Detection of free-form objects using appearance-based local features

Authors:
M. Asuncion Vicente;C. Fernandez;L. Paya;A. Gil
Affiliations:
Miguel Hernandez University, Elche, Alicante, Spain;Miguel Hernandez University, Elche, Alicante, Spain;Miguel Hernandez University, Elche, Alicante, Spain;Miguel Hernandez University, Elche, Alicante, Spain
Venue:
VIIP '07 The Seventh IASTED International Conference on Visualization, Imaging and Image Processing
Year:
2007

Citing 8
Cited 0

A Computational Approach to Edge Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network-Based Face Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: Features Versus Templates

IEEE Transactions on Pattern Analysis and Machine Intelligence
Higher Order Statistical Learning for Vehicle Detection in Images

ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Non-negative Matrix Factorization with Sparseness Constraints

The Journal of Machine Learning Research
Equivalence of Some Common Linear Feature Extraction Techniques for Appearance-Based Object Recognition Tasks

IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces for recognition

Journal of Cognitive Neuroscience

Quantified Score

Hi-index	0.00

Visualization

Abstract

An appearance based object detection system is presented, whose main goal is the detection of free-form objects in non structured environments. A multichannel and multiscale approach is used. Two different feature extraction methods are combined to generate a model for each object: Principal Component Analysis (PCA) and Non-negative Matrix Factorization with Sparsity Constraints (NMFSC). An object-dependant adjustment of channel weighting allows to detect different objects with high accuracy. The detection threshold is also automatically adjusted depending on the object to be detected.