Improved Segmentation Technique for Underwater Images Based on K-means and Local Adaptive Thresholding

  • Agrawal Avni Rajeev
  • Saroj Hiranwal
  • Vijay Kumar Sharma
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 10)


In many cases, images are influenced by radiance and environmental turbulences owing to temperature variation, specifically in the case of underwater images. Due to lack of stableness in underwater circumstances, object identification in underwater is not easy in any aspect. As we know, the process of segmenting the image is a quite essential in automated object recognition systems. Subsequently, there is a necessity of segmenting the images. By means of segmenting the image, we split the image in meaningful fragments in a way to detect the concerned regions to annotate the data. We also need to process the image to eradicate the radiance effect. In this paper, we propose the improved technique to eradicate the effect of radiance and identify the object with more precision and accuracy. According to the proposed improved technique, the two segmentation techniques, k-means segmentation and local adaptive thresholding method, are merged. K-means deals with object detection whereas local adaptive thresholding eradicates the radiance effect. Lastly, the performance of improved technique is evaluated using objective assessment parameter namely, entropy, PSNR, and mutual information.


Underwater images Illumination Radiance K-means Local adaptive thresholding Mutual information Entropy 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Agrawal Avni Rajeev
    • 1
  • Saroj Hiranwal
    • 1
  • Vijay Kumar Sharma
    • 1
  1. 1.Computer scienceRajasthan Institute of Engineering and Technology, Rajasthan Technical UniversityJaipurIndia

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