Overlaying rendered virtual annotations on top of the camera view of the real world requires intensive use of computer vision paradigms for object recognition and tracking. This involves computationally intensive tasks and the availability of large-scale databases of ref-erence images. In some domains, a lack of reference images may be particularly disruptive. For example, with wine bottles, labels may not be available because (a) periodically changed by the winery, and (b) specific bottles may belong to the long tail, making label retrieval difficult or even impossible. In the following, we present Augmented Wine Recognition (AWR), a system that does not require any reference image optimized to perform an augmented reading of wine labels.
Stacchio L., Angeli A., Donatiello L., Giacche A., Marfia G. (2022). Rethinking Augmented Wine Recognition. 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA : Institute of Electrical and Electronics Engineers Inc. [10.1109/ISMAR-Adjunct57072.2022.00117].
Rethinking Augmented Wine Recognition
Stacchio L.;Angeli A.;Donatiello L.;Marfia G.
2022
Abstract
Overlaying rendered virtual annotations on top of the camera view of the real world requires intensive use of computer vision paradigms for object recognition and tracking. This involves computationally intensive tasks and the availability of large-scale databases of ref-erence images. In some domains, a lack of reference images may be particularly disruptive. For example, with wine bottles, labels may not be available because (a) periodically changed by the winery, and (b) specific bottles may belong to the long tail, making label retrieval difficult or even impossible. In the following, we present Augmented Wine Recognition (AWR), a system that does not require any reference image optimized to perform an augmented reading of wine labels.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
