Image fusion metrics : evolution in a nutshell

Image fusion process merges two images into a single more informative image. Objective image fusion per- formance metrics rely primarily on measuring the amount of information transferred from each source image into the fused image. Objective image fusion metrics have evolved from image processing dissimilarity metrics. Additionally, researchers have developed many additions to image dissimilarity metrics in order to better value the local fusion worthy features in source images. This paper studies the evolution of objective image fusion performance metrics and their subjective and objective validation. It describes how a fusion performance metric evolves starting with image dissimilarity metrics, its realization into image fusion contexts, its localized weighting factors and the validation process.

Fusion

 Solo exhibition of art works

Liza McCosh: fusion

Exhibition catalogue with introduction, essay, biography and images

Multi-sensory face biometric fusion (for personal identification)

The objective of this work is to recognize faces using sets of images in visual and thermal spectra. This is challenging because the former is greatly affected by illumination changes, while the latter frequently contains occlusions due to eye-wear and is inherently less discriminative. Our method is based on a fusion of the two modalities. Specifically: we examine (i) the effects of preprocessing of data in each domain, (ii) the fusion of holistic and local facial appearance, and (iii) propose an algorithm for combining the similarity scores in visual and thermal spectra in the presence of prescription glasses and significant pose variations, using a small number of training images (5-7). Our system achieved a high correct identification rate of 97% on a freely available test set of 29 individuals and extreme illumination changes.

Versican processing by a disintegrin-like and metalloproteinase domain with thrombospondin-1 repeats proteinases-5 and -15 facilitates myoblast fusion

Skeletal muscle development and regeneration requires the fusion of myoblasts into multinucleated myotubes. Because the enzymatic proteolysis of a hyaluronan and versican-rich matrix by ADAMTS versicanases is required for developmental morphogenesis, we hypothesized that the clearance of versican may facilitate the fusion of myoblasts during myogenesis. Here, we used transgenic mice and an in vitro model of myoblast fusion, C2C12 cells, to determine a potential role for ADAMTS versicanases. Versican processing was observed during in vivo myogenesis at the time when myoblasts were fusing to form multinucleated myotubes. Relevant ADAMTS genes, chief among them Adamts5 and Adamts15, were expressed both in developing embryonic muscle and differentiating C2C12 cells. Reducing the levels of Adamts5 mRNA in vitro impaired myoblast fusion, which could be rescued with catalytically active but not the inactive forms of ADAMTS5 or ADAMTS15. The addition of inactive ADAMTS5, ADAMTS15, or full-length V1 versican effectively impaired myoblast fusion. Finally, the expansion of a hyaluronan and versican-rich matrix was observed upon reducing the levels of Adamts5 mRNA in myoblasts. These data indicate that these ADAMTS proteinases contribute to the formation of multinucleated myotubes such as is necessary for both skeletal muscle development and during regeneration, by remodeling a versican-rich pericellular matrix of myoblasts. Our study identifies a possible pathway to target for the improvement of myogenesis in a plethora of diseases including cancer cachexia, sarcopenia, and muscular dystrophy.

Structural damage identification based on self-fitting ARMAX model and multi-sensor data fusion

Statistical time series methods have proven to be a promising technique in structural health monitoring, since it provides a direct form of data analysis and eliminates the requirement for domain transformation. Latest research in structural health monitoring presents a number of statistical models that have been successfully used to construct quantified models of vibration response signals. Although a majority of these studies present viable results, the aspects of practical implementation, statistical model construction and decision-making procedures are often vaguely defined or omitted from presented work. In this article, a comprehensive methodology is developed, which essentially utilizes an auto-regressive moving average with exogenous input model to create quantified model estimates of experimentally acquired response signals. An iterative self-fitting algorithm is proposed to construct and fit the auto-regressive moving average with exogenous input model, which is capable of integrally finding an optimum set of auto-regressive moving average with exogenous input model parameters. After creating a dataset of quantified response signals, an unlabelled response signal can be identified according to a 'closest-fit' available in the dataset. A unique averaging method is proposed and implemented for multi-sensor data fusion to decrease the margin of error with sensors, thus increasing the reliability of global damage identification. To demonstrate the effectiveness of the developed methodology, a steel frame structure subjected to various bolt-connection damage scenarios is tested. Damage identification results from the experimental study suggest that the proposed methodology can be employed as an efficient and functional damage identification tool. © The Author(s) 2014.

Multi-Kinect skeleton fusion for physical rehabilitation monitoring

 Kinect has been increasingly applied in rehabilitation as a motion capture device. However, the inherent limitations significantly hinder its further development in this important area. Although a number of Kinect fusion approaches have been proposed, only a few of them was actually considered for rehabilitation. In this paper, we propose to fuse information from multiple Kinects to achieve this. Given the specific scenario of users suffering from limited range of movements, we propose to calibrate depth cameras in multiple Kinects with 3D positions of joints on a human body rather than in a checkerboard pattern, so that patients are able to calibrate Kinects without extra support. Kalman filter is applied for skeleton-wise Kinect fusion since skeleton data (3D positions of joints) and its derivatives are preferred by physiotherapists to evaluate the exercise performance of patients. Various preliminary experiments were conducted to illustrate the accuracy of proposed calibration and fusion approach by comparing with a commercial Vicon system®, confirming the practical use of the system in rehabilitation exercise monitoring.

An adaptive complementary filter for inertial sensor based data fusion to track upper body motion

  Remote human activity monitoring is critical and essential in physiotherapy with respect to the skyrocketing healthcare expenditure and the fast aging population. One of frequently used method to monitor human activity is wearing inertial sensors since it is low-cost and accurate. However, the measurements of those sensors are able only to estimate the orientation and rotation angles with respect to actual movement angles, because of differences in the body’s co-ordination system and the sensor’s co-ordination system. There were numerous studies being conducted to improve the accuracy of estimation, though there is potential for further discussions on improving accuracy by replacing heavy algorithms to less complexity. This research is an attempt to propose an adaptive complementary filter for identifying human upper arm movements. Further, this article discusses a feasibility of upper arm rehabilitation using the proposed adaptive complementary filter and inertial measurement sensors. The proposed algorithm is tested with four healthy subjects wearing an inertial sensor against gold standard, which is the VICON system. It demonstrated root mean squared error of 8.77◦ for upper body limb orientation estimation when compared to gold standard VICON optical motion capture system.

Identifying influential nodes in complex networks: A multiple attributes fusion method

How to identify influential nodes is still an open hot issue in complex networks. Lots of methods (e.g., degree centrality, betweenness centrality or K-shell) are based on the topology of a network. These methods work well in scale-free networks. In order to design a universal method suitable for networks with different topologies, this paper proposes a Multiple Attribute Fusion (MAF) method through combining topological attributes and diffused attributes of a node together. Two fusion strategies have been proposed in this paper. One is based on the attribute union (FU), and the other is based on the attribute ranking (FR). Simulation results in the Susceptible-Infected (SI) model show that our proposed method gains more information propagation efficiency in different types of networks. © 2014 Springer International Publishing.

Fusion: repo to expo

Deakin University has set up a new exhibition system, Fusion, to showcase the University's special collections and research outputs. Data in Fusion will also be used by researchers in the digital humanities, and will provide a means of involving the wider, local, community in research efforts and collaborative projects. Fusion uses the Omeka software, which supports highly-visual, media-rich presentations. It is linked to the University's research repository, which remains the primary store for the data.Deakin’s special collections include unique material related to Alfred Deakin and the Federation of Australia, as well as the cultural and social milieu of the period 1859 to 1920. The Library has begun a digitisation project to ensure the long-term preservation of the rarer, older and most fragile items from the collection. The poster will cover:• Principal software requirements for the new system• Fusion setup, features and functionality• Curation of items for digitisation• Long-term preservation strategy• Exhibition planning and setup• Collaborative projects with Research Services• Collaboration with the wider community• Future plans, including crowd-sourcing projects• Lessons learntThe poster will draw on the following conference themes:Supporting research: Research digital outputs as collection materialsSupporting research: Research content accessibilitySupporting research: Data management / curationConnect: Crowdsourcing

A data fusion method in wireless sensor networks

The success of a Wireless Sensor Network (WSN) deployment strongly depends on the quality of service (QoS) it provides regarding issues such as data accuracy, data aggregation delays and network lifetime maximisation. This is especially challenging in data fusion mechanisms, where a small fraction of low quality data in the fusion input may negatively impact the overall fusion result. In this paper, we present a fuzzy-based data fusion approach for WSN with the aim of increasing the QoS whilst reducing the energy consumption of the sensor network. The proposed approach is able to distinguish and aggregate only true values of the collected data as such, thus reducing the burden of processing the entire data at the base station (BS). It is also able to eliminate redundant data and consequently reduce energy consumption thus increasing the network lifetime. We studied the effectiveness of the proposed data fusion approach experimentally and compared it with two baseline approaches in terms of data collection, number of transferred data packets and energy consumption. The results of the experiments show that the proposed approach achieves better results than the baseline approaches.