A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graphical Models and Image Processing
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning a Sparse Representation for Object Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Locating objects using the Hausdorff distance
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Detecting Pedestrians Using Patterns of Motion and Appearance
International Journal of Computer Vision
Pedestrian Detection in Crowded Scenes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Contour-Based Learning for Object Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Multiple Object Class Detection with a Generative Model
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Multi-Aspect Detection of Articulated Objects
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Component Learning for Object Detection
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Contour Grouping Based on Contour-Skeleton Duality
International Journal of Computer Vision
Object detection using spatial histogram features
Image and Vision Computing
Object detection combining recognition and segmentation
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
A cascade of feed-forward classifiers for fast pedestrian detection
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
A discriminative feature space for detecting and recognizing faces
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
An improved template matching method for object detection
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Hi-index | 0.01 |
Motivated by the discriminative ability of shape information and local patterns in object recognition, this paper proposes a window-based object descriptor that integrates both cues. In particular, contour templates representing object shape are used to derive a set of so-called key points at which local appearance features are extracted. These key points are located using an improved template matching method that utilises both spatial and orientation information in a simple and effective way. At each of the extracted key points, a new local appearance feature, namely non-redundant local binary pattern (NR-LBP), is computed. An object descriptor is formed by concatenating the NR-LBP features from all key points to encode the shape as well as the appearance of the object. The proposed descriptor was extensively tested in the task of detecting humans from static images on the commonly used MIT and INRIA datasets. The experimental results have shown that the proposed descriptor can effectively describe non-rigid objects with high articulation and improve the detection rate compared to other state-of-the-art object descriptors.