This article presents a new method for generating an optimum image morph field, i.e. an optimum mapping of one image to another image by distorting the brightness and geometry of the former image. The mapping is calculated by maximizing the probability of the morph field in a Bayesian framework. In contrast to other techniques this new method needs no training and is derived based on group invariances and expected singularities, only. An infeasible exhaustive search is replaced by an new iterative approximation approach where, in a neighborhood around the current morph field solution, the probability distribution is approximated by a Gaussian probability distribution for which the most likely solution is evaluated by linear algebra techniques. New views are generated by applying linearly interpolated morph fields to the original reference image. Experiments demonstrate that this approach is well suited to interpolate between different views of a single face image or between images of different persons. Finally, a new face recognition algorithm makes use of the fact that morphs among images of a single person are confined to a five-dimensional subspace within the space of all possible morphs.

Varying illumination is severe problem for existing face recognition algorithms. Altering the light direction from left to right, for example, causes a change of contrast in large face regions and causes most face recognition algorithms to fail. Theoretical results, based on the law of incoherent light superposition, provide the solid ground on which a new illumination invariant recognition algorithm is derived. A face recognition experiment demonstrates that this algorithm indeed shows improved recognition performance even if the conditions, for which the theoretical results were derived, do not hold exactly.

Estimating probabilities based on measured numbers of occurrences of events provides a central link from probability theory to real world applications. In an important class of applications the probabilistic events correspond to the digitized outcome of an analog sensor. This paper shows theoretically and experimentally that such events are governed by a natural similarity relation which imposes subtle but highly effective a priori constraints on the meta-probability distribution, i.e. the probability distribution of probability values. The application of these constraints significantly improves the probability estimates based on the measurements. The results are applied to estimating order-1 Markov model parameters in image processing applications. 1 Introduction Virtually every real-world signal processing problem has a non-deterministic component. Therefore, its adequate description has to be statistical, in terms of joint probability distributions. Numerous examples can be found ...

A new segmentation algorithm is derived, based on an object-background probability estimate exploiting the experimental fact that the statistics of local image derivatives show a Laplacian distribution. The objects' simple connectedness is included directly into the probability estimate and leads to an iterative optimization approach that can be implemented efficiently. This new approach avoids early thresholding, explicit edge detection, motion analysis, and grouping.>

A shape-from-shading algorithm that recovers depth from a brightness image typically in fewer than ten iterations, is described. This algorithm, which is a simplification of the algorithm of J. Oliensis and P. Dupuis (1991), is based on a minimum downhill principle that guarantees continuous surfaces and stable results. The algorithm is applicable to a broad variety of objects and reflectance maps.>