Abstract The ability to leverage data science can generate valuable insights and actions in organisations by enhancing data‐driven decision‐making to find optimal solutions based on complex business parameters and data. However, only a small percentage of the organisations can successfully obtain a business value from their investments due to a lack of organisational management, alignment, and culture. Becoming a data‐driven organisation requires an organisational change that should be managed and fostered from a holistic multidisciplinary perspective. Accordingly, this study seeks to address these problems by developing the Data Drivenness Process Capability Determination Model (DDPCDM) based on the ISO/IEC 330xx family of standards. The proposed model enables organisations to determine their current management capabilities, derivation of a gap analysis, and the creation of a comprehensive roadmap for improvement in a structured and standardised way. DDPCDM comprises two main dimensions: process and capability. The process dimension consists of five organisational management processes: change management, skill and talent management, strategic alignment, organisational learning, and sponsorship and portfolio management. The capability dimension embraces six levels, from incomplete to innovating. The applicability and usability of DDPCDM are also evaluated by conducting a multiple‐case study in two organisations. The results reveal that the proposed model is able to evaluate the strengths and weaknesses of an organisation in adopting, managing, and fostering the transition to a data‐driven organisation and providing a roadmap for continuously improving the data‐drivenness of organisations.

We propose a new bi-directional 2-D mesh representation of video objects, which utilizes multiple keyframes with forward and backward tracking. Experimental results on use of this representation for video object tracking in the presence of self occlusion are presented.

With the advent of MPEG-4, object-based natural/synthetic hybrid multimedia content is becoming more ubiquitous. In this paper, we address object-based interactive analysis of natural video for editing/authoring natural/synthetic hybrid content. Boundary and local motion of video objects are described by snake and 2-D mesh representations, respectively. The 2-D mesh modeling in effect performs a mapping of a natural video object into a computer graphics representation, namely geometry with motion and a texture map; thus allowing for easy editing of natural video objects using tools already developed in computer graphics. This paper presents the components of a tool designed for interactive video object analysis and editing using graphical models whose syntax and semantics conform with VRML and MPEG-4 standards. The analysis tool is developed using Java and C++, while rendering and editing are performed within VRML or MPEG-4 browsers and authoring tools. Demonstrations of the video analysis and editing are included.

This paper presents pixel-based and region-based parametric motion segmentation methods for robust motion segmentation with the goal of aligning motion boundaries with those of real objects in a scene. We first describe a two-step iterative procedure for parametric motion segmentation by either motion-vector or motion-compensated intensity matching. We next present a region-based extension of this method, whereby all pixels within a predefined spatial region are assigned the same motion label. These predefined regions may be fixed- or variable-size blocks or arbitrary-shaped areas defined by color or texture uniformity. A particular combination of these pixel-based and region-based methods is then proposed as a complete algorithm to obtain the best possible segmentation results on a variety of image sequences. Experimental results showing the benefits of the proposed scheme are provided.

This paper presents region-based affine motion parameter clustering methods by motion-vector and intensity matching to provide improved robustness and alignment of motion boundaries with real object boundaries. Regions may be formed as fixed- or variable-size rectangular blocks, triangular patches, or arbitrary-shaped areas defined by color or texture uniformity. A particular combination of these affine clustering methods is then proposed to obtain the best segmentation results on a variety of image sequences. Experimental results showing the benefits of the proposed scheme are provided.

We propose a video manipulation formalism that includes several special effects authoring tools such as animation, transfiguration, and augmented reality, based on a 2-D mesh-based video object representation. Texture maps of video objects are registered on a reference mesh, and then transfigured (rendered) by warping them using 2-D mesh mappings that are obtained by tracking the original sequence of video objects. The formalism also allows alpha blending of a moving object with another object using an alpha map sequence and a moving texture map. A VRML browser is used as an interactive user-interface to implement these manipulation tools. A Web demo is also available.

This paper deals with manipulation of natural video objects using graphical models and composition of processed objects within a VRML browser. In particular, 2-D mesh-based representation of video objects and resulting functionalities are integrated within VRML 2.0 for real-time rendering using IndexedFaceSet and CoordinateInterpolator nodes of VRML 2.0. A software demonstration of this representation and functionalities provided is shown.

We propose a robust, object-based approach to high-resolution image reconstruction from video using the projections onto convex sets (POCS) framework. The proposed method employs a validity map and/or a segmentation map. The validity map disables projections based on observations with inaccurate motion information for robust reconstruction in the presence of motion estimation errors; while the segmentation map enables object-based processing where more accurate motion models can be utilized to improve the quality of the reconstructed image. Procedures for the computation of the validity map and segmentation map are presented. Experimental results demonstrate the improvement in image quality that can be achieved by the proposed methods.

We propose a region-based approach to high-resolution image reconstruction from video using the projections onto convex sets framework. The region-based formalism allows the use of a validity map and/or a segmentation map. The validity map disables projections based on observations with inaccurate motion information, for robust reconstruction in the presence of motion estimation errors; while the segmentation map enables object-based processing where more accurate motion models can be utilized to improve the reconstruction quality of image object. A procedure for the computation of the validity map is presented. Experimental results demonstrate the quality improvement that can be achieved by the proposed region-based approach.