Comments on 'Information measure for performance of image fusion'

The unsuitability of using classic mutual information measure as a performance measure for image fusion is discussed. Analytical proof that classic mutual information cannot be considered a measure for image fusion performance is provided.

Adaptive fusion of gait and face for human identification in video

Most work on multi-biometric fusion is based on static fusion rules which cannot respond to the changes of the environment and the individual users. This paper proposes adaptive multi-biometric fusion, which dynamically adjusts the fusion rules to suit the real-time external conditions. As a typical example, the adaptive fusion of gait and face in video is studied. Two factors that may affect the relationship between gait and face in the fusion are considered, i.e., the view angle and the subject-to-camera distance. Together they determine the way gait and face are fused at an arbitrary time. Experimental results show that the adaptive fusion performs significantly better than not only single biometric traits, but also those widely adopted static fusion rules including SUM, PRODUCT, MIN, and MAX.

Optimal multisensor data fusion for linear systems with missing measurements

Multisensor data fusion has attracted a lot of research in recent years. It has been widely used in many applications especially military applications for target tracking and identification. In this paper, we will handle the multisensor data fusion problem for systems suffering from the possibility of missing measurements. We present the optimal recursive fusion filter for measurements obtained from two sensors subject to random intermittent measurements. The noise covariance in the observation process is allowed to be singular which requires the use of generalized inverse. Illustration example shows the effectiveness of the proposed filter in the measurements loss case compared to the available optimal linear fusion methods.

Color map-based image fusion

An image fusion system accepts two source images and produces a 'better' fused image. The term 'better' differs from one context to another. In some contexts, it means holding more information. In other contexts, it means getting more accurate results or readings. In general, images hold more than just the color values. Histogram distribution, dynamic range of colors, and color maps are all as valuable as the color values presenting the pictorial information of the image. This paper studies the problems of fusing images from different domains. It proposes a method to extend the fusion algorithms to fuse image properties that define the interpretation of captured images as well.

Generation of mutant leukaemia inhibitory factor (LIF)-IgG heavy chain fusion proteins as bivalent antagonists of LIF

Two leukaemia inhibitory factor (LIF) mutants, designated MH35-BD and LIF05, have been shown to have a capacity to inhibit the biological activities of not only human LIF (hLIF) but also other interleukin-6 (IL-6) subfamily cytokines such as human oncostatin M (hOSM). These cytokines share the same receptor complex in which the glycoprotein 130 (gp130) subunit is a common constituent. However, at low concentrations and in their monomeric forms, such molecules have a relatively short plasma half-life due to rapid clearance from the kidneys. Here, to prolong their serum half-lives, we have used a multi-step polymerase chain reaction (PCR) to fuse each of the LIF05 and MH35-BD cDNA fragments to a sequence encoding the Fc portion, and the hinge region, of the human immunoglobulin G (hIgG) heavy chain. The linking was achieved through an oligomer encoding a thrombin-sensitive peptide linker thus generating MH35-BD:Fc and LIF05:Fc, respectively. Both Fc fusion constructs were expressed in insect cell Sf21 and the proteins were purified by two successive affinity chromatography steps using nickel–nitrilotriacetic acid (Ni–NTA) agarose and protein A beads. The Ba/F3 cell-based proliferation assay was used to confirm that the proteins were biologically active. In addition, preliminary pharmacokinetics indicates that the Fc fusion constructs have a longer serum half-life compared to their non-fusion counterparts.

Optimal linear data fusion for systems with missing measurements

In this paper, we provide the optimal data fusion filter for linear systems suffering from possible missing measurements. The noise covariance in the observation process is allowed to be singular which requires the use of generalized inverse. The data fusion process is made on the raw data provided by two sensors  observing the same entity. Each of the sensors is losing the measurements in its own data loss rate. The data fusion filter is provided in a recursive form for ease of implementation in real-world applications.

Robust multi-sensor data fusion

This research obtains the optimal estimation and data fusion for linear and nonlinear systems suffering from uncertain observations (missing measurements). The noise from the different data sources are considered to correlated. The derivation of the robust Kalman filter for systems subject to aditional uncertainties in the modelling parameters is presented.

An algebraic framework for multimodal image fusion

This thesis presents an algebraic framework for multimodal image fusion. The framework derives algebraic constructs and equations that govern the fusion process. The derived equations serve as objective functions according to which image fusion algorithms and metrics can be tuned. The equations also prove the duality between image fusion algorithms and metrics.

Minimizing the limitations of GL analyser of fusion based email classification

In the last decade, the Internet email has become one of the primary method of communication used by everyone for the exchange of ideas and information. However, in recent years, along with the rapid growth of the Internet and email, there has been a dramatic growth in spam. Classifications algorithms have been successfully used to filter spam, but with a certain amount of false positive trade-offs. This problem is mainly caused by the dynamic nature of spam content, spam delivery strategies, as well as the diversification of the classification algorithms. This paper presents an approach of email classification to overcome the burden of analyzing technique of GL (grey list) analyser as further refinements of our previous multi-classifier based email classification [10]. In this approach, we introduce a “majority voting grey list (MVGL)” analyzing technique with two different variations which will analyze only the product of GL emails. Our empirical evidence proofs the improvements of this approach, in terms of complexity and cost, compared to existing GL analyser. This approach also overcomes the limitation of human interaction of existing analyzing technique.

A lightweight intrusion alert fusion system

In this paper, we present some practical experience on implementing an alert fusion mechanism from our project. After investigation on most of the existing alert fusion systems, we found the current body of work alternatively weighed down in the mire of insecure design or rarely deployed because of their complexity. As confirmed by our experimental analysis, unsuitable mechanisms could easily be submerged by an abundance of useless alerts. Even with the use of methods that achieve a high fusion rate and low false positives, attack is also possible. To find the solution, we carried out analysis on a series of alerts generated by well-known datasets as well as realistic alerts from the Australian Honey-Pot. One important finding is that one alert has more than an 85% chance of being fused in the following 5 alerts. Of particular importance is our design of a novel lightweight Cache-based Alert Fusion Scheme, called CAFS. CAFS has the capacity to not only reduce the quantity of useless alerts generated by IDS (Intrusion Detection System), but also enhance the accuracy of alerts, therefore greatly reducing the cost of fusion processing. We also present reasonable and practical specifications for the target-oriented fusion policy that provides a quality guarantee on alert fusion, and as a result seamlessly satisfies the process of successive correlation. Our experimental results showed that the CAFS easily attained the desired level of survivable, inescapable alert fusion design. Furthermore, as a lightweight scheme, CAFS can easily be deployed and excel in a large amount of alert fusions, which go towards improving the usability of system resources. To the best of our knowledge, our work is a novel exploration in addressing these problems from a survivable, inescapable and deployable point of view.

Towards autonomous image fusion

Mobile robots are providing great assistance operating in hazardous environments such as nuclear cores, battlefields, natural disasters, and even at the nano-level of human cells. These robots are usually equipped with a wide variety of sensors in order to collect data and guide their navigation. Whether a single robot operating all sensors or a swarm of cooperating robots operating their special sensors, the captured data can be too large to be transferred across limited resources (e.g. bandwidth, battery, processing, and response time) in hazardous environments. Therefore, local computations have to be carried out on board the swarming robots to assess the worthiness of captured data and the capacity of fused information in a certain spatial dimension as well as selection of proper combination of fusion algorithms and metrics. This paper introduces to the concepts of Type-I and Type-II fusion errors, fusion capacity, and fusion worthiness. These concepts together form the ladder leading to autonomous fusion systems.