Stereo image segmentation with application in underwater fish detection and tracking

摘要

Most often, background subtraction and image segmentation methods use images or video captured using a single camera. However, segmentation can be improved using stereo images by reducing errors caused due to illumination fluctuations and object occlusion. This work proposes a background subtraction and image segmentation method for images obtained using a two camera stereo system. Stereo imaging is often employed in order to obtain depth information. On the other hand, the objective of this work is mainly to extract accurate boundaries of objects from stereo images, which are otherwise difficult to obtain. Improving the outline detection accuracy is vital for object recognition applications. An application of the proposed technique is presented for the detection and tracking of fish in underwater image sequences. Outline fish detection is a challenging task since fish are not rigid objects. Moreover, color is not necessarily a reliable means to segment underwater images, therefore, grayscale images are used. Due to these two reasons, and due to the fact that underwater images captured in non-controlled environments are often blurry and poorly illuminated, commonly used local correlation methods are not sufficient for stereo image matching. The proposed algorithm improves segmentation in several scenarios including cases where fish are occluded by other fish regions. Although the work concentrates on segmenting fish images, it can be employed in other underwater image segmentation applications where visible-light cameras are used.