A new algorithm for segmenting a multimodal grey-scale image is proposed. The image is described as a sample of a joint Gibbs random field of region labels and grey levels. To initialize the model, a mixed multimodal empirical grey-level distribution is approximated with linear combinations of Gaussians, one combination per region. Bayesian decisions involving expectation maximization and genet...

Many signal processing applications pose optimization problems with multimodal and nonsmooth cost functions. Gradient methods are ine!ective in these situations. The adaptive simulated annealing (ASA) o!ers a viable optimization tool for tackling these di$cult nonlinear optimization problems. Three applications, maximum likelihood (ML) joint channel and data estimation, in"nite-impulse-response...

Learning in neural networks can be formulated as global optimization of a multimodal error function that is defined over the high-dimensional space of connection weights. This global optimization is both theoretically intractable [26] [35] and difficult in practice. Traditional learning heuristics, e.g., back-propagation [31] or Boltzmann learning [11], are largely based on gradient methods or ...

Multiobjective optimization strategy so-called Physical Programming allows controller designers a flexible way to express design preferences with a ’physical’ sense. For each objective (settling time, overshoot, disturbance rejection, etc.) preferences are established through categories as desirable, tolerable, unacceptable, etc. assigned to numerical ranges. The problem is translated into a un...

In this paper, different automatic registration schemes based on different optimization techniques in conjunction with different similarity measures are compared in terms of accuracy and efficiency. Results from every optimization procedure are quantitatively evaluated with respect to the manual registration, which is the standard registration method used in clinical practice. The comparison ha...

 The clonal selection principle is used to explain the basic features of an adaptive immune response to an antigenic stimulus. It establishes the idea that only those cells that recognize the antigens are selected to proliferate. The selected cells are subject to an affinity maturation process, which improves their affinity to the selective antigens. In this paper, we propose a computational i...

We tackle here the problem of multimodal image nonrigid registration, which is of prime importance in remote sensing and medical imaging. The difficulties encountered by classical registration approaches include feature design and slow optimization by gradient descent. By analyzing these methods, we note the significance of the notion of scale. We design easy-to-train, fully-convolutional neura...

In vivo imaging plays a key role in cell tracking, particularly for the optimization of cellular therapeutics. A recent trend is to use more than one imaging modality (multimodality imaging) for this purpose. There are several advantages to multimodal cell tracking, particularly the corroboration of data obtained using a new imaging agent or technique with an established one, and the ability to...

In many real-world optimization problems, the location of multiple optima is often required in a search space. In order to evaluate the solution, thousands of fitness function evaluations are involved that is a time consuming or expensive processes. Therefore, standard Particle Swarm Optimization (PSO) meets a special challenge for a very large number of problem function evaluations. Applying m...

Abstract. In this paper, we propose a new method to generate a continuous belief functions from a multimodal probability distribution function defined over a continuous domain. We generalize Smets’ approach in the sense that focal elements of the resulting continuous belief function can be disjoint sets of the extended real space of dimension n. We then derive the continuous belief function fro...