The fourth industrial revolution has increased focus on industrial digital transformation (IDT) for its industrial value creation potential. However, practical implementation continues to require improved support from tools and frameworks. This research develops an empirically based conceptual framework for IDT. The study utilizes semi-structured interviews with technology experts, building on and extending the dynamic capabilities theory as its theoretical foundation to identify how IDT uses technologies to create organizational value via smartness capability. The results identify four groups of technological features that support enterprise smartness (1. intelligence, 2. simulation, visualization, and remote interaction, 3. data and information management, and 4. stimuli responsiveness) and the organizational features that characterize IDT, including digitization, integration, persistent customer engagement, production ecosystems, data capability, information transparency, and smartness. The framework identifies that transformation results in organizational value creation. The study’s implication for professionals is to re-focus IDT strategy on developing value-creating capability in the enterprise.

Moving targets at a very large distance from a camera appear small and of low contrast. The low signal-to-noise-ratio and the presence of clutter in the background degrade the detection performance of conventional moving object detection techniques. To address these challenges, we propose temporal pre-processing of video frames using a biologically-inspired vision model. The bio-inspired model consists of multiple layers of processing analogous to the photoreceptor cells in the visual system of small insects. The adaptive filtering mechanism in the photoreceptor cells suppresses clutter and expands the possible range of input signal changes which improves the target background contrast. We perform experiments on real world video sequences of small moving targets captured with a high bit depth, high resolution and high frame-rate camera. Experimental results show that the biological vision based pre-processing leads to improved detection performance when used in conjunction with a variety of computer vision based moving object detection algorithms. The temporal bio-processing alone has improved the area under the receiver operating characteristic (AUROC) curve of the best performing algorithm by 75.4%. Our results suggest that the bio-inspired pre-processing has strong potential to become a key component of a practical small target detection system.

Computer vision algorithms that make use of optical flow are constantly increasing in complexity, especially in the context of elaborated algorithms that are heavily inspired by biology. To develop upon and utilise these algorithms for realworld tasks, their extensive parameter sets need to be tuned. Due to algorithmic complexity, and non-linearities present throughout their parameter set, this is no small task. Using an adaptive genetic algorithm, which itself is biologically-inspired, we look at the performance and behaviour of the tuning when significant changes have been made to a low speed rotational velocity optical flow estimation algorithm. We validate that previously reported changes to the optical flow estimator yielded a fitness increase of over 30% when compared to the baseline model the changes were made to, and a 15% increase once that baseline model was also tuned. This improvement would be extremely unlikely without the aid of evolutionary computation algorithms. This shows that even an extremely complex computer vision algorithm with many parameters, 36 in this case, can be tuned to an operating point, facilitating the continued development work on the vision algorithm by allowing for the validation and quantification of algorithmic changes.

Accidental falls have been identified as a cause of mortality for elders who live alone around the globe. Following a fall, additional injury can be sustained if proper fall recovery techniques are not followed. These secondary complications can be reduced if the person had access to safe recovery procedures or were assisted, either by a person or a robot. We propose a framework for in situ robotic assistance for post fall recovery scenarios. In order to assist autonomously robots need to recognize an individual's posture and subactivities (e.g., falling, rolling, move to hands and knees, crawling, and push up through legs, sitting or standing). Human body skeleton tracking through RGB-D pose estimation methods fail to identify the body parts during key phases of fall recovery due to high occlusion rates in fallen, and recovering, postures. To address this issue, we investigated how low-level image features can be leveraged to recognize an individual's subactivities. Depth cuboid similarity features (DCSFs) approach was improved with M-partitioned histograms of depth cuboid prototypes, integration of activity progression direction, and outlier spatiotemporal interest point removal. Our modified DCSF algorithm was evaluated on a unique RGB-D multiview dataset, achieving 87.43 ± 1.74% accuracy in the extensive 3003 (C15 10) combinations of trainingtest groups of 15 subjects in 10 trials. This result was significantly larger than the nearest competitor, and faster in the training phase. This work could lead to more accurate in situ robotic assistance for fall recovery, saving lives for victims of falls.

The neuronal pathway for biological motion vision is complex and non-linear. Despite considerable research effort it has defied accurate modelling for over 50 years. Recently we proposed a computational model for the calculation of egomotion that explained a number of outstanding issues, such as reliable coding in different environments and responses to artificially contrast rescaled images. Here we varied the amount of noise to determine the robustness of the model under conditions more typical of real-world scenes. High-dynamic range panoramic images taken from various environments were used as inputs to a computational motion model of biological motion vision. Gaussian white noise was added after image pre-processing but before motion detection. The addition of noise around the levels observed experimentally, in both biology and an engineered camera system, resulted in a surprising 50% increase in the discriminability of different velocity levels over that seen in the noise free condition. The more commonly used gradient model for motion detection produced outputs so swamped by noise they were unreliable under the same conditions. While the phenomenon of stochastic resonance has been observed previously in biological and bio-inspired systems it is most commonly found in conjunction with non-linear thresholding operations, such as spike generation. These findings are unusual as they show noise being beneficial in a model of an analogue system. They also highlight the robustness of the correlation model for biological motion detection to very large levels of noise.

The extraction of accurate self-motion information from the visual world is a difficult problem that has been solved very efficiently by biological organisms utilizing non-linear processing. Previous bio-inspired models for motion detection based on a correlation mechanism have been dogged by issues that arise from their sensitivity to undesired properties of the image, such as contrast, which vary widely between images. Here we present a model with multiple levels of non-linear dynamic adaptive components based directly on the known or suspected responses of neurons within the visual motion pathway of the fly brain. By testing the model under realistic high-dynamic range conditions we show that the addition of these elements makes the motion detection model robust across a large variety of images, velocities and accelerations. Furthermore the performance of the entire system is more than the incremental improvements offered by the individual components, indicating beneficial non-linear interactions between processing stages. The algorithms underlying the model can be implemented in either digital or analog hardware, including neuromorphic analog VLSI, but defy an analytical solution due to their dynamic non-linear operation. The successful application of this algorithm has applications in the development of miniature autonomous systems in defense and civilian roles, including robotics, miniature unmanned aerial vehicles and collision avoidance sensors.