Image-Based grasping point detection using boosted histograms of oriented gradients

Authors:
Leonidas Lefakis;Horst Wildenauer;Manuel Pascual Garcia-Tubio;Lech Szumilas
Affiliations:
IDIAP Research Center, Martigny, Switzerland;Institute for Computer Aided Automation, Vienna University of Technology;Automation and Control Institute, Vienna University of Technology;Research Industrial Institute for Automation and Measurement, Warzawa
Venue:
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
Year:
2010

Citing 13
Cited 0

Robust Real-Time Face Detection

International Journal of Computer Vision
Learning to Detect Objects in Images via a Sparse, Part-Based Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape-Based Recognition of Wiry Objects

IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Generic Object Recognition with Boosting

IEEE Transactions on Pattern Analysis and Machine Intelligence
The relationship between Precision-Recall and ROC curves

ICML '06 Proceedings of the 23rd international conference on Machine learning
Robotic Grasping of Novel Objects using Vision

International Journal of Robotics Research
Multiscale Categorical Object Recognition Using Contour Fragments

IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving object detection with boosted histograms

Image and Vision Computing
From Images to Shape Models for Object Detection

International Journal of Computer Vision
DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo

IEEE Transactions on Pattern Analysis and Machine Intelligence
Sharing features: efficient boosting procedures for multiclass object detection

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Shape context and chamfer matching in cluttered scenes

CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we describe the components of a novel algorithm for the detection of grasping points from monocular images of previously unseen objects. A basic building block of our approach is the use of a newly devised descriptor, capable of representing grasping point shape and appearance by the use of histograms of oriented gradients in a semi-local manner. Combined with boosting our method learns discriminative grasp point models for new objects from a set of annotated real-world images. The method has been extensively evaluated on challenging images of real scenes, exhibiting largely varying characteristics concerning illumination conditions, scene complexity, and viewpoint. Our experiments show that the method, despite these variations, works in a stable manner and that its performance compares favorably to the state-of-the-art.