Caelles, S.
* 2017: One-Shot Video Object Segmentation
* 2018: Deep Extreme Cut: From Extreme Points to Object Segmentation
* 2019: Video Object Segmentation without Temporal Information
Caelli, T.[Terry]
* 2001: Hidden Markov Models: Applications in Computer Vision
* 2001: Hidden Markov Models: Applications in Computer Vision
* 2010: Primitive-based 3D structure inference from a single 2D image for insect modeling: Towards an electronic field guide for insect identification
* 2012: Automatic On-Site Fire Ant Screening System, An
* 2012: Parametric Manifold of an Object under Different Viewing Directions
* 2013: Matching non-aligned objects using a relational string-graph
* 2018: scale-invariant change detection method for land use/cover change research, A
Includes: Caelli, T.[Terry] Caelli, T. Caelli, T.[Terrence]
7 for Caelli, T.
Caelli, T.M.
* 1978: On Perceptual Analyzers Underlying Visual Texture Discrimination: Part I
* 1978: On Perceptual Analyzers Underlying Visual Texture Discrimination: Part II
* 1978: On the Limits of Fourier Decompositions in Visual Texture Perception
* 1981: Visual Perception, Theory and Practice
* 1983: Coding Images in the Frequency Domain: Filter Design and Energy Processing Characteristics of the Human Visual System
* 1984: Figural Synthesis
* 1985: Three Processing Characteristics of Visual Texture Segmentation
* 1986: On the Extraction and Alignment of Image Edges
* 1987: Fast Edge-Only Matching Techniques for Robot Pattern Matching
* 1988: Adaptive Computational Model for Texture Segmentation, An
* 1988: Adaptive iterative algorithm for image restoration
* 1988: Filter-Based Models for Pattern Classification
* 1988: Multiobject Pattern Recognition and Detection in Noisy Backgrounds Using a Hierarchical Approach
* 1988: On the Minimum Number of Templates Required for Shift, Rotation and Size Invariant Pattern Recognition
* 1988: Parsing Scale-Space and Spatial Stability Analysis
* 1988: Sequential Adaptive Recursive Filter for Image Restoration, A
* 1989: Invariant Pattern Recognition Using Multiple Filter Image Representations
* 1989: Recovery of the 3-D Location and Motion of a Rigid Object Through Camera Image (An Extended Kalman Filter Approach)
* 1991: Efficient Visual Representation and Reconstruction from Generalized Curvature Measures
* 1992: Generating Complete Depth Maps In Passive Vision Systems
* 1992: Some new techniques for evidence-based object recognition: EB-ORS1
* 1993: Image Encoding, Labeling and Reconstruction from Differential Geometry
* 1993: improved rule generation method for evidence-based classification systems, An
* 1993: On the Classification of Image Regions by Colour, Texture, and Shape
* 1993: On the Symbolic Interpretation of Traffic Scenes
* 1993: Rulegraphs for Pattern Recognition
* 1993: Texture Classification and Segmentation Algorithms in Man and Machine
* 1994: Computation of Surface Geometry and Segmentation Using Covariance Techniques
* 1994: Inverting an Illumination Model from Range and Intensity Maps
* 1994: Learning Relational Structures: Applications to Computer Vision
* 1994: Learning Structural Descriptions of Patterns: A New Technique for Conditional Clustering and Rule Generation
* 1994: On the Relationship Between Surface Covariance and Differential Geometry
* 1994: Rulegraphs for Graph Matching in Pattern Recognition
* 1994: Variations on the Evidence-Based Object Recognition Theme
* 1995: Estimating the Parameters of an Illumination Model Using Photometric Stereo
* 1995: In-place covariance operators for computer vision
* 1995: Learning how to find patterns or objects in complex scenes
* 1995: Picture Interpretation: A Symbolic Approach
* 1995: Range Measurement from Defocus Gradient
* 1996: Adaptive Gabor Filters for Texture Segmentation
* 1996: Concurrent, Hierarchical Approach to Symbolic Dynamic Scene Interpretation, A
* 1996: Machine Learning Paradigms for Pattern Recognition and Image Understanding
* 1996: Range Image Segmentation by Dynamic Neural-Network Architecture
* 1996: Using Gabor Filters to Measure the Physical Parameters of Lines
* 1996: Using Gabor Filters to Measure the Physical Parameters of Lines
* 1997: CITE: A Trainable Image Annotation System
* 1997: Machine Learning and Image Interpretation
* 1997: Region-Based Coding of Color Images Using Karhunen-Loeve Transform
* 1997: Scene Understanding by Rule Evaluation
* 1998: 3D Shape Matching and Inspection Using Geometric Features and Relational Learning
* 1998: Combining NL Processing and Video Data to Query American Football
* 1998: Filter techniques for complex spatio-chromatic image processing
* 1998: Learning Image Annotation: The Cite System
* 1998: Validation of Machine Learning Techniques: Decision Trees and Finite Training Set
* 1999: Interactively Matching Hand-Drawings Using Induction
* 1999: Local Structure in Images from Entropy Production
* 1999: Low-cost Interactive Active Monocular Range Finder
* 1999: On the Representation of Image Structures via Scale Space Entropy Conditions
* 1999: relational learning method for pattern and object recognition, A
* 1999: Visual information from anisotropic transformations
* 2000: Entropy Production in Colour Images
* 2000: Invariance Signatures: Characterizing Contours by Their Departures from Invariance
* 2000: Learning Image Feature Extraction: Modeling, Tracking and Predicting Human Performance
* 2000: Learning Task-Specific Object Recognition and Scene Understanding
* 2000: model-based neural network for edge characterization, A
* 2000: Theory of spatiochromatic image encoding and feature extraction
* 2001: Encoding Visual Information Using Anisotropic Transformations
* 2001: information-theoretic approach to active vision, An
* 2001: On Learning the Shape of Complex Actions
* 2001: On the Learning of Complex Movement Sequences
* 2001: On the Representation of Visual Information
* 2002: Application of Agent-Oriented Techniques to Symbolic Matching and Object Recognition, An
* 2002: Entropy-based representation of image information
* 2002: Generalized Spatio-Chromatic Diffusion
* 2002: Improved estimation of hidden Markov model parameters from multiple observation sequences
* 2002: trainable hierarchical hidden markov tree model for color image annotation, A
* 2003: Components analysis of hidden Markov models in computer vision
* 2003: Low-cost interactive active range finder
* 2004: Bayesian Stereo Matching
* 2004: comparison of junction tree and relaxation algorithms for point matching using different distance metrics, A
* 2004: Diagnostic tools for evaluating and updating hidden Markov models
* 2004: Eigenspace Projection Clustering Method for Inexact Graph Matching, An
* 2004: Forestry Scene Geometry Estimation Via Statistical Learning
* 2004: graph decomposition approach to least squares attributed graph matching, A
* 2004: Graphical models for graph matching
* 2005: Graphical models for graph matching: Approximate models and optimal algorithms
* 2005: Robust and Efficient Road Tracking in Aerial Images
* 2006: 3D Mapping of Surface Temperature Using Thermal Stereo
* 2006: Approximating the problem, not the solution: An alternative view of point set matching
* 2006: Boosted Band Ratio Feature Selection for Hyperspectral Image Classification
* 2006: Component Optimization for Image Understanding: A Bayesian Approach
* 2006: Graphical Models and Point Pattern Matching
* 2006: Invariant Object Material Identification via Discriminant Learning on Absorption Features
* 2006: Online Discriminative Approach to Background Subtraction, An
* 2006: Road tracking in aerial images based on human-computer interaction and Bayesian filtering
* 2006: Specular Free Spectral Imaging Using Orthogonal Subspace Projection
* 2006: Unified Formulation of Invariant Point Pattern Matching, A
* 2007: Bayesian Stereo Matching
* 2010: Colour Adjustment and Specular Removal for Non-uniform Shape from Shading
* 2010: High-Order Circular Derivative Pattern for Image Representation and Recognition
* 2011: Real-Time Discriminative Background Subtraction
* 2012: Topological features and iterative node elimination for speeding up subgraph isomorphism detection
* 2015: Delaunay-supported edges for image graphs
* 2015: Efficient subgraph matching using topological node feature constraints
* 2017: Bayesian Contrast Measures and Clutter Distribution Determinants of Human Target Detection
Includes: Caelli, T.M. Caelli, T.M.[Terry M.]
105 for Caelli, T.M.