Boyken, J.D.[Jeffery D.]
* 2000: Method and apparatus for generating virtual views of sporting events
Boykin, K.G.[Kenneth G.]
* 2015: Geographic Layers as Landscape Drivers for the Marco Polo Argali Habitat in the Southeastern Pamir Mountains of Tajikistan
* 2016: Multispectral and Texture Feature Application in Image-Object Analysis of Summer Vegetation in Eastern Tajikistan Pamirs
* 2020: Cloud-Based Evaluation of the National Land Cover Database to Support New Mexico's Food-Energy-Water Systems, A
Boykin, S.[Stanley]
* 2000: Machine learning of event segmentation for news on demand
Boykov, Y.Y.[Yuri Y.]
* 1997: Disparity Component Matching for Visual Correspondence
* 1997: Variable Neighborhood Approach to Early Vision, A
* 1998: Markov Random Fields with Efficient Approximations
* 1998: New Bayesian Framework for Object Recognition, A
* 1998: Variable Window Approach to Early Vision, A
* 1999: Fast Approximate Energy Minimization via Graph Cuts
* 1999: New Bayesian Framework for Object Recognition, A
* 2000: Adaptive Bayesian Recognition in Tracking Rigid Objects
* 2001: Demonstration of Segmentation with Interactive Graph Cuts
* 2001: Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision, An
* 2001: Fast Approximate Energy Minimization via Graph Cuts
* 2001: Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images
* 2003: Computing geodesics and minimal surfaces via graph cuts
* 2004: Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision, An
* 2005: Graph cuts in vision and graphics: Theories and applications
* 2005: What Metrics Can Be Approximated by Geo-Cuts, Or Global Optimization of Length/Area and Flux
* 2006: Active Graph Cuts
* 2006: From Photohulls to Photoflux Optimization
* 2006: Graph Cuts and Efficient N-D Image Segmentation
* 2006: Integral Solution to Surface Evolution PDEs Via Geo-cuts, An
* 2006: Oriented Visibility for Multiview Reconstruction
* 2006: Semiautomatic Segmentation with Compact Shape Prior
* 2007: Applications of parametric maxflow in computer vision
* 2007: Capacity Scaling for Graph Cuts in Vision
* 2007: Efficient Shape Matching Via Graph Cuts
* 2007: Global Optimization for Shape Fitting
* 2007: Intrinsic Mean for Semi-metrical Shape Retrieval Via Graph Cuts
* 2008: Scalable graph-cut algorithm for N-D grids, A
* 2009: Globally optimal segmentation of multi-region objects
* 2009: Semiautomatic Segmentation with Compact Shape Prior
* 2010: Continuous Max-Flow Approach to Potts Model, A
* 2010: Fast Approximate Energy Minimization with Label Costs
* 2010: Superpixels and Supervoxels in an Energy Optimization Framework
* 2010: TV-based Multi-label Image Segmentation with Label Cost Prior
* 2011: Continuous Max-Flow Approach to Minimal Partitions with Label Cost Prior, A
* 2011: Fast Continuous Max-Flow Approach to Non-convex Multi-labeling Problems, A
* 2011: Interactive Segmentation with Super-Labels
* 2011: Recursive MDL via graph cuts: Application to segmentation
* 2011: Study on Convex Optimization Approaches to Image Fusion, A
* 2012: Convex Max-Flow Approach to Distribution-Based Figure-Ground Separation, A
* 2012: Curvature-based regularization for surface approximation
* 2012: Energy-Based Geometric Multi-model Fitting
* 2012: Fast Approximate Energy Minimization with Label Costs
* 2012: Fast Fusion Moves for Multi-Model Estimation
* 2012: Hausdorff Distance Constraint for Multi-surface Segmentation
* 2012: Minimizing Energies with Hierarchical Costs
* 2012: Segmentation with Non-linear Regional Constraints via Line-Search Cuts
* 2013: Auxiliary Cuts for General Classes of Higher Order Functionals
* 2013: Fast Trust Region for Segmentation
* 2013: GrabCut in One Cut
* 2013: Guest Editorial: Energy Optimization Methods
* 2013: In Defense of 3D-Label Stereo
* 2013: Partial Enumeration and Curvature Regularization
* 2014: Convexity Shape Prior for Segmentation
* 2014: Efficient Squared Curvature
* 2014: Energy Based Multi-model Fitting & Matching for 3D Reconstruction
* 2014: Pseudo-bound Optimization for Binary Energies
* 2014: Submodularization for Binary Pairwise Energies
* 2015: Joint Optimization of Segmentation and Color Clustering
* 2015: Secrets of GrabCut and Kernel K-Means
* 2015: Thin Structure Estimation with Curvature Regularization
* 2015: Volumetric Bias in Segmentation and Reconstruction: Secrets and Solutions
* 2016: Hedgehog Shape Priors for Multi-Object Segmentation
* 2016: Normalized Cut Meets MRF
* 2017: Adaptive and Move Making Auxiliary Cuts for Binary Pairwise Energies
* 2017: Convexity Shape Prior for Binary Segmentation
* 2017: Efficient Optimization for Hierarchically-Structured Interacting Segments (HINTS)
* 2017: Local Submodularization for Binary Pairwise Energies
* 2018: K-convexity Shape Priors for Segmentation
* 2018: Normalized Cut Loss for Weakly-Supervised CNN Segmentation
* 2018: On Regularized Losses for Weakly-supervised CNN Segmentation
* 2019: Beyond Gradient Descent for Regularized Segmentation Losses
* 2019: Divergence Prior and Vessel-Tree Reconstruction
* 2019: Efficient Segmentation: Learning Downsampling Near Semantic Boundaries
* 2019: Kernel Clustering: Density Biases and Solutions
* 2019: Kernel Cuts: Kernel and Spectral Clustering Meet Regularization
* 2021: Confluent Vessel Trees with Accurate Bifurcations
* 2021: Robust Trust Region for Weakly Supervised Segmentation
* 2022: Image Segmentation Using Deep Learning: A Survey
* 2022: Sparse Non-local CRF
Includes: Boykov, Y.Y.[Yuri Y.] Boykov, Y.Y.
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