Development and validation of an instrument to measure organizational cultures' support of business process management

The purpose of Business Process Management (BPM) is to increase the efficiency and effectiveness of organizational processes through improvement and innovation. Despite a common understanding that culture is an important element in these efforts, there is a dearth of theoretical and empirical research on culture as a facilitator of successful BPM. We develop the BPM culture construct and propose a validated instrument with which to measure organizational cultures’ support of BPM. The operationalization of the BPM culture concept provides a theoretical foundation for future research and a tool to assist organizations in developing a cultural environment that supports successful BPM.

Distributed low-power image processing in wireless sensor networks for intelligent video surveillance applications

Distributed Wireless Smart Camera (DWSC) network is a special type of Wireless Sensor Network (WSN) that processes captured images in a distributed manner. While image processing on DWSCs sees a great potential for growth, with its applications possessing a vast practical application domain such as security surveillance and health care, it suffers from tremendous constraints. In addition to the limitations of conventional WSNs, image processing on DWSCs requires more computational power, bandwidth and energy that presents significant challenges for large scale deployments. This dissertation has developed a number of algorithms that are highly scalable, portable, energy efficient and performance efficient, with considerations of practical constraints imposed by the hardware and the nature of WSN. More specifically, these algorithms tackle the problems of multi-object tracking and localisation in distributed wireless smart camera net- works and optimal camera configuration determination. Addressing the first problem of multi-object tracking and localisation requires solving a large array of sub-problems. The sub-problems that are discussed in this dissertation are calibration of internal parameters, multi-camera calibration for localisation and object handover for tracking. These topics have been covered extensively in computer vision literatures, however new algorithms must be invented to accommodate the various constraints introduced and required by the DWSC platform. A technique has been developed for the automatic calibration of low-cost cameras which are assumed to be restricted in their freedom of movement to either pan or tilt movements. Camera internal parameters, including focal length, principal point, lens distortion parameter and the angle and axis of rotation, can be recovered from a minimum set of two images of the camera, provided that the axis of rotation between the two images goes through the camera's optical centre and is parallel to either the vertical (panning) or horizontal (tilting) axis of the image. For object localisation, a novel approach has been developed for the calibration of a network of non-overlapping DWSCs in terms of their ground plane homographies, which can then be used for localising objects. In the proposed approach, a robot travels through the camera network while updating its position in a global coordinate frame, which it broadcasts to the cameras. The cameras use this, along with the image plane location of the robot, to compute a mapping from their image planes to the global coordinate frame. This is combined with an occupancy map generated by the robot during the mapping process to localised objects moving within the network. In addition, to deal with the problem of object handover between DWSCs of non-overlapping fields of view, a highly-scalable, distributed protocol has been designed. Cameras that follow the proposed protocol transmit object descriptions to a selected set of neighbours that are determined using a predictive forwarding strategy. The received descriptions are then matched at the subsequent camera on the object's path using a probability maximisation process with locally generated descriptions. The second problem of camera placement emerges naturally when these pervasive devices are put into real use. The locations, orientations, lens types etc. of the cameras must be chosen in a way that the utility of the network is maximised (e.g. maximum coverage) while user requirements are met. To deal with this, a statistical formulation of the problem of determining optimal camera configurations has been introduced and a Trans-Dimensional Simulated Annealing (TDSA) algorithm has been proposed to effectively solve the problem.

Separable and non-separable discrete wavelet transform based texture features and image classification of breast thermograms

Highly sensitive infrared cameras can produce high-resolution diagnostic images of the temperature and vascular changes of breasts. Wavelet transform based features are suitable in extracting the texture difference information of these images due to their scale-space decomposition. The objective of this study is to investigate the potential of extracted features in differentiating between breast lesions by comparing the two corresponding pectoral regions of two breast thermograms. The pectoral regions of breastsare important because near 50% of all breast cancer is located in this region. In this study, the pectoral region of the left breast is selected. Then the corresponding pectoral region of the right breast is identified. Texture features based on the first and the second sets of statistics are extracted from wavelet decomposed images of the pectoral regions of two breast thermograms. Principal component analysis is used to reduce dimension and an Adaboost classifier to evaluate classification performance. A number of different wavelet features are compared and it is shown that complex non-separable 2D discrete wavelet transform features perform better than their real separable counterparts.

The Challenges and Opportunities in Developing an Organizational Approach to Managing Workforce Diversity

For the past two decades the concept of managing individual difference in the workforce has been popular in many Western organizations, with calls to manage this "diversity" for the greater good of the organization and the\ individuals in it. Paradoxically, there is no agreed definition for this concept, and its description remains unclear and often contested (Jensen, Maznevski & Schneider 2011). Indeed, a range of terms is used, including diversity: diversity at work, managing diversity, diversity management, workplace diversity, productive diversity, and so forth. The foundation of the concept of managing diversity is the idea that an organization's workforce displays a range of “diverse” characteristics. The characteristics that are included under the heading Of "diversity" vary.

Organizational reframing : the commercialization of a public sector organization

The purpose of this study is to elaborate shared schema change theory in the context of the radical restructuring-commercialization of an Australian public infrastructure organization. Commercialization of the case organization imposed high individual and collective cognitive processing and emotional demands as organizational members sought to develop new shared schema. Existing schema change research suggests that radical restructuring renders pre-existing shared schema irrelevant and triggers new schema development through experiential learning (Balogun and Johnson, 2004). Focus groups and semi-structured interviews were conducted at four points over a three-year period. The analysis revealed that shared schema change occurred in three broad phases: (1) radical restructuring and aftermath; (2) new CEO and new change process schema, and: (3) large-group meeting and schema change. Key findings include: (1) radical structural change does not necessarily trigger new shared schema development as indicated in prior research; (2) leadership matters, particularly in framing new means-ends schema; (3) how change leader interventions are sequenced has an important influence on shared schema change, and; (4) the creation of facilitated social processes have an important influence on shared schema change.

A computer image analysis system for microvessel density measurement in solid tumours

Microvessel density (MVD) is a widely used surrogate measure of angiogenesis in pathological specimens and tumour models. Measurement of MVD can be achieved by several methods. Automation of counting methods aims to increase the speed, reliability and reproducibility of these techniques. The image analysis system described here enables MVD measurement to be carried out with minimal expense in any reasonably equipped pathology department or laboratory. It is demonstrated that the system translates easily between tumour types which are suitably stained with minimal calibration. The aim of this paper is to offer this technique to a wider field of researchers in angiogenesis.

Intra-organizational information asymmetry in offshore ISD outsourcing

Purpose - Contemporary offshore Information System Development (ISD) outsourcing is becoming even more complex. Outsourcing partner has begun ‘re-outsourcing’ components of their projects to other outsourcing companies to minimize cost and gain efficiencies. This paper aims to explore intra-organizational Information Asymmetry of re-outsourced offshore ISD outsourcing projects. Design/methodology/approach - An online survey was conducted to get an overall view of Information Asymmetry between Principal and Agents (as per the Agency theory). Findings - Statistical analysis showed that there are significant differences between the Principal and Agent on clarity of requirements, common domain knowledge and communication effectiveness constructs, implying an unbalanced relationship between the parties. Moreover, our results showed that these three are significant measurement constructs of Information Asymmetry. Research limitations/implications - In our study we have only considered three main factors as common domain knowledge, clarity of requirements and communication effectiveness as three measurement constructs of Information Asymmetry. Therefore, researches are encouraged to test the proposed constructs further to increase its precision. Practical implications - Our analysis indicates significant differences in all three measurement constructs, implying the difficulties to ensure that the Agent is performing according to the requirements of the Principal. Using the Agency theory as theoretical view, this study sheds light on the best contract governing methods which minimize Information Asymmetry between the multiple partners within ISD outsourcing organizations. Originality/value - Currently, to the best of our knowledge, no study has undertaken research on Intra-organizational Information Asymmetry in re-outsourced offshore ISD outsourcing projects.

A comparison of the histochemical and image-derived proteoglycan content of articular cartilage

There are several methods for determining the proteoglycan content of cartilage in biomechanics experiments. Many of these include assay-based methods and the histochemistry or spectrophotometry protocol where quantification is biochemically determined. More recently a method based on extracting data to quantify proteoglycan content has emerged using the image processing algorithms, e.g., in ImageJ, to process histological micrographs, with advantages including time saving and low cost. However, it is unknown whether or not this image analysis method produces results that are comparable to those obtained from the biochemical methodology. This paper compares the results of a well-established chemical method to those obtained using image analysis to determine the proteoglycan content of visually normal (n=33) and their progressively degraded counterparts with the protocols. The results reveal a strong linear relationship with a regression coefficient (R2) = 0.9928, leading to the conclusion that the image analysis methodology is a viable alternative to the spectrophotometry.

The impact of organizational boundaries on (re)building capabilities

The literature concerning firm boundaries has focussed extensively on the rationale for different boundary choices and the economic efficiencies that such choices can make. There is also an acknowledged position that a firm’s boundary choices may impact the ability of a firm to maintain and even build new capabilities, though such choices may not be optimal from an economic efficiency perspective. It is in this context that we seek to investigate how firms make this potential trade-off in respect of their boundary choices and how these choices are implemented across a wide range of activities. Using qualitative data from three public sector construction oriented organizations, we observe that neither pure make nor buy decisions assisted significantly in capability building. Dual modes – where firms make and buy the same product or service simultaneously – provided firms with some opportunities to manage this paradox, but the most successful decisions seemed to occur in respect of using intermediate governance modes such as alliances. We also observed that the boundary choice was just one dimension of the capability building process and firms pursuing the same boundary choice decisions often had quite divergent outcomes on the basis of their boundary management and the ability of knowledge to move across firm boundaries.

Image segmentation and pose estimation of humans in video

This thesis introduces improved techniques towards automatically estimating the pose of humans from video. It examines a complete workflow to estimating pose, from the segmentation of the raw video stream to extract silhouettes, to using the silhouettes in order to determine the relative orientation of parts of the human body. The proposed segmentation algorithms have improved performance and reduced complexity, while the pose estimation shows superior accuracy during difficult cases of self occlusion.

A conceptualization of complexity in IS-driven organizational transformations

Organizational transformations reliant on successful ICT system developments (continue to) fail to deliver projected benefits even when contemporary governance models are applied rigorously. Modifications to traditional program, project and systems development management methods have produced little material improvement to successful transformation as they are unable to routinely address the complexity and uncertainty of dynamic alignment of IS investments and innovation. Complexity theory provides insight into why this phenomenon occurs and is used to develop a conceptualization of complexity in IS-driven organizational transformations. This research-in-progress aims to identify complexity formulations relevant to organizational transformation. Political/power based influences, interrelated business rules, socio-technical innovation, impacts on stakeholders and emergent behaviors are commonly considered as characterizing complexity while the proposed conceptualization accommodates these as connectivity, irreducibility, entropy and/or information gain in hierarchically approximation and scaling, number of states in a finite automata and/or dimension of attractor, and information and/or variety.

Dealing with shadows : capturing intrinsic scene appearance for image-based outdoor localisation

In outdoor environments shadows are common. These typically strong visual features cause considerable change in the appearance of a place, and therefore confound vision-based localisation approaches. In this paper we describe how to convert a colour image of the scene to a greyscale invariant image where pixel values are a function of underlying material property not lighting. We summarise the theory of shadow invariant images and discuss the modelling and calibration issues which are important for non-ideal off-the-shelf colour cameras. We evaluate the technique with a commonly used robotic camera and an autonomous car operating in an outdoor environment, and show that it can outperform the use of ordinary greyscale images for the task of visual localisation.

Optimal camera planning under versatile user constraints in multi-camera image processing systems

The selection of optimal camera configurations (camera locations, orientations, etc.) for multi-camera networks remains an unsolved problem. Previous approaches largely focus on proposing various objective functions to achieve different tasks. Most of them, however, do not generalize well to large scale networks. To tackle this, we propose a statistical framework of the problem as well as propose a trans-dimensional simulated annealing algorithm to effectively deal with it. We compare our approach with a state-of-the-art method based on binary integer programming (BIP) and show that our approach offers similar performance on small scale problems. However, we also demonstrate the capability of our approach in dealing with large scale problems and show that our approach produces better results than two alternative heuristics designed to deal with the scalability issue of BIP. Last, we show the versatility of our approach using a number of specific scenarios.

Training-free probability models for whole-image based place recognition

Whole-image descriptors such as GIST have been used successfully for persistent place recognition when combined with temporal filtering or sequential filtering techniques. However, whole-image descriptor localization systems often apply a heuristic rather than a probabilistic approach to place recognition, requiring substantial environmental-specific tuning prior to deployment. In this paper we present a novel online solution that uses statistical approaches to calculate place recognition likelihoods for whole-image descriptors, without requiring either environmental tuning or pre-training. Using a real world benchmark dataset, we show that this method creates distributions appropriate to a specific environment in an online manner. Our method performs comparably to FAB-MAP in raw place recognition performance, and integrates into a state of the art probabilistic mapping system to provide superior performance to whole-image methods that are not based on true probability distributions. The method provides a principled means for combining the powerful change-invariant properties of whole-image descriptors with probabilistic back-end mapping systems without the need for prior training or system tuning.

Using image quality metrics to detect smoke-affected laser data

Field robots often rely on laser range finders (LRFs) to detect obstacles and navigate autonomously. Despite recent progress in sensing technology and perception algorithms, adverse environmental conditions, such as the presence of smoke, remain a challenging issue for these robots. In this paper, we investigate the possibility to improve laser-based perception applications by anticipating situations when laser data are affected by smoke, using supervised learning and state-of-the-art visual image quality analysis. We propose to train a k-nearest-neighbour (kNN) classifier to recognise situations where a laser scan is likely to be affected by smoke, based on visual data quality features. This method is evaluated experimentally using a mobile robot equipped with LRFs and a visual camera. The strengths and limitations of the technique are identified and discussed, and we show that the method is beneficial if conservative decisions are the most appropriate.