Latecki, L.J.[Longin J.]
* 1993: Topological Connectedness and 8-Connectedness in Digital Pictures
* 1994: Toward Non-Parametric Digital Shape Representation and Recovery
* 1995: Digital Geometric Invariance and Shape Representation
* 1995: Digitization Constraints That Preserve Topology and Geometry
* 1995: Digitizations Preserving Topological and Differential Geometric-Properties
* 1995: Generalized Convexity: C3 and Boundaries of Convex-Sets
* 1995: Semi-Proximity Continuous-Functions in Digital Images
* 1995: Well-Composed Sets
* 1996: Algorithm for a 3D Simplicity Test, An
* 1996: Modelling Digital Straight Lines
* 1997: 3D Well-Composed Pictures
* 1997: Realistic Digitization Model of Straight-Lines, A
* 1998: Discrete Representation of Spatial Objects in Computer Vision
* 1998: Preserving Topology by a Digitization Process
* 1998: Shape Similarity Measure for Image Database of Occluding Contours
* 1998: Supportedness and Tameness Differentialless Geometry of Plane-Curves
* 1999: Convexity Rule for Shape Decomposition Based on Discrete Contour Evolution
* 1999: Digital geometric methods in document image analysis
* 1999: Digitizations preserving shape
* 1999: Polygon Evolution by Vertex Deletion
* 2000: Shape Descriptors for Non-Rigid Shapes with a Single Closed Contour
* 2000: Shape Similarity Measure Based on Correspondence of Visual Parts
* 2001: Relevance Ranking of Video Data using Hidden Markov Model Distances and Polygon Simplification
* 2002: Application of planar shape comparison to object retrieval in image databases
* 2002: Automatic recognition of unpredictable events in videos
* 2002: Recovering a Polygon from Noisy Data
* 2002: Special issue: Shape Representation and Similarity for Image Databases
* 2003: choice of vantage objects for image retrieval, The
* 2003: Detection of changes in surveillance videos
* 2003: Topologies for the digital spaces Z2 and Z3
* 2004: Motion Detection Based on Local Variation of Spatiotemporal Texture
* 2004: Optimal partial shape similarity
* 2005: Tracking motion objects in infrared videos
* 2006: 3D Object Digitization: Majority Interpolation and Marching Cube
* 2006: 3D Object Digitization: Majority Interpolation and Marching Cubes
* 2006: 3D Object Digitization: Topology Preserving Reconstruction
* 2006: Object Tracking with Dynamic Template Update and Occlusion Detection
* 2006: Polygonal Approximation of Point Sets
* 2006: Using Extended EM to Segment Planar Structures in 3D
* 2007: Contour Grouping Based on Local Symmetry
* 2007: Discrete Skeleton Evolution
* 2007: elastic partial shape matching technique, An
* 2007: Shape Classification Based on Skeleton Path Similarity
* 2007: Skeleton Pruning by Contour Partitioning with Discrete Curve Evolution
* 2007: Skeletonization using SSM of the Distance Transform
* 2007: Topological Equivalence between a 3D Object and the Reconstruction of Its Digital Image
* 2007: Visual Curvature
* 2008: Contour Extraction Using Particle Filters
* 2008: Detection and recognition of contour parts based on shape similarity
* 2008: Improving Shape Retrieval by Learning Graph Transduction
* 2008: Merging maps of multiple robots
* 2008: Path Similarity Skeleton Graph Matching
* 2008: Symmetry of Shapes Via Self-Similarity
* 2008: Topological Repairing of 3D Digital Images
* 2008: Unified Curvature Definition for Regular, Polygonal, and Digital Planar Curves, A
* 2009: Active skeleton for non-rigid object detection
* 2009: Contour Grouping Based on Contour-Skeleton Duality
* 2009: Distance Learning Based on Convex Clustering
* 2009: Locally constrained diffusion process on locally densified distance spaces with applications to shape retrieval
* 2009: Piecewise Linear Models with Guaranteed Closeness to the Data
* 2009: Shape band: A deformable object detection approach
* 2009: Shape guided contour grouping with particle filters
* 2009: Video Coding Scheme Based on Joint Spatiotemporal and Adaptive Prediction, A
* 2010: Balancing Deformability and Discriminability for Shape Matching
* 2010: Boosting Chamfer Matching by Learning Chamfer Distance Normalization
* 2010: Contour based object detection using part bundles
* 2010: Convex shape decomposition
* 2010: Learning Context-Sensitive Shape Similarity by Graph Transduction
* 2010: Weakly Supervised Shape Based Object Detection with Particle Filter
* 2011: Affinity learning on a tensor product graph with applications to shape and image retrieval
* 2011: Feature context for image classification and object detection
* 2011: From partial shape matching through local deformation to robust global shape similarity for object detection
* 2011: Particle filter with state permutations for solving image jigsaw puzzles
* 2011: Skeleton growing and pruning with bending potential ratio
* 2012: Contour-based object detection as dominant set computation
* 2012: Dense Neighborhoods on Affinity Graph
* 2012: Fan Shape Model for object detection
* 2012: Maximum weight cliques with mutex constraints for video object segmentation
* 2012: Shape matching and classification using height functions
* 2012: View-Invariant Object Detection by Matching 3D Contours
* 2013: Affinity Learning with Diffusion on Tensor Product Graph
* 2013: Densifying Distance Spaces for Shape and Image Retrieval
* 2013: Fast Detection of Dense Subgraphs with Iterative Shrinking and Expansion
* 2013: Graph Transduction Learning with Connectivity Constraints with Application to Multiple Foreground Cosegmentation
* 2013: Learning Non-linear Calibration for Score Fusion with Applications to Image and Video Classification
* 2013: Shape clustering: Common structure discovery
* 2014: 3D object retrieval by 3D curve matching
* 2014: Bag of contour fragments for robust shape classification
* 2014: Human Detection Using Learned Part Alphabet and Pose Dictionary
* 2014: Unsupervised Segmentation of RGB-D Images
* 2015: 3D Shape Matching via Two Layer Coding
* 2015: Dense Subgraph Partition of Positive Hypergraphs
* 2015: Semantic Segmentation of RGBD Images with Mutex Constraints
* 2015: Sequential Monte Carlo for Maximum Weight Subgraphs with Application to Solving Image Jigsaw Puzzles
* 2016: Context-regularized learning of fully convolutional networks for scene labeling
* 2016: Efficient shape representation, matching, ranking, and its applications
* 2016: GIFT: A Real-Time and Scalable 3D Shape Search Engine
* 2016: Location-Aware Image Classification
* 2016: Multi-scale context for scene labeling via flexible segmentation graph
* 2016: Similarity Fusion for Visual Tracking
* 2017: Amodal Detection of 3D Objects: Inferring 3D Bounding Boxes from 2D Ones in RGB-Depth Images
* 2017: convolutional neural network framework for blind mesh visual quality assessment, A
* 2017: Ensemble Diffusion for Retrieval
* 2017: GIFT: Towards Scalable 3D Shape Retrieval
* 2017: Unsupervised object region proposals for RGB-D indoor scenes
* 2018: Convolutional Neural Network for Blind Mesh Visual Quality Assessment Using 3D Visual Saliency
* 2018: Dense Deconvolutional Network for Semantic Segmentation
* 2019: Automatic Ensemble Diffusion for 3D Shape and Image Retrieval
* 2019: Common Object Discovery as Local Search for Maximum Weight Cliques in a Global Object Similarity Graph
* 2019: Lednet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation
* 2019: Re-Ranking via Metric Fusion for Object Retrieval and Person Re-Identification
* 2019: Regularized Diffusion Process on Bidirectional Context for Object Retrieval
* 2019: Scene Parsing Via Dense Recurrent Neural Networks With Attentional Selection
* 2019: Training convolutional neural network from multi-domain contour images for 3D shape retrieval
* 2020: Combination Of Handcrafted And Deep Learning-Based Features For 3d Mesh Quality Assessment
* 2020: DCM: A Dense-Attention Context Module For Semantic Segmentation
* 2020: No-reference mesh visual quality assessment via ensemble of convolutional neural networks and compact multi-linear pooling
* 2021: Leveraging Line-point Consistence to Preserve Structures for Wide Parallax Image Stitching
* 2022: BANet: Boundary-Assistant Encoder-Decoder Network for Semantic Segmentation
* 2022: Contextual ensemble network for semantic segmentation
* 2022: DPNET: Dual-Path Network for Efficient Object Detection with Lightweight Self-Attention
* 2022: DRBANET: A Lightweight Dual-Resolution Network for Semantic Segmentation with Boundary Auxiliary
* 2023: Characteristic Mapping for Ellipse Detection Acceleration
* 2023: CNN2Graph: Building Graphs for Image Classification
* 2023: Graph Convolutional Networks based on manifold learning for semi-supervised image classification
* 2023: Rank Flow Embedding for Unsupervised and Semi-Supervised Manifold Learning
* 2023: Self-Supervised Learning with Masked Image Modeling for Teeth Numbering, Detection of Dental Restorations, and Instance Segmentation in Dental Panoramic Radiographs
* 2024: Self-Supervised Learning with Masked Autoencoders for Teeth Segmentation from Intra-oral 3D Scans
Includes: Latecki, L.J.[Longin J.] Latecki, L.J.[Longin Jan] Latecki, L.J.
128 for Latecki, L.J.
Lateef, F.[Fahad]
* 2022: Saliency Heat-Map as Visual Attention for Autonomous Driving Using Generative Adversarial Network (GAN)
Lategahn, H.
* 2010: Texture Classification by Modeling Joint Distributions of Local Patterns With Gaussian Mixtures
* 2014: Vision-Only Localization
Lateh, H.B.
* 2012: TerraSAR-X Data in Cut Slope Soil Stability Monitoring in Malaysia
Latella, M.[Melissa]
* 2021: Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data, A
* 2021: Satellite Image Processing for the Coarse-Scale Investigation of Sandy Coastal Areas