Learning Photometric Invariance for Object Detection

Color is a powerful visual cue in many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions that negatively affect the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, this approach may be too restricted to model real-world scenes in which different reflectance mechanisms can hold simultaneously.     

Virtual Reference Feedback Tuning for non-minimum phase plants

Model reference control design methods fail when the plant has one or more non-minimum phase zeros that are not included in the reference model, leading possibly to an unstable closed loop. This is a very serious problem for data-based control design methods, where the plant is typically unknown. In this paper, we extend the Virtual Reference Feedback Tuning method to non-minimum phase plants. This extension is based on the idea proposed in Lecchini and Gevers (2002) for Iterative Feedback Tuning. We present a simple two-step procedure that can cope with the situation where the unknown plant may or may not have non-minimum phase zeros.     

Identification of multi-input systems: variance analysis and input design issues

This paper examines the identification of multi-input systems. Motivated by an experiment design problem (should one excite the various inputs simultaneously or separately), we examine the effect of an additional input on the variance of the estimated coefficients of parametrized rational transfer function models, with special emphasis on the commonly used FIR, ARX, ARMAX, OE and BJ model structures. We first show that, for model structures that have common parameters in the input-output and noise models (e.g. ARMAX), any additional input contributes to a reduction of the covariance of all parameter estimates. We then show that the accuracy improvement extends beyond the case of common parameters in all transfer functions, and we show exactly which parameter estimates are improved when a new input is added. We also conclude that it is always better to excite all inputs simultaneously.     

A decade of progress in iterative process control design: from theory to practice

One of the most active areas of research in the nineties has been the study of the interplay between system identification and robust control design. It has led to the development of “control-oriented identification design”, the paradigm being that, since the model is only a tool for the design of a controller, its accuracy (or its error distribution) must be tuned towards the control design objective. This observation has led to the concept of “iterative identification and control design” and, subsequently, to model-free iterative controller design, in which the controller parameters are iteratively tuned on the basis of successive experiments performed on the real plant, leading to better and better closed-loop behaviour. These iterative methods have found immediate applications in industry; they have also been applied to the optimal tuning of PID controllers. This paper presents the progress that has been accomplished in iterative process control design over the last decade. It is illustrated with some applications in the chemical industry.     

Image segmentation and similarity of color-texture objects

We aim for content-based image retrieval of textured objects in natural scenes under varying illumination and viewing conditions. To achieve this, image retrieval is based on matching feature distributions derived from color invariant gradients. To cope with object cluttering, region-based texture segmentation is applied on the target images prior to the actual image retrieval process. The retrieval scheme is empirically verified on color images taken from textured objects under different lighting conditions.     

How exciting can a signal really be?

The rate of parameter convergence in a number of adaptive estimation schemes is related to the smallest eigenvalue of the average information matrix determined by the regression vector. Using a very simple examples, we illustrate that the input signals that maximize this minimum eigenvalue may be quite different from the input signals that optimize more classical input design criteria, e.g. D-optimal criterion.     

Short-term oestrogen replacement therapy improves insulin resistance, lipids and fibrinolysis in postmenopausal women with NIDDM

Oestrogen replacement therapy is associated with a decreased risk of cardiovascular disease in postmenopausal women. Patients with non-insulin-dependent diabetes mellitus (NIDDM) have an increased cardiovascular risk. However, oestrogen replacement therapy is only reluctantly prescribed for patients with NIDDM. In a double blind randomized placebo controlled trial we assessed the effect of oral 17 beta-estradiol during 6 weeks in 40 postmenopausal women with NIDDM. Glycated haemoglobin (HbA1c), insulin sensitivity, suppressibility of hepatic glucose production, lipoprotein profile and parameters of fibrinolysis were determined. The oestrogen treated group demonstrated a significant decrease of HbA1c and in the normotriglyceridaemic group a significantly increased suppression of hepatic glucose production by insulin. Whole body glucose uptake and concentrations of non-esterified fatty acids did not change. LDL-cholesterol- and apolipoprotein B levels decreased, and HDL-cholesterol, its subfraction HDL2-cholesterol and apolipotrotein A1 increased. The plasma triglyceride level remained similar in both groups. Both the concentration of plasminogen activator inhibitor-1 antigen and its active subfraction decreased. Tissue type plasminogen activator activity increased significantly only in the normotriglyceridaemic group. Oestrogen replacement therapy improves insulin sensitivity in liver, glycaemic control, lipoprotein profile and fibrinolysis in postmenopausal women with NIDDM. For a definite answer as to whether oestrogens can be more liberally used in NIDDM patients, long term studies including the effect of progestogens are necessary.     

Quantifying the error in estimated transfer functions withapplication to model order selection

Previous results on estimating errors or error bounds on identified transfer functions have relied on prior assumptions about the noise and the unmodeled dynamics. This prior information took the form of parameterized bounding functions or parameterized probability density functions, in the time or frequency domain with known parameters. It is shown that the parameters that quantify this prior information can themselves be estimated from the data using a maximum likelihood technique. This significantly reduces the prior information required to estimate transfer function error bounds. The authors illustrate the usefulness of the method with a number of simulation examples. How the obtained error bounds can be used for intelligent model-order selection that takes into account both measurement noise and under-modeling is shown. Another simulation study compares the method to Akaike's well-known FPE and AIC criteria     

Medical decisions made by teams: carefulness and responsibility

To a growing extent, medical decision making takes place in the context of a team. Where multidisciplinary cooperation is necessary and the decisions to be taken are complex and consequential (in particular at the beginning and the end of life) joint decision making is considered an essential requirement of careful medical practice. From a legal point of view, this raises the question who is accountable for such decisions. Basically, accountability (and the liability that may result from it) will stay with the individual participants, and in particular with the first responsible, attending physician. In this respect, decision making in such situations is not basically different from other, more informal consultation structures (such as regular case reviews at department level) which are current practice in many health care institutions. Finally, if joint decision making is required, it should be clear in advance how decisions are to be arrived at in the team.