Title of article :
Image registration for the underwater inspection using the maximum a posteriori technique
Author/Authors :
Guo، Jenhwa نويسنده , , Cheng، Sheng-Wen نويسنده , , Ying، Cheng-Yang نويسنده , , Liu، Te-Chih نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2003
Abstract :
This work describes an image registration method for underwater inspection tasks. A remotely operated vehicle equipped with a video camera and a scanning sonar is used as the testbed vehicle. Each image of the underwater scene is saved along with the video cameraʹs position and orientation. The images are then combined to create a large composite picture of the underwater structure being inspected. This method is based upon a maximum a posteriori estimation technique and provides smooth and robust estimates of image shifts. Our results demonstrate the feasibility of this highly promising underwater inspection procedure.
Keywords :
neural-network modularity , two-hidden-layer feedforward networks (TLFNs) , Storage capacity , Learning capability
Journal title :
IEEE Journal of Oceanic Engineering
Journal title :
IEEE Journal of Oceanic Engineering