Range image segmentation combining edge-detection and region-growing techniques with applications sto robot bin-picking using vacuum gripper

1990 ◽  
Vol 20 (6) ◽  
pp. 1313-1325 ◽  
Author(s):  
E. Al-Hujazi ◽  
A. Sood
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Region Growing ◽  
Range Image ◽  
Bin Picking

New improvements to range image segmentation by edge detection

2002 ◽  
Vol 9 (2) ◽  
pp. 43-45 ◽  
Author(s):  
O.R.P. Bellon ◽  
L. Silva
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Range Image

A Fit-and-Merge Algorithm for Range-Image Segmentation and Model Reconstruction

2002 ◽  
Vol 2 (4) ◽  
pp. 285-293 ◽  
Author(s):  
M. Djebali ◽  
M. Melkemi ◽  
N. Sapidis
Keyword(s):  
Image Segmentation ◽  
Polynomial Approximation ◽  
Region Growing ◽  
Reconstruction Algorithm ◽  
Theoretical Work ◽  
Range Image ◽  
Model Reconstruction ◽  
Industrial Data ◽  
Merging Procedure ◽  
Principal Surfaces

A segmentation and model-reconstruction algorithm is proposed based on polynomial approximation and on a new version of the “region growing” methodology. First, an initial partition is calculated on the basis of differential-geometric properties of the range image. Then, the first merging procedure is applied (“merge with constraints”) aiming at correctly identifying principal surfaces of the model. It examines all possible mergers of regions and selects those satisfying strict compatibility constraints. The second merging procedure relaxes these constraints to produce the “extended” regions and surfaces of the final segmentation. Theoretical work is presented proving the consistency of these merging procedures. Finally, application of the algorithm on industrial data is presented demonstrating the efficiency of the proposed methodology.

A hybrid image segmentation algorithm based on edge detection, thresholding and region growing

2007 ◽  
Author(s):  
Zhiming Xie ◽  
Guannan Chen ◽  
Rong Chen ◽  
Jinping Lei ◽  
Shangyuan Feng ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Region Growing ◽  
Segmentation Algorithm ◽  
Image Segmentation Algorithm

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

1994 ◽  
Vol 08 (04) ◽  
pp. 969-995 ◽  
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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