It is clear that much of the world's technological innovation originates from the domain of the startup, an arena in which the United States has historically played a major role. However, while business and other schools have traditionally offered courses in entrepreneurship, only about one third of all new businesses ever reach profitability and less than half of all new startups survive more than five years. Much of the attrition of small businesses is a result of poor preparation of the founders and the failure of academia to treat entrepreneurship as something more than a purely academic subject. A new program at Hofstra University focuses on a fresh approach to preparing Computer Science and Engineering students in entrepreneurship by providing a curriculum specifically designed to meet the myriad challenges encountered by entrepreneurs in the “real” world.

Entrepreneurship is an integral part of the computing education at Hofstra University and consists of: (i) two optional, specialization-type programs as part of undergraduate degrees that target students with strong entrepreneurial interests, and (ii) open seminars and enrichment modules, exposing all majors to entrepreneurial ideas and activities while attempting to spark their creativity and drive for innovation. To educate students and help them understand the purpose, procedures, cost and legal implications of patenting and intellectual property issues and to give them first-hand experience in these subjects, we have included “patents and intellectual property” as one of the facets of the new programs and activities. In this paper, we present the implementation details and we summarize the experiences and practices and draw conclusions. Adoption of modules at Qatar University illustrates the generality and transferability of the approach.

Numerous applications processing 3D point data will gain from the ability to estimate reliably normals and differential geometric properties. Normal estimates are notoriously noisy, the errors propagate and may lead to flawed, inaccurate, and inconsistent curvature estimates. Frankot-Chellappa introduced the use of integrability constraints in normal estimation. Their approach deals with graphs z = f(x,y)- We present a newly discovered general orientability constraint (GOC) for 3D point clouds sampled from general surfaces, not just graphs. It provides a tool to quantify the confidence in the estimation of normals, topology, and geometry from a point cloud. Furthermore, similarly to the Frankot-Chellappa constraint, the GOC can be used directly to extract the topology and the geometry of the manifolds underlying 3D point clouds. As an illustration we describe an automatic Cloud-to-Geometry pipeline which exploits the GOC.

We present new theoretical results which have implications in answering one of the fundamental questions in computer vision: recognition of surfaces and surface shapes. We state the conditions under which: (i) a surface can be recovered, uniquely, from the tangent plane map, in particular from the Gauss map; (ii) a surface shape can be recovered from the metric and the deforming forces. In case where such conditions are not satisfied we classify all exceptions, i.e. the surfaces and surface shapes for which the recovery and registration problems are ill-posed.

We describe an estimation technique which, given a measurement of the depth of a target from a wide-field-of-view (WFOV) stereo camera pair, produces a minimax risk fixed-size confidence interval estimate for the target depth. This work constitutes the first application to the computer vision domain of optimal fixed-size confidence-interval decision theory. The approach is evaluated in terms of theoretical capture probability and empirical capture frequency during actual experiments with a target on an optical bench. The method is compared to several other procedures including the Kalman Filter. The minimax approach is found to dominate all the other methods in performance. In particular for the minimax approach, a very close agreement is achieved between theoretical capture probability and empirical capture frequency. This allows performance to be accurately predicted, greatly facilitating the system design, and delineating the tasks that may be performed with a given system.

In this paper we address an application of computer vision which can in the future change completely our way of communicating over the network. We present our version of a testbed for telecollaboration. It is based on a highly accurate and precise stereo algorithm. The results demonstrate the live (on-line) recovery of 3D models of a dynamically changing environment and the simultaneous display and manipulation of the models.