There is ample evidence of deviations between the actual and planned interaction structures between a firm's IT department and business units. Such deviations can hinder senior managers from governing their IT organizations effectively because they do not know how
work really gets done. This paper develops an explanation for why actual structures differ from planned structures. Understanding this phenomenon is indispensable for managers to govern the real organization, to uphold compliance with important standards (e.g., ITIL, COBIT), to decide whether the formal or the actual organization is more effective, and, finally, to identify management actions that support the optimal structure. To develop this understanding, we analyze the interaction structures at the interface between firms' business units and IT units in four rich cases, using data from 56 interviews and 47 questionnaires, and applying qualitative methods and social network analysis, which give us deep insights into planned and actual interaction among employees. We test two different
explanations for deviations of actual from planned interaction structures and find that boundary-spanning theory provides the dominant explanation for such deviations: Inclined to span the business/IT boundary most effectively, the actors involved deviate from planned
structures especially when other structures offer better boundary-spanning potential, which is influenced primarily by cross-domain knowledge. In addition, relationships also play an important role. On the positive side, relationships provide opportunities for such deviations, while on the negative side, a conflict-laden relationship might hinder deviations even if they were advantageous.
We develop a transformation based on morphological filters
that measures the contrast of image texture. This ransforma
tion is proportional to texture contrast, but insensitive to its specific type. Though the transformation provides a high response in textured areas, it suppresses individual high contrast features that stand apart from textured areas. It can serve as an effective texture descriptor for unsupervised or supervised segmentation of textured regions, provides high accuracy of localization and does not involve heavy computations. The method is robust to variations of illumination and works on
different types of images without needing to be tuned. The only parameter is a scale related parameter. We illustrate the use of the proposed method on satellite and aerial images.
Recently, we introduced a morphological texture contrast (MTC) operator that allows detection of textural and non-texture regions in images. In this paper we provide comparison of the MTC with other available techniques. We show that, in contrast to other approaches, the MTC discriminates between texture details and isolated features, and does not extend borders of texture regions. Using the ideas underlying the MTC operator, we develop a complementary operator called morphological feature contrast (MFC) that allows extraction of isolated features while not being confused by texture details. We illustrate an application of the MFC operator for extraction of isolated bjects such as individual trees or buildings that should be distinguished from forests or urban centers. We furthermore provide an example of how this operator can be used for detection of isolated linear structures. We also derive an
extended version of the MFC that works with vector-valued images.
We introduce an approach for the detection of approximately rectangular structures in gray scale images. Our research is motivated by the Silvretta Historica project that aims at automated detection of remains of livestock
enclosures in remotely sensed images of alpine regions. The approach allows detection of enclosures with linear sides of various sizes and proportions. It is robust to incomplete or fragmented rectangles and tolerates deviations from a perfect rectangular shape. Morphological operators are used to extract linear features. They are grouped into parameterized linear segments by means of a local Hough transform. To identify appropriate configurations of linear segments we define convexity and angle constraints. Configurations meeting these constraints are rated by a proposed rectangularity measure that discards overly fragmented configurations and configurations with more than one side completely missing. The search for appropriate configurations is efficiently performed on a graph. Its nodes represent linear segments and edges encode the above constraints. We tested our approach on a set of aerial and GeoEye-1 satellite images of 0.5m resolution that contain ruined livestock enclosures of approximately rectangular shape. The approach showed encouraging results in finding configurations of linear segments originating from the objects of our interest.