Automatic Image Segmentation Using Edge and Marker-Controlled Watershed Transformation

2014 ◽  
Keyword(s):  
Image Segmentation ◽  
Watershed Transformation

Detritus image segmentation using watershed transformation

2009 ◽  
Vol 29 (10) ◽  
pp. 2859-2861 ◽  
Author(s):  
Qing-bo SHEN ◽  
Wei WU ◽  
Xiao-min YANG ◽  
Xiao-hai HE
Keyword(s):  
Image Segmentation ◽  
Watershed Transformation

Analyzing the Optimal Performance of Pest Image Segmentation using Non Linear Objective Assessments

2016 ◽  
Vol 6 (6) ◽  
pp. 2789 ◽  
Author(s):  
Siva Sangari A ◽  
Saraswady D
Keyword(s):  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Objective Assessment ◽  
Absolute Error ◽  
Agricultural Field ◽  
Watershed Transformation ◽  
Fuzzy C Means ◽  
Plant Cultivation ◽  
Fuzzy C Means Algorithm

<p>In modern agricultural field, pest detection is a major role in plant cultivation. In order to increase the Production rate of agricultural field, the presence of whitefly pests which cause leaf discoloration is the major problem.  This emphasizes the necessity of image segmentation, which divides an image into parts that have strong correlations with objects to reflect the actual information collected from the real world. Image processing is affected by illumination conditions, random noise and environmental disturbances due to atmospheric pressure or temperature fluctuation. The quality of pest images is directly affected by atmosphere medium, pressure and temperature. The fuzzy c means (FCM) have been proposed to identify accurate location of whitefly pests. The watershed transform has interesting properties that make it useful for many different image segmentation applications: it is simple and intuitive, can be parallelized, and always produces a complete division of the image. However, when applied to pest image analysis, it has important drawbacks (over segmentation, sensitivity to noise). In this paper, pest image segmentation using marker controlled watershed segmentation is presented. Objective of this paper is segmenting the pest image and comparing the results of fuzzy c means algorithm and marker controlled watershed transformation. The performance of an image segmentation algorithms are compared using nonlinear objective assessment or the quantitative measures like structural content, peak signal to noise ratio, normalized correlation coefficient, average difference and normalized absolute error. Out of the above methods the experimental results show that fuzzy c means algorithm performs better than watershed transformation algorithm in processing pest images.</p>

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