Underwater Imaging, Histogram Equalization & Algorithms
This paper describes about the underwater computer vision. Underwater image processing is in general a challenging task, due to its environment, poor sunlight and the turbidity in itself. Optical, sonar and ultrasound images are captured from the underwater environment. Often optical cameras seem to be a good choice, especially in the case of underwater species identification or counting, coral reefs, pipeline monitoring, mining etc. Underwater images captured from such cameras are poor in contrast, blurred and often contain noise due to the flora and fauna floating in the water. The visibility is limited due to the fact that when light enters the water it is exponentially attenuated. In some cases, underwater images are captured in very low illumination such that object detection itself becomes a challenging task. For accurate object recognition, underwater images must undergo initial pre-processing. This preprocessing must include image enhancement and de- noising as the image suffers from poor contrast, non-uniform lighting, blurring etc. The extracted features must undergo an Artificial Intelligence algorithm for classification. Such automation is often required, as ocean floor is monitored continuously for various applications and manual identification will not help. Artificial neural networks, which exhibit capabilities for adaptation, was used for classifying the objects of interest underwater. The learning algorithms were designed such that features were trained and back propagated to the hidden neurons, until the error was minimized. A stepwise feature selection process was used to determine the subset of features that will optimize the probability of detection and classification. This resulted in accurate object recognition with 96% classification accuracy.
Article Details
Unique Paper ID: 151541

Publication Volume & Issue: Volume 8, Issue 1

Page(s): 225 - 235
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