for Knobelreiter, P.
Knoblauch, D.[Daniel]
* 2009: Factorization of Correspondence and Camera Error for Unconstrained Dense Correspondence Applications
* 2009: Focused Volumetric Visual Hull with Color Extraction
* 2011: Non-Parametric Sequential Frame Decimation for Scene Reconstruction in Low-Memory Streaming Environments
* 2011: Ray Divergence-Based Bundle Adjustment Conditioning for Multi-view Stereo
Knoblauch, K.[Kenneth]
* 2007: Maximum likelihood difference scaling of image quality in compression-degraded images
* 2011: Calibrating MS-SSIM for compression distortions using MLDS
* 2012: Optimizing Multiscale SSIM for Compression via MLDS
* 2014: Spatial selectivity of the watercolor effect
* 2016: Maximum likelihood conjoint measurement of lightness and chroma
* 2016: Perceptual color spacing derived from maximum likelihood multidimensional scaling
Includes: Knoblauch, K.[Kenneth] Knoblauch, K.
Knoble, C.[Charles]
* 2023: Bridging the Gap: Analyzing the Relationship between Environmental Justice Awareness on Twitter and Socio-Environmental Factors Using Remote Sensing and Big Data
Knobler, R.[Robert]
* 1997: Fast tissue segmentation based on a 4D feature map: Preliminary results
Knoblich, U.[Ulf]
* 2002: Stimulus Simplification and Object Representation: A Modeling Study
* 2002: Visual Categorization: How the Monkey Brain Does It
Knobloch, A.
* 2014: Spatial predictive mapping using artificial neural networks
Knoblock, C.[Craig]
* 2007: Special Issue on Noisy Text Analytics
* 2019: Intelligent Systems for Geosciences: An Essential Research Agenda
Knoblock, C.A.[Craig A.]
* 2006: Automatically Conflating Road Vector Data with Orthoimagery
* 2006: Classification of Line and Character Pixels on Raster Maps Using Discrete Cosine Transformation Coefficients and Support Vector Machine
* 2007: Unsupervised information extraction from unstructured, ungrammatical data sources on the World Wide Web
* 2008: Automatically and Accurately Conflating Raster Maps with Orthoimagery
* 2009: Automatic and Accurate Extraction of Road Intersections from Raster Maps
* 2009: Method for Automatically Extracting Road Layers from Raster Maps, A
* 2010: Approach for Recognizing Text Labels in Raster Maps, An
* 2011: Harvesting maps on the web
* 2011: Recognition of Multi-oriented, Multi-sized, and Curved Text
* 2013: general approach for extracting road vector data from raster maps, A
* 2014: How Linked Open Data Can Help in Locating Stolen or Looted Cultural Property
* 2014: Survey of Digital Map Processing Techniques, A
* 2018: Map Archive Mining: Visual-Analytical Approaches to Explore Large Historical Map Collections
* 2018: Spatialising uncertainty in image segmentation using weakly supervised convolutional neural networks: a case study from historical map processing
* 2021: Combining Remote-Sensing-Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents
15 for Knoblock, C.A.