Negative Effects of Relative Proximity and Absolute Geography on Open Innovation Practices in High-tech SMEs in the UK

© 2014 IEEE. This exploratory study addresses a gap in management literature by addressing the role of location in the continuously expanding field of open innovation research. In this context, we analyze potential negative effects of absolute geography and relative proximity on open innovation practices in high-tech small and medium-sized enterprises (SMEs) in the United Kingdom. Drawing upon cluster theory and business ecosystem literature, the analysis from three SME case studies in the East of England suggests that presumed 'favorable' location variables, such as close relative proximity between partners and the presence of economic clusters, can have certain negative effects on open innovation practices.

Towards a complete dense geometric and photometric reconstruction under varying pose and illumination

This paper proposes a novel framework to construct a geometric and photometric model of a viewed object that can be used for visualisation in arbitrary pose and illumination. The method is solely based on images and does not require any specialised equipment. We assume that the object has a piece-wise smooth surface and that its reflectance can be modelled using a parametric bidirectional reflectance distribution function. Without assuming any prior knowledge on the object, geometry and reflectance have to be estimated simultaneously and occlusion and shadows have to be treated consistently. We exploit the geometric and photometric consistency using the fact that surface orientation and reflectance are local invariants. In a first implementation, we demonstrate the method using a Lambertian object placed on a turn-table and illuminated by a number of unknown point light-sources. A discrete voxel model is initialised to the visual hull and voxels identified as inconsistent with the invariants are removed iteratively. The resulting model is used to render images in novel pose and illumination. © 2004 Elsevier B.V. All rights reserved.

Computer Vision and Pattern Recognition Technologies for Construction

This book will be of particular interest to academics, researchers, and graduate students at universities and industrial practitioners seeking to apply mobile and pervasive computing systems to improve construction industry productivity.