'' '' '' '' Counting Pig using Marker-Controlled Watershed Segmentation
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International Journal of Current Microbiology and Applied Sciences (IJCMAS)
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National Academy of Agricultural Sciences (NAAS)
NAAS Score: *5.38 (2020)
[Effective from January 1, 2020]
For more details click here

ICV 2019: 96.39
Index Copernicus ICI Journals Master List 2019 - IJCMAS--ICV 2019: 96.39
For more details click here

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Original Research Articles                      Volume : 10, Issue:1, January, 2021

PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
Publisher : Excellent Publishers
Email : editorijcmas@gmail.com / submit@ijcmas.com
Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2018: 95.39
NAAS RATING 2020: 5.38

Int.J.Curr.Microbiol.App.Sci.2021.10(1): 2069-2078
DOI: https://doi.org/10.20546/ijcmas.2021.1001.238


Counting Pig using Marker-Controlled Watershed Segmentation
Salam Jayachitra Devi1* and Kh. Manglem Singh2
1National Research Centre on Pig, Guwahati, India
2National Institute of Technology, Manipur, India
*Corresponding author
Abstract:

Counting the total number of pigs manually on a large-scale pig farm is a crucial and inefficient task. As this process is time-consuming and includes many critical points that can lead to miscalculation. Some of the challenging issues in pig counting include overlapping, partial occlusion, different viewpoint that limits the usage of traditional object detection methods. Image segmentation is used for object detection, which separate foreground and background pixels of the images. In this paper, we used Marker-Controlled Watershed segmentation method for counting pig in an image. Here, different image thresholding techniques such as Otsu threshold, Adaptive threshold and manual threshold is considered. The structural similarity of these thresholding techniques is determined using jaccards coefficient index. Otsu threshold gives the best similarity scores. The average processing time of these thresholding techniques is also determined. Further, the images obtained from Otsu threshold is checked for overlapping objects. In case of image with overlapping objects, the segmentation is done using marker-controlled watershed segmentation algorithm to segregate the overlapping objects and label the objects individually. In case of non overlapping, objects present in the images obtained from Otsu threshold are label directly to count the number of pigs present in the image. Hence, this segmentation process provides an efficient way for counting pigs in an image.


Keywords: Image segmentation, counting pig, Object detection, Overlapping objects
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How to cite this article:

Salam Jayachitra Devi and Manglem Singh, Kh. 2021. Counting Pig using Marker-Controlled Watershed Segmentation.Int.J.Curr.Microbiol.App.Sci. 10(1): 2069-2078. doi: https://doi.org/10.20546/ijcmas.2021.1001.238
Copyright: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike license.