Pubblicazione scientifica

Segmentation of Aquaculture Underwater Scene Images based on SLIC Superpixels Merging-Fast Marching Method Hybrid

FILONE TECNOLOGICO	TEMA
economia circolare e sostenibilità

RIASSUNTO

Segmentation is a challenging task for the complex and low-quality underwater images, as this is prerequisite to advanced tasks in fish monitoring such as fish detection and classification. A demand exists for underwater image segmentation algorithms that can robustly segment fish from its background. A competitive approach is the integration of states-of-the-art image segmentation algorithms: SLIC superpixels merging by KAZE Keypoints clustering and Fast Marching Method (FMM) to a single framework. The combination of these established methods offers robustness towards underwater images of different visual qualities. First, a locally acquired underwater image is represented as superpixels. Then, the KAZE features of an underwater image is extracted. Such features are utilized by the k-means clustering to group superpixels which contains fish pixels into a region. Lastly, the merged region is further segmented with Fast Marching Method and corresponding morphological processes. The study presents the viability of the integration of different image segmentation techniques for localized application. The number of superpixels, KAZE Keypoint score threshold and FMM threshold are identified to affect the performance of the proposed algorithm. Qualitative observations and quantitative measures validate the robustness of this generated algorithm to address this difficult and persistent task.

DATA

Data di pubblicazione: 16/11/2020

AUTORI

VINCENT JAN D ALMERO
JONNEL D ALEJANDRINO
ARGEL A BANDALA
ELMER P DADIOS

ENTE DI AFFERENZA

DE LA SALLE UNIVERSITYELECTRONICS & COMM ENG DEPARTMENTMANILAPHILIPPINES
DE LA SALLE UNIVERSITYMANUFACTURING ENG & MGMT DEPARTMENTMANILAPHILIPPINES

RIVISTA

2020 IEEE REGION 10 CONFERENCE (TENCON) (Page(s): 432-437)

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