Ricerc@Sapienza

Forward-Looking Sonar (FLS) is one of the most effective devices for underwater exploration which provides high-resolution images that can be used for several tasks in marine research, oceanographic, and deep-sea exploration. The limitation of current underwater acoustic channels does not allow transmitting these images in real-time, therefore image compression is required. Since acoustic images are characterized by speckle noise, an important challenge, in this area, is how to perform the compression while preserving relevant information.

The availability of a background model that describes the scene is a prerequisite for many computer vision applications. In several situations, the model cannot be easily generated when the background contains some foreground objects (i.e., bootstrapping problem). In this letter, an Adaptive Bootstrapping Management (ABM) method, based on keypoint clustering, is proposed to model the background on video sequences acquired by mobile and static cameras.

Detection of moving objects is a topic of great interest in computer vision. This task represents a prerequisite
for more complex duties, such as classification and re-identification. One of the main challenges regards the
management of dynamic factors, with particular reference to bootstrapping and illumination change issues.
The recent widespread of PTZ cameras has made these issues even more complex in terms of performance due
to their composite movements (i.e., pan, tilt, and zoom). This paper proposes a combined keypoint clustering