Underwater image processing method for fish localization and detection in submarine environment
| Autor: | Mohcine Boudhane, Benayad Nsiri |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image processing 02 engineering and technology 01 natural sciences Object-class detection Digital image 0202 electrical engineering electronic engineering information engineering Media Technology Computer vision Mean-shift Electrical and Electronic Engineering 0105 earth and related environmental sciences Feature detection (computer vision) 010505 oceanography business.industry Pattern recognition Image segmentation Object detection Computer Science::Computer Vision and Pattern Recognition Signal Processing 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Noise (video) Artificial intelligence business |
| Zdroj: | Journal of Visual Communication and Image Representation. 39:226-238 |
| ISSN: | 1047-3203 |
| DOI: | 10.1016/j.jvcir.2016.05.017 |
| Popis: | A new underwater image preprocessing method for underwater detection is proposed.This method consists of three procedures. One is image denoising, another is image segmentation via mean-shift algorithm, and the other is log likelihood ratio test.Poisson-Gauss mixture algorithm is proposed for noise reduction.Log-Likelihood ratio test is applied for robust fish detection.Experimental results outperform the state of the art methods. Object detection is an important process in image processing, it aims to detect instances of semantic objects of a certain class in digital images and videos. Object detection has applications in many areas of computer vision such as underwater fish detection. In this paper we present a method for preprocessing and fish localization in underwater images. We are based on a Poisson-Gauss theory, because it can accurately describe the noise present in a large variety of imaging systems. In the preprocessing step we denoise and restore the raw images. These images are split into regions utilizing the mean shift algorithm. For each region, statistical estimation is done independently in order to combine regions into objects. The method is tested under different underwater conditions. Experimental results show that the proposed approach outperforms state of the art methods. |
| Databáze: | OpenAIRE |
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