Morphological Reconstruction of Semantic Layers in Map Images

Map images are composed of semantic layers depicted in arbitrary color. Often images require separation into layers for storage and processing. Separation results in severe artifacts because of layer overlapping. In the current work, we design the technique to restore semantic layers after separation. The designed restoration technique alleviates compression deficiency of reconstructed layers versus corrupted ones with lossless compression algorithms (ITU Group 4, PNG, JBIG and IBIG2 with optimized context templates), and provides better visual quality of images in operations with selective layer removal/extraction

The role of images in social media analytics: A multimodal digital humanities approach

The analysis of social media data is an emerging research field that aims to study the dynamics of urban life. In this study, we adopt a multimodal digital humanities approach to combine the analysis of text-based social media data with visual social media data in an interactive map to investigate urban life in Singapore from a social semiotic perspective. Twitter is used as a source of user-defined localised textual data, Instagram as a source of localised user-generated images and Foursquare as a source of user-defined location-based information where is semantically organised according to Wikipedia's classification tree. In this way, we track the multimodal content of social media according to semantically organised location-based sources. This study suggests that users of Twitter express emotion about their own lives and the world around them, but these linguistic resources are differentially deployed according to venue. However, this is less variation in the use of photos to construe personal relationships, suggesting that photos fulfil and intrinsic need to be observed which transcends the nature of the social practice which is taking place. It is envisaged that the role of the visual will continue to expand as digital technologies refashion and transform out semiotic world.status: publishe

Morphological Reconstruction of Semantic Layers in Map Images

Map images are composed of semantic layers depicted in arbitrary color. Color separation is often needed to divide the image into layers for storage and processing. Separation can result in severe artifacts because of the overlapping of the layers. In this work, we introduce a technique to restore the original semantic layers after the color separation. The proposed restoration technique improves compression performance of the reconstructed layers in comparison to the corrupted ones when compressed by lossless algorithms such as International Communication Unit (ITU) Group 4 (TIFF G4), Portable Network Graphics (PNG), Joint Bi-level Image experts Group (JBIG), and context tree method. The resulting technique also provides good visual quality of the reconstructed image layers, and can therefore be applied for selective layer removal/ extraction in other map processing applications, e.g., area measurement. 2006 SPIE and IS&T. #DOI: 10.1117/1.2178188#

Interactive software for multimodal analysis

10.1177/1470357212446414Visual Communication113363-38