Yui, S.
* fast narrow band method and its application in topology-adaptive 3-D modeling, A
Yuille, A.[Alan]
* Home Page.
* email: Yuille, A.[Alan]: yuille AT stat.ucla.edu
* Active Vision
* Bottom-Up and Top-down Object Detection using Primal Sketch Features and Graphical Models
* Direct Passive Navigation: Analytical Solution for Quadratic Patches
* Graph-shifts: Natural image labeling by dynamic hierarchical computing
* Grouping Iso-Velocity Points for Ego-Motion Recovery
* Guest Editorial: Statistical and Computational Theories of Vision: Modeling, Learning, Sampling and Computing, Part I
* Local, Global, and Multilevel Stereo Matching
* MRF Labeling with a Graph-Shifts Algorithm
* Non-Rigid Motion and Regge Calculus
* Occlusions and Binocular Stereo
* Perspective Projection Invariants
* Recovering Object Surfaces from Viewed Changes in Surface Texture Patterns
* Region Competition and its Analysis: A Unified Theory for Image Segmentation
* Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
* Rigidity and Smoothness of Motion
* Statistical Morphology and Bayesian Reconstruction
* Surface Shape from Warping
Includes: Yuille, A.[Alan] Yuille, A.
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Yuille, A.L.
* 3D Symmetry-Curvature Duality Theorems
* 5+/-2 Eigenimages Suffice: An Empirical Investigation of Low-Dimensional Lighting Models
* A* perspective on deterministic optimization for deformable templates, An
* Algorithms from statistical physics for generative models of images
* Automated Extraction of the Cortical Sulci Based on a Supervised Learning Approach
* Bas-Relief Ambiguity, The
* bayesian network framework for relational shape matching, A
* Common Framework for Image Segmentation, A
* Creation of Structure in Dynamic Shape, The
* Data Fusion for Sensory Information Processing Systems
* Deformable Templates for Face Recognition
* Deformable templates for feature extraction from medical images
* Deformable Templates, Robust Statistics, and Hough Transforms
* Depth Recovery Algorithm Using Defocus Information, A
* Describing Surfaces
* Detecting and reading text in natural scenes
* Detecting Object Boundaries Using Low-, Mid-, and High-level Information
* Determining Generative Models of Objects Under Varying Illumination: Shape and Albedo from Multiple Images Using SVD and Integrability
* Determining the Optimal Weights in Multiple Objective Function Optimization
* Double-Loop Algorithm to Minimize the Bethe and Kikuchi Free Energies, A
* Double-Loop Algorithm to Minimize the Bethe Free Energy, A
* Efficient Deformable Template Detection and Localization without User Initialization
* Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification
* Efficient Optimization of a Deformable Template Using Dynamic Programming
* Extremum Principle for Shape from Contour, An
* Feature Extraction from Faces Using Deformable Templates
* Fingerprint Theorems for Zero-Crossings
* Fingerprints Theorems
* FORMS: A Flexible Object Recognition and Modelling System
* framework for shape representation and recognition, A
* Fundamental Bounds on Edge Detection: An Information Theoretic Evaluation of Different Edge Cues
* Fundamental Limits of Bayesian Inference: Order Parameters and Phase Transitions for Road Tracking
* G-factors: Relating Distributions on Features to Distributions on Images
* Generalized Deformable Models, Statistical Physics and Matching Problems
* Generalized Ordering Constraint for Stereo Correspondence, A
* Generative Model Based Approach to Motion Segmentation, A
* generic viewpoint assumption and planar bias, The
* High-Level and Generic Models for Visual Search: When Does High Level Knowledge Help?
* Image Parsing: Unifying Segmentation, Detection, and Recognition
* Image Warping for Shape Recovery and Recognition
* Impossible Shaded Images
* KGBR Viewpoint-Lighting Ambiguity and its Resolution by Generic Constraints, The
* KGBR viewpointlighting ambiguity, The
* Learning Object Representations from Lighting Variations
* Manhattan World: Compass Direction from a Single Image by Bayesian Inference
* Massively Parallel Implementations of Theories for Apparent Motion
* Mathematical Analysis of the Motion Coherence Theory, A
* Max Margin AND/OR Graph learning for parsing the human body
* Mean-Field Phase Transistions and Correlation Functions for Gibbs Random Fields
* model for the estimate of local velocity, A
* Motion Coherence Theory, The
* Motion Estimation by Swendsen-Wang Cuts
* Multilevel Enhancement and Detection of Stereo Disparity Surfaces
* Order Parameters for Detecting Target Curves in Images: When Does High Level Knowledge Help?
* Order Parameters for Minimax Entropy Distributions: When does High Level Knowledge Help?
* Regularized Solution to Edge Detection, A
* Relating image warping to 3D geometrical deformations
* Scale invariance without scale selection
* Scaling and Fingerprint Theorems for Zero-Crossings
* Scaling Theorems for Zero-Crossings
* Segmenting by Seeking the Symmetry Axis
* Shape and Albedo from Multiple Images Using Integrability
* Shape from Shading via the Fusion of Specular and Lambertian Image Components
* Shape from Shading, Occlusion and Texture
* Shape Matching and Recognition: Using Generative Models and Informative Features
* Shape matching and registration by data-driven EM
* Signfinder: Using Color to Detect, Localize and Identify Informational Signs
* Smoothest Velocity Field and Token Matching Schemes, The
* Sources from Shading
* statistical approach to multi-scale edge detection, A
* Statistical Cues for Domain Specific Image Segmentation with Performance Analysis
* Statistical edge detection: learning and evaluating edge cues
* Stereo and Controlled Movement
* Stereo and Eye Movement
* Stereo integration, mean field theory and psychophysics
* Stereopsis And Eye-Movement
* Structure-perceptron learning of a hierarchical log-linear model
* Texture Segmentation by Minimizing Vector-Valued Energy Functionals: The Coupled-Membrane Model
* Time-Efficient Cascade for Real-Time Object Detection: With applications for the visually impaired, A
* Training a general purpose deformable template
* Unsupervised Learning of Object Deformation Models
* Unsupervised learning of probabilistic object models (POMs) for object classification, segmentation and recognition
* Visual Motion Estimation and Prediction: A Probabilistic Network Model for Temporal Coherence
* Zero Crossings on Lines of Curvature
Includes: Yuille, A.L. Yuille, A.L.[Alan L.]
84 for Yuille, A.L.