<title>Neural net range image segmentation for object recognition</title>

Range image segmentation and surface parameter extraction for 3-D object recognition of industrial parts

10.1109/robot.1987.1088026 ◽

2005 ◽

Author(s):

Joonhee Han ◽

R. Volz ◽

T. Mudge

Keyword(s):

Image Segmentation ◽

Object Recognition ◽

Parameter Extraction ◽

Range Image ◽

Surface Parameter

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Unsupervised Range Image Segmentation and Object Recognition Using Feature Proximity and Markov Random Field

Intelligent Autonomous Systems 13 - Advances in Intelligent Systems and Computing ◽

10.1007/978-3-319-08338-4_58 ◽

2015 ◽

pp. 807-820 ◽

Cited By ~ 1

Author(s):

Dario Lodi Rizzini ◽

Fabio Oleari ◽

Andrea Atti ◽

Jacopo Aleotti ◽

Stefano Caselli

Keyword(s):

Image Segmentation ◽

Object Recognition ◽

Random Field ◽

Markov Random Field ◽

Range Image ◽

Markov Random

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A HYBRID METHOD TOWARDS THE SEGMENTATION OF RANGE IMAGES FOR 3-D OBJECT RECOGNITION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001494000498 ◽

1994 ◽

Vol 08 (04) ◽

pp. 969-995 ◽

Cited By ~ 6

Author(s):

DINESH P. MITAL ◽

EAM KHWANG TEOH ◽

ALAN W.T. LIM

Keyword(s):

Image Segmentation ◽

Object Recognition ◽

Hybrid Approach ◽

Region Growing ◽

Object Identification ◽

Range Image ◽

Range Images ◽

One Dimensional ◽

Identification Process ◽

Combine Region

This paper presents a hybrid approach towards accomplishing the task of range image segmentation. It tends to combine region growing and one-dimensional orthogonal polynomial fitting techniques for the detection of edges in range images. Edges detected by such a method possesses good localisation property. This aids to steer the region growing process towards accomplishing accurate border partitioning. In addition, the incorporation of region growing process eliminates internal micro edges and provides for missing border reconstruction as it is able to detect weak edges. It is believed that the edge and segmentation maps produced, when conveyed to the higher level recognition process, may prove valuable not only for CAD based modeling purposes but may also aid to provide descriptive syntax to the object identification process.

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