scholarly journals A Combined Approach Based on Fuzzy Classification and Contextual Region Growing to Image Segmentation

2016 ◽  
Author(s):  
Mahaman Sani Chaibou ◽  
Karim Kalti ◽  
Basel Solaiman ◽  
Mohamed Ali Mahjoub
Keyword(s):  
Image Segmentation ◽  
Region Growing ◽  
Fuzzy Classification ◽  
Combined Approach

Towards distributed region growing image segmentation based on MapReduce

2015 ◽  
Author(s):  
P. N. Happ ◽  
R. S. Ferreira ◽  
G. A. O. P. Costa ◽  
R. Q. Feitosa ◽  
C. Bentes ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Region Growing

scholarly journals A smoke image segmentation algorithm based on rough set and region growing

2019 ◽  
Vol 65 (No. 8) ◽  
pp. 321-329
Author(s):  
Haitao Wang ◽  
Yanli Chen
Keyword(s):  
Image Segmentation ◽  
Rough Set ◽  
Region Growing ◽  
Recall Rate ◽  
Segmentation Algorithm ◽  
Colour Space ◽  
Seed Point ◽  
Black Smoke ◽  
Growth Method ◽  
Image Segmentation Algorithm

Because the image fire smoke segmentation algorithm can not extract white, gray and black smoke at the same time, a smoke image segmentation algorithm is proposed by combining rough set and region growth method. The R component of the image is extracted in the RGB colour space, the roughness histogram is constructed according to the statistical histogram of the R component, and the appropriate valley value in the roughness histogram is selected as the segmentation threshold, the image is roughly segmented. Relative to the background image, the smoke belongs to the motion information, and the motion region is extracted by the interframe difference method to eliminate static interference. Smoke has a unique colour feature, a smoke colour model is created in the RGB colour space, the motion disturbances of similar colour are removed and the suspected smoke areas are obtained. The seed point is selected in the region, and the region is grown on the result of rough segmentation, the smoke region is extracted. The experimental results show that the algorithm can segment white, gray and black smoke at the same time, and the irregular information of smoke edges is relatively complete. Compared with the existing algorithms, the average segmentation accuracy, recall rate and F-value are increased by 19%, 21.5% and 20%, respectively.<br /><br />

Pixel-parallel digital CMOS implementation of image segmentation by region growing

2005 ◽  
Vol 152 (6) ◽  
pp. 579 ◽  
Author(s):  
T. Morimoto ◽  
Y. Harada ◽  
T. Koide ◽  
H.J. Mattausch
Keyword(s):  
Image Segmentation ◽  
Region Growing ◽  
Digital Cmos ◽  
Cmos Implementation

scholarly journals An Adaptive Region Growing Based on Neutrosophic Set in Ultrasound Domain for Image Segmentation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 60584-60593 ◽  
Author(s):  
Xue Jiang ◽  
Yanhui Guo ◽  
Haibin Chen ◽  
Yaqin Zhang ◽  
Yao Lu
Keyword(s):  
Image Segmentation ◽  
Region Growing ◽  
Neutrosophic Set

scholarly journals REGION GROWING IMAGE SEGMENTATION ON LARGE DATASETS USING GPU

2016 ◽  
Vol 15 (14) ◽  
pp. 7486-7497
Author(s):  
Gurpreet Kaur ◽  
Sonika Jindal
Keyword(s):  
Image Segmentation ◽  
Traffic Control ◽  
Cluster Computing ◽  
Region Growing ◽  
Large Data ◽  
Maximum Speed ◽  
Processing Unit ◽  
Small Areas ◽  
Best Fitting ◽  
Seed Pixel

Image segmentation is an important image processing, and it seems everywhere if we want to analyze what inside the image. There are varieties of applications of image segmentation such as the field of filtering noise from image, medical imaging, and locating objects in satellite images and in automatic traffic control systems, machine vision in problem of feature extraction and in recognition. This paper focuses on accelerating the image segmentation mechanism using region growing algorithm inside GPU (Graphical Processing Unit). In region growing algorithm, an initial set of small areas are iteratively merged according to similarity constraints. We have started by choosing an arbitrary seed pixel and compare it with neighboring pixels. Region is grown from the seed pixel by adding in neighboring pixels that are similar, increasing the size of the region. When the growth of one region stops we simply choose another seed pixel which does not yet belong to any region and start again. This whole process is continued until all pixels belong to some region. If any of the segment makers has the fusion cost lower than the maximum fusion cost (a given threshold), it is selected to grow. Avoid information overlapping like two threads attempting to merge its segment with the same adjacent segment.  Experiments have demonstrated that the proposed shape features do not imply in a significant change of the segmentation results, as long as the algorithm’s parameters are properly adjusted. Moreover, experiments for performance evaluation indicated the potential of using GPUs to accelerate this kind of application. For a simple hardware (GeForce 630M GT), the parallel algorithm reached a maximum speed up of approximately 20-30% for different datasets. Considering that segmentation is responsible for a significant portion of the execution time in many image analysis applications, especially in object-oriented analysis of remote sensing images, the experimentally observed acceleration values are significant. Two variants of PBF (Parallel Best Fitting) and PLMBF (Parallel Local Mutual Best Fitting) have been used to analyze the best merging cost of the two segments. It has been found that the PLMBF has been performed better than PBF.  It should also be noted that these performance gains can be obtained with low investment in hardware, as GPUs with increasing processing power are currently available on the market at declining prices. The parallel computational scheme is well suited for cluster computing, leading to a good solution for segmenting very large data sets.

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