Segmentation of 3D acoustic images for object recognition purposes

This paper addresses the problem of segmenting 3D acoustic images for object recognition purposes. For remotely operated vehicle (ROV) navigation the 3D surroundings have to be understood. Sensors, such as 3D acoustic imaging sensors, can be used for this purpose. Automatic segmentation and reconstruction of an underwater scene could make many underwater operations more effective and reliable. The characteristics of 3D acoustic images are different from conventional optical or range images and simple standard segmentation methods developed for optical imaging or range images are often rendered more or less useless. 3D acoustic images have usually got low resolution and exhibit characteristics inherent to acoustical imaging, such as speckle noise and target shadows. The main advantages are that the acoustic imaging sensor is functional even in turbid water conditions and is capable of providing 3D (range) information. A literature review of work in the field of acoustic image segmentation is given, summarising the state-of-the-art in acoustic image segmentation approaches for object recognition purposes. Four different approaches, thresholding, fuzzy clustering, Markov random fields and connected components approach, are implemented and tested on synthetic and real 3D acoustic images