A Low Complexity Architecture for Binary Image Erosion and Dilation using Structuring Element Decomposition

2005 ◽  
Author(s):  
H. Hedberg ◽  
F. Kristensen ◽  
P. Nilsson ◽  
V. Owall
Keyword(s):  
Low Complexity ◽  
Binary Image ◽  
Structuring Element

scholarly journals Binary morphological closing and opening image filters

2005 ◽  
Author(s):  
Gaetan Lehmann
Keyword(s):  
Binary Image ◽  
Structuring Element ◽  
Image Filters

Binary morphological closing and opening image filters remove structures smaller than the structuring element in a binary image.

scholarly journals Efficient contour detection in binary and label images

2008 ◽  
Author(s):  
Gaetan Lehmann
Keyword(s):  
Execution Time ◽  
Contour Detection ◽  
Binary Image ◽  
Multiprocessor Systems ◽  
Similar Performance ◽  
Performance Improvements ◽  
Structuring Element ◽  
Generic Filter ◽  
Shape And Size

ly in ITK, the only way to find the contour of the objects in a binary image is to use the BinaryErodeImageFilter, with a kernel of radius 1. This filter is a generic filter, made to support any shape and size of structuring element, and thus is not optimized for the particular case needed to detect the contours. Moreover, that filter is not multithreaded, so it can’t get the performance improvements allowed by the multiprocessor systems. As a result, the contour detection can be quite time consuming currently – for example, in SignedMaurerDistanceMapImageFilter, the contour detection takes about 33% of the execution time. This contribution comes with a new filter which highly improve the performance of the contour detection in the binary image, and a second filter which allow the detection of the countours in label images with similar performance.

Novel Watershed Algorithm for Touching Rice Image Segmentation

2011 ◽  
Vol 271-273 ◽  
pp. 1-6
Author(s):  
Fang Jun Kuang ◽  
Wei Hong Xu ◽  
Yan Hua Wang
Keyword(s):  
Image Segmentation ◽  
Measurement Accuracy ◽  
Binary Image ◽  
Watershed Algorithm ◽  
The Real ◽  
Structuring Element

An efficient watershed algorithm is proposed in order to solve the problem that touching rice is difficult to process during consequent image segmentation. First, the binary image is ultra-eroded by using a different structuring element to form different distance image. Second, watershed image is obtained by using the watershed algorithm. Finally, the real watershed can be extracted. Compared with other watershed algorithms, the experiment results demonstrated that this method segmented out rice successfully in the touching rice image and improved the measurement accuracy, and also overcome over-segmentation effectively.

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