Collaborative Collective Algorithms to Coordinate UGVs

Sentel/Brilliant Innovations has developed autonomous UGVs (unmanned ground vehicles) capable of generating a map of an unknown location through exploration using local software and the power of Google Tango technology. This project was tasked with developing an efficient and capable map-stitching solution allowing multiple UGVs to coordinate their movements and share information in order to greatly improve the speed at which these drones can be used to generate maps. The solution utilizes the processing power of a Raspberry Pi to pull maps from a Redis server and stitch them together. Once stitched, the maps are redistributed via the Redis server back through the network, providing every UGV the opportunity to obtain the global map. All of this stitching is performed on a single UGV, freeing the other drones to focus on generating and uploading their own unique maps to the server. The drones can use this new information to better inform their next move to prevent multiple drones from generating a map of the same location. In the future, Sentel/Brilliant Innovations hopes to take this technology and attach more advanced sensors to the drones, allowing them to add greater detail of the environment to the map rather than simply drawing boundaries. These drones have many potential applications, such as search and rescue, seeking out potential hazards, and intelligence for military and civil use.https://scholarscompass.vcu.edu/capstone/1187/thumbnail.jp

Automatic facial analysis for objective assessment of facial paralysis

Facial Paralysis is a condition causing decreased movement on one side of the face. A quantitative, objective and reliable assessment system would be an invaluable tool for clinicians treating patients with this condition. This paper presents an approach based on the automatic analysis of patient video data. Facial feature localization and facial movement detection methods are discussed. An algorithm is presented to process the optical flow data to obtain the motion features in the relevant facial regions. Three classification methods are applied to provide quantitative evaluations of regional facial nerve function and the overall facial nerve function based on the House-Brackmann Scale. Experiments show the Radial Basis Function (RBF) Neural Network to have superior performance

Pictures in words : indexing, folksonomy and representation of subject content in historic photographs

Subject access to images is a major issue for image collections. Research is needed to understand how indexing and tagging contribute to make the subjects of historic photographs accessible. This thesis firstly investigates the evidence of cognitive dissonance between indexers and users in the way they attribute subjects to historic photographs, and, secondly, how indexers and users might work together to enhance subject description. It analyses how current indexing and social tagging represent the subject content of historic photographs. It also suggests a practical way indexers can work with taggers to deal with the classic problem of resource constraints and to enhance metadata to make photo collections more accessible. In an original application of the Shatford/Panofsky classification matrix within the applications domain of historic images, patterns of subject attribution are explored between taggers and professional indexers. The study was conducted in two stages. The first stage (Studies A to D) investigated how professional indexers and taggers represent the subject content of historic photographs and revealed differences based on Shatford/Panofsky. The indexers (Study A) demonstrated a propensity for specific and generic subjects and almost complete avoidance of abstracts. In contrast, a pilot study with users (Study B) and with baseline taggers (Studies C and D) showed their propensity for generics and equal inclination to specifics and abstracts. The evidence supports the conclusion that indexers and users approach the subject content of historic photographs differently, demonstrating cognitive dissonance, a conflict between how they appear to think about and interpret images. The second stage (Study E) demonstrated that an online training intervention affected tagging behaviour. The intervention resulted in increased tagging and fuller representation of all subject facets according to the Shatford/Panofsky classification matrix. The evidence showed that trained taggers tagged more generic and abstract facets than untrained taggers. Importantly, this suggests that training supports the annotation of the higher levels of subject content and so potentially provides enhanced intellectual access. The research demonstrated a practical way institutions can work with taggers to extend the representation of subject content in historic photographs. Improved subject description is critical for intellectual access and retrieval in the cultural heritage space. Through systematic application of the training method a richer corpus of descriptors might be created that enhances machine based information retrieval via automatic extraction