The information organization and expression is a key issue for the Augmented Reality Maintenance Guiding System(ARMGS). In this paper, we firstly build up the relationship between the aircraft maintenance manual, EBOM figure and virtual prototype data , then construct a prototype of Interactive Electronic Technical Manual (IETM). The system can convert the aircraft virtual prototype data to the required information for augmented reality maintenance guiding system, based on the information, the prototype can be rapidly and lifelikely displayed. The system proposed in this paper significantly improves the quality and effectiveness of modeling.
People resonate more with music when exposed to visual information, and music enhances their perception of video content. Cross-modal recommendation techniques can be used to suggest appropriate background music for a given video. However, there is not a simple correspondence between the different modal data. Therefore, to explore the association between the two modalities of video and music, we propose MFF-VBMR, a video background music recommendation model based on multi-level fusion features. The model uses the cross-modal information of static, dynamic and emotional content of video and music to realize the task of matching and recommending suitable background music for a given video. We propose a feature normalized convolutional similarity algorithm network FNC, which takes into account the pairwise similarity of visual and acoustic regions without losing region details. Experimental results show that the proposed model outperforms other existing models in terms of performance and achieves satisfactory results for video background music recommendation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.