Fast image processing with constraints by solving linear PDEs

We present a general framework that allows image filtering by minimization of a functional using a linear and positive definite partial differential equation (PDE) while also permitting to control the weight of each pixel individually. Linearity and positive definiteness allow to use fast algorithms to calculate the solution. Pixel weighting allows to enforce the preservation of edge information without the need for nonlinear diffusion by making use of information coming from an external source. The proof of existence and uniqueness of the solution is outlined and based on that a numerical scheme for finding the solution is introduced. Using this framework we developed two applications. The first is simple and fast denoising, which incorporates an edge detection algorithm. In this case the functional is designed to enhance the weight of the approximation term over the smoothing term at those places where an edge is detected. The second application is a background suppression algorithm that is robust against noise, shadows thrown by the object, and on the background and varying illumination. The results are qualitatively not quite as good as the ones obtained with nonlinear PDEs, but this disadvantage is compensated by the processing speed, which allows analysis of a 320×240 color frame in about 0.3s on a standard PC

Reconstruction of Images from Gabor Graphs with Applications in Facial Image Processing

Graphs labeled with complex-valued Gabor jets are one of the important data formats for face recognition and the classification of facial images into medically relevant classes like genetic syndromes. We here present an interpolation rule and an iterative algorithm for the reconstruction of images from these graphs. This is especially important if graphs have been manipulated for information processing. One such manipulation is averaging the graphs of a single syndrome, another one building a composite face from the features of various individuals. In reconstructions of averaged graphs of genetic syndromes, the patients' identities are suppressed, while the properties of the syndromes are emphasized. These reconstructions from average graphs have a much better quality than averaged images

Context dependent feature groups, a proposal for object representation.

The usefulness of contextually guided processors is investigated a little further. A more general use for binding V1 cell responses than the one in the target article is proposed, which takes into account that strong responses of these cells can mean more than the presence of lines and edges. The possibility for different grouping depending on the activities of neighboring cells is essential for the approach

Object Recognition Robust Under Translations, Deformations, and Changes in Background.

Recognition systems based on model matching using low level features often fail due to a variation in background. As a solution I present a system for the recognition of human faces independent of hairstyle. Correspondence maps between an image and a model are established by coarse-fine matching in a Gabor pyramid. These are used for hierarchical recognition

Organic Computing

Organic Computing is a research field emerging around the conviction that problems of organization in complex systems in computer science, telecommunications, neurobiology, molecular biology, ethology, and possibly even sociology can be tackled scientifically in a unified way. From the computer science point of view, the apparent ease in which living systems solve computationally difficult problems makes it inevitable to adopt strategies observed in nature for creating information processing machinery. In this book, the major ideas behind Organic Computing are delineated, together with a sparse sample of computational projects undertaken in this new field. Biological metaphors include evolution, neural networks, gene-regulatory networks, networks of brain modules, hormone system, insect swarms, and ant colonies. Applications are as diverse as system design, optimization, artificial growth, task allocation, clustering, routing, face recognition, and sign language understanding

Neural networks as a model for visual perception: what is lacking?

A central mystery of visual perception is the classical problem of invariant object recognition: Different appearances of an object can be perceived as ``the same'', despite, e.g., changes in position or illumination, distortions, or partial occlusion by other objects. This article reports on a recent email discussion over the question whether a neural network can learn the simplest of these invariances, i.e. generalize over the position of a pattern on the input layer, including the author's view on what ``learning shift-invariance'' could mean. That definition leaves the problem unsolved. A similar problem is the one of learning to detect symmetries present in an input pattern. It has been solved by a standard neural network requiring some 70000 input examples. Both leave some doubt if backpropagation learning is a realistic model for perceptual processes. Abandoning the view that a stimulus-response system showing the desired behavior must be learned from scratch, yields a radically different solution. Perception can be seen as an active process that rapidly converges from some initial state to an ordered state, which in itself codes for a percept. As an example, I will present a solution to the visual correspondence problem, which greatly alleviates both problems mentioned above

Background Invariant Face Recognition

As a contribution to handling the symbol grounding problem in AI an object recognition system is presented that is exemplified with human faces. It differs from earlier systems by a pyramidal representation and the ability to cope with structured background. 1 Introduction Despite remarkable successes within purely symbolic contexts progress in Artificial Intelligence seems to be hampered severely by the symbol grounding problem, i.e. by the difficulties of extracting symbols to be manipulated from information obtained from the real world. A conditio sine qua non for a solution of this are very robust object recognition schemes. In this paper system for the recognition of human faces independently of small distortions and background is presented. The latter becomes problematic as soon as features are employed that extend beyond one pixel. This system is formulated here in a technical fashion, but at least the essential matching mechanisms can be formulated in neuronal dynamics using t..