Matlab Implementation Results: Detection and Counting of Young Larvae and Juvenile by Image Enhancement and Region Growing Segmentation Approach
Valliappan Raman1, Sundresan Perumal2
1Dr.Valliappan Raman, is with the MRG Lab, Universiti Sains Malaysia, Penang, Malaysia.
2Dr. Sundresan Perumal, is with the faculty of science and technology, Universiti Sains Islamic Malaysia.
Manuscript received on April 16, 2015. | Revised Manuscript received on April 27, 2015. | Manuscript published on March 05, 2015. | PP: 57-65 | Volume-5, Issue-2, May 2015. | Retrieval Number: B2582055215/2015©BEIESP
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©The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: This paper describes techniques to perform efficient and accurate recognition in larvae images captured from the hatcheries for counting the live and dead larvae’s. In order to accurately model small, irregularly shaped larvae and juvenile, the larvae images are enhanced by three enhancement methods, and segmentation of larvae and juvenile is performed by orientation associated with each edge pixel of region growing segmentation method. The two vital tasks in image analysis are recognition and extraction of larvae and juvenile from an image. When these tasks are manually performed, it calls for human experts, making them more time consuming, more expensive and highly constrained. These negative factors led to the development of various computer systems performing an automatic recognition and extraction of visual information to bring consistency, efficiency and accuracy in image analysis. This main objective of this paper is to study on the various existing automated approaches for recognition and extraction of objects from an image in various scientific and engineering applications. In this study, a categorization is made based on the four principle factors (Input, Segment the larvae, Recognition, Counting) with which each approach is drive .The achieved result of recognition and classification of larvae is around 85%. All the results achieved through matlab implementation are discussed in this paper are proved to work efficiently in real environment.
Keywords: Enhancement, Segmentation and Counting.