NDLI: Semi-Automatic Prediction of Landmarks on Human Models in Varying Poses

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Table of Contents

Program Committee

Additional Reviewers

Probabilistic Framework for Feature-Point Matching

A PCA-Based Binning Approach for Matching to Large SIFT Database

Matching Maximally Stable Extremal Regions Using Edge Information and the Chamfer Distance Function

Quasi-Random Scale Space Approach to Robust Keypoint Extraction in High-Noise Environments

Bayesian Identity Clustering

Automated Classification of Operational SAR Sea Ice Images

Efficient Augmentation of the EKF Structure from Motion with Frame-to-Frame Features

Real-Time Virtual Viewpoint Generation on the GPU for Scene Navigation

A Simple but Effective Approach to Video Copy Detection

Texture CLassification Using Compressed Sensing

The Effect of Colour Space on Image Sharpening Algorithms

Automated Filter Parameter Selection Using Measures of Noiseness

Organ Recognition Using Gabor Filters

Multispectral Face Recognition in Texture Space

Curious George: An Integrated Visual Search Platform

Automatic Position Registration of Street-level Fisheye Images into Aerial Image Using Line Structures and Mutual Information

Extracting outlined planar clusters of street facades from 3D point clouds

Matching Images Using Invariant Level-line Primitives under Projective Transformation

Semi-Automatic Prediction of Landmarks on Human Models in Varying Poses

Probabilistic Tracking of Pedestrian Movements via In-Floor Force Sensing

Human Tracking Using Spatialized Multi-level Histogram and Mean Shift

Selecting and Commanding Individual Robots in a Multi-Robot System

Flame Region Detection Based on Histogram Backprojection

Water Flow Detection in a Handwashing Task

Binocular Camera Calibration Using Rectification Error

Rough Terrain Reconstruction for Rover Motion Planning

A Comparison of the EKF, SPKF, and the Bayes Filter for Landmark-Based Localization

Multi-Layer Atlas System for Map Management

A Computer Vision System for Analyzing and Interpreting the Cephalo-ocular Behavior of Drivers in a Simulated Driving Context

An Accurate Image Processing Algorithm for Detecting FISH Probe Locations Relative to Chromosome Landmarks on DAPI Stained Metaphase Chromosome Images

Thermal Imaging as a Way to Classify Cognitive Workload

Retina Vessel Detection Using Fuzzy Ant Colony Algorithm

Robot Localization in Rough Terrains: Performance Evaluation

The Sentinel Problem for a Multi-hop Sensor Network

Telepresence across the Ocean

Fall in! Sorting a Group of Robots with a Continuous Controller

Deformable Object Segmentation and Contour Tracking in Image Sequences Using Unsupervised Networks

Unsupervised Feature Selection and Learning for Image Segmentation

Decoupled Active Surface for Volumetric Image Segmentation

A Bayesian Information Flow Approach to Image Segmentation

Global Context Descriptors for SURF and MSER Feature Descriptors

Construction of a 3D Model of Real-world Object Using Range Intensity Images

Automated Place Classification Using Object Detection

Window-Based Range Flow with an Isometry Constraint

Mammogram Image Superresolution Based on Statistical Moment Analysis

Max-Margin Offline Pedestrian Tracking with Multiple Cues

Object Inter-camera Tracking with Non-overlapping Views: A New Dynamic Approach

Human Action Recognition Using Salient Opponent-Based Motion Features

Human Upper Body Pose Recognition Using Adaboost Template for Natural Human Robot Interaction

Fast FEM-Based Non-Rigid Registration

Publisher's Information

2009 Canadian Conference on Computer and Robot Vision

2008 Canadian Conference on Computer and Robot Vision

Fourth Canadian Conference on Computer and Robot Vision (CRV '07)

The 3rd Canadian Conference on Computer and Robot Vision (CRV'06)

The 2nd Canadian Conference on Computer and Robot Vision (CRV'05)

First Canadian Conference on Computer and Robot Vision, 2004. Proceedings.

Semi-Automatic Prediction of Landmarks on Human Models in Varying Poses

Content Provider	IEEE Xplore Digital Library
Author	Wuhrer, S. Ben Azouz, Z. Chang Shu
Copyright Year	2010
Abstract	We present an algorithm to predict landmarks on 3D human scans in varying poses. Our method is based on learning bending-invariant landmark properties. We also learn the spatial relationships between pairs of landmarks using canonical forms. The information is modeled by a Markov network, where each node of the network corresponds to a landmark position and where each edge of the network represents the spatial relationship between a pair of landmarks. We perform probabilistic inference over the Markov network to predict the landmark locations on human body scans in varying poses. We evaluated the algorithm on 200 models with different shapes and poses. The results show that most landmarks are predicted well.
Starting Page	136
Ending Page	142
File Size	656626
Page Count	7
File Format	PDF
ISBN	9781424469635
e-ISBN	9781424469642
DOI	10.1109/CRV.2010.25
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-31
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer vision Shape Statistical analysis Biological system modeling Councils Robot vision systems Humans Predictive models Prediction algorithms Markov random fields
Content Type	Text
Resource Type	Article

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