Abstract
In this paper, we propose a multi-sensor fusion model using an autoencoder neural network for 3D object recognition, which fuses multiple sensory data to integrate its internal object representation. This model was evaluated using camera images from many viewpoints on a hemisphere around the target. Three images were generated from one camera image by hue and saturation value clusters. After learning the target's images from many viewpoints in an autoencoder neural network, the continuous internal representations which correspond to viewpoints, were constructed in a compress layer of the autoencoder neural network. We found that the internal representation is generalized about the viewpoints which were not in the training sets of the target. The average of the squared errors of the autoencoder neural network is about three times higher when the compared object is unknown than when the object has already been taught as the target but not the learning point. Results of the experiment demonstrate the effectiveness of our proposed model to 3D object recognition.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
