Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
Computer vision: theory and industrial applications
Computer vision: theory and industrial applications
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vision guided bin picking and mounting in a flexible assembly cell
IEA/AIE '00 Proceedings of the 13th international conference on Industrial and engineering applications of artificial intelligence and expert systems: Intelligent problem solving: methodologies and approaches
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
Nonparametric Segmentation of Curves into Various Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Concatenate feature extraction for robust 3D elliptic object localization
Proceedings of the 2004 ACM symposium on Applied computing
3D Pose Estimation of Cactus Leaves using an Active Shape Model
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Interactive Assembly Guide Using Augmented Reality
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
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Machine vision is today a well-established technology in industry where especially conveyer belt applications are successful. A related application area is the situation where a number of objects are located in a bin and each has to be picked from the bin. This problem is known as the automatic bin-picking problem and has a huge market potential due to the countless situations where bin-picking is done manually. In this paper we address a general bin-picking problem present at a large pump manufacturer, Grundfos, where many objects with circular openings are handled each day. We pose estimate the objects by finding the 3D opening based on the elliptic projections into two cameras. The ellipses from the two cameras are handled in a unifying manner using graph theory together with an approach that links a pose and an ellipse via the equation for a general cone. Tests show that the presented algorithm can estimate the poses for a large variety of orientations and handle both noise and occlusions.