The Interplay among Organisational Learning Culture, Agility, Growth and Big Data Capabilities

This paper examines how an organisational learning culture impacts organisational agility by developing a model based on dynamic capabilities. The model treats agility as a dynamic capability and explains how an organisational learning culture (OLC) triggers a chain reaction through its influence on organisational agility (OA) that ultimately results in company growth. This paper also investigates the role of big data capabilities in transferring learning outcomes into dynamic capabilities. The model is tested through data collected from a survey of 138 Australian companies. Partial least squares structural equation modeling is adopted to empirically demonstrate how agility fully mediates the impact of the learning culture on growth. In addition, this paper further sheds light on the moderating role of big data competencies on the effects of OLC on OA. After presenting the results with implications to theory and practice, the paper ends with suggestions for future studies

Moving Visual Representations of Video Objects for Content-Based Search and Browsing*

This paper proposes object-based moving visual representations for quick browsing of video content. These representations are hierarchical, such that at the coarse level a sequence of alpha planes provides a moving representation of object shape and motion information for object contours. Alternatively, a 2D mesh representation provides a complete visual rep-resentation of object motion and shape. The finest level visual representation can be obtained by texture mapping onto the moving meshes. The paper also dis-cusses trade-offs between each representation in terms of the amount of indexing information that needs to be stored, the robustness of the representation, and the accuracy of the representation.

Robust Watermarking Of Busy Images

This work extends the watermarking method proposed by Kutter et al. [1] to increase the watermark decoding performance for textured or busy images. The proposed algorithm modifies watermark embedding rule to utilize image characteristics, like local standard deviation and gradient magnitude, in order to increase the decoding accuracy for busy images. The method does not need original image for decoding and controls the watermark embedding process at encoder site, resulting in a more accurate decoding