Dentistry is a particularly complex and sophisticated applied science; many problems have to be solved by analyzing intensive longitudinal data. For example, dynamic carious lesion assessment requires dental researchers to perform knowledge discovery in a situation with multiple specimens across different experimental phases. The technological development and availability of cyberinfrastructure today can enable dental researchers to perform existing procedures far faster and more accurately than ever. This paper uses dynamic carious lesion activity assessment as a case study, to illustrate how visual computing on advanced cyberinfrastructure can expand beyond statistical number crunching and information retrieval to make an imaginative and creative contribution to some aspects of dental science. Our work focuses on the generation of BIG pictures on cyberinfrastructure and the presentation of derived dental structures in an interactive means, which combine to allow researchers to navigate from observation to qualitative discovery and then to quantitative assessment with multiple variables and degrees of freedom. Our work has seen early use by our collaborators in oral health research, where our system has been used to pose and answer domain-specific questions for quantitative assessment of dynamic carious lesion activities.

In this paper, we address the challenging problem of single-view cross-season place recognition. A new approach is proposed for compact discriminative scene descriptor that helps in coping with changes in appearance in the environment. We focus on a simple effective strategy that uses objects whose appearance remain the same across seasons as valid landmarks. Unlike popular bag-of-words (BoW) scene descriptors that rely on a library of vector quantized visual features, our descriptor is based on a library of raw image data (e.g., visual experience shared by colleague robots, publicly available photo collections from Google StreetView), and directly mines it to identify landmarks (i.e., image patches) that effectively explain an input query/database image. The discovered landmarks are then compactly described by their pose and shape (i.e., library image ID, and bounding boxes) and used as a compact discriminative scene descriptor for the input image. We collected a dataset of single-view images across seasons with annotated ground truth, and evaluated the effectiveness of our scene description framework by comparing its performance to that of previous BoW approaches, and by applying an advanced Naive Bayes Nearest neighbor (NBNN) image-to-class distance measure.

We propose a discriminative and compact scene descriptor for single-view place recognition that facilitates long-term visual SLAM in familiar, semi-dynamic and partially changing environments. In contrast to popular bag-of-words scene descriptors, which rely on a library of vector quantized visual features, our proposed scene descriptor is based on a library of raw image data (such as an available visual experience, images shared by other colleague robots, and publicly available image data on the web) and directly mine it to find visual phrases (VPs) that discriminatively and compactly explain an input query / database image. Our mining approach is motivated by recent success in the field of common pattern discovery-specifically mining of common visual patterns among scenes-and requires only a single library of raw images that can be acquired at different time or day. Experimental results show that even though our scene descriptor is significantly more compact than conventional descriptors it has a relatively higher recognition performance.