Dealing with an ever increasing amount of data is a challenge that military intelligence analysts or team of analysts face
day to day. Increased individual and collective comprehension goes through collaboration between people. Better is the
collaboration, better will be the comprehension. Nowadays, various technologies support and enhance collaboration by
allowing people to connect and collaborate in settings as varied as across mobile devices, over networked computers,
display walls, tabletop surfaces, to name just a few. A powerful collaboration system includes traditional and multimodal
visualization features to achieve effective human communication. Interactive visualization strengthens
collaboration because this approach is conducive to incrementally building a mental assessment of the data meaning. The
purpose of this paper is to present an overview of the envisioned collaboration architecture and the interactive
visualization concepts underlying the Sensemaking Support System prototype developed to support analysts in the
context of the Joint Intelligence Collection and Analysis Capability project at DRDC Valcartier. It presents the current
version of the architecture, discusses future capabilities to help analyst(s) in the accomplishment of their tasks and
finally recommends collaboration and visualization technologies allowing to go a step further both as individual and as a
Computer vision experiments are not very often linked to practical applications but rather deal with typical laboratory experiments under controlled conditions. For instance, most object recognition experiments are based on specific models used under limitative constraints. Our work proposes a general framework for rapidly locating significant 3D objects in 2D static images of medium to high complexity, as a prerequisite step to recognition and interpretation when no a priori knowledge of the contents of the scene is assumed. In this paper, a definition of generic objects is proposed, covering the structures that are implied in the image. Under this framework, it must be possible to locate generic objects and assign a significance figure to each one from any image fed to the system. The most significant structure in a given image becomes the focus of interest of the system determining subsequent tasks (like subsequent robot moves, image acquisitions and processing). A survey of existing strategies for locating 3D objects in 2D images is first presented and our approach is defined relative to these strategies. Perceptual grouping paradigms leading to the structural organization of the components of an image are at the core of our approach.
A computer vision approach for the extraction of feature points on 3D images of dental imprints is presented. The position of feature points are needed for the measurement of a set of parameters for automatic diagnosis of malocclusion problems in orthodontics. The system for the acquisition of the 3D profile of the imprint, the procedure for the detection of the interstices between teeth, and the approach for the identification of the type of tooth are described, as well as the algorithm for the reconstruction of the surface of each type of tooth. A new approach for the detection of feature points, called the watershed algorithm, is described in detail. The algorithm is a two-stage procedure which tracks the position of local minima at four different scales and produces a final map of the position of the minima. Experimental results of the application of the watershed algorithm on actual 3D images of dental imprints are presented for molars, premolars and canines. The segmentation approach for the analysis of the shape of incisors is also described in detail.
This paper presents a computer vision system for the acquisition and processing of 3-D images of wax dental imprints. The ultimate goal of the system is to measure a set of 10 orthodontic parameters that will be fed to an expert system for automatic diagnosis of occlusion problems. An approach for the acquisition of range images of both sides of the imprint is presented. Range is obtained from a shape-from-absorption technique applied to a pair of grey-level images obtained at two different wavelengths. The accuracy of the range values is improved using sensor fusion between the initial range image and a reflectance image from the pair of grey-level images. The improved range image is segmented in order to find the interstices between teeth and, following further processing, the type of each tooth on the profile. Once each tooth has been identified, its accurate location on the imprint is found using a region- growing approach and its shape is reconstructed with third degree polynomial functions. The reconstructed shape will be later used by the system to find specific features that are needed to estimate the orthodontic parameters.