M.H. ter Brugge

Car license plate recognition with neural networks and fuzzy logic

A car license plate recognition system (CLPR-system) has been developed to identify vehicles by the contents of their license plate for speed-limit enforcement. This type of application puts high demands on the reliability of the CLPR-system. A combination of neural and fuzzy techniques is used to guarantee a very low error rate at an acceptable recognition rate. First experiments along highways in the Netherlands show that the system has an error rate, of 0.02% at a recognition rate of 98.51%. These results are also compared with other published CLPR-systems.

Transformational DT-CNN design from morphological specifications

Morphology provides the algebraic means to specify operations on images. Discrete-time cellular neural networks (DT-CNNs) mechanize the execution of operations on images. The paper first shows the equivalence between morphological functions and DT-CNNs. Then, the argument is extended to the synthesis of optimal DT-CNN structures from complex morphological expressions. It is shown that morphological specifications may be freely derived, to be subsequently transformed and adopted to the needs of a specific target terminology. This process of technology mapping can be automated along the well-trodden path in CAD for microelectronics.

On the representation of data for optimal learning

The key to the successful training of a neural network lies in the careful composition of the learning set. A simple method of data screening is described to verify conformance with the restrictions imposed by the target network topology and ensuing applied to the nondestructive ultrasonic diagnosis of spot welds. It is illustrated how such a screening may indicate the suitability of a learning set before application to the neural net and therefore aids in designing a proper precoder.

Design of discrete-time cellular neural networks based on mathematical morphology

Mathematical morphology is a discipline that provides a formal framework for the analysis and manipulation of images. Its theoretical foundations have been well-established in the last forty years and it has shown to be a power fool tool in the development of a large number of image processing applications. This paper shows that a lot of knowledge that is developed in the field of mathematical morphology can be applied to discrete-time cellular neural networks (DTCNNs). DTCNN equivalencies of the elementary morphological operators, which are the basic building blocks for complex image operations, are introduced and the correctness of these templates is formally proved.

CNN-Applications in Toll Driving

This paper describes a system for the automatic identification of vehicles by the contents of its license plate. The system has been developed over the last five years and is one of the four candidate systems for automatic toll collection in the Netherlands. Due to the extreme time and performance requirements placed on the system, advanced techniques like DT–CNNs and classifier combining are used. The first tests on the road show that the system correctly recognizes 85.4% of the passing vehicles, while marking the remaining 14.6% as unrecognizable.

Efficient DTCNN implementations for large-neighborhood functions

Most image processing tasks, like pattern matching, are defined in terms of large-neighborhood discrete time cellular neural network (DTCNN) templates, while most hardware implementations support only direct-neighborhood ones (3/spl times/3). Literature on DTCNN template decomposition shows that such large-neighborhood functions can be implemented as a sequence of successive direct-neighborhood templates. However, for this procedure the number of templates in the decomposition is exponential in the size of the original template. This paper shows how template decomposition is induced by the decomposition of structuring elements in the morphological design process. It is proved that an upper bound for the number of templates found in this way is quadratic in the size of the original template. For many cases more efficient and even optimal decompositions can be obtained.

Optimizing the morphological design of discrete-time cellular neural networks

The morphological design of discrete-time cellular neural networks (DTCNNs) has been presented in a companion paper (1996). DTCNN templates have been given for the elemental morphological operators. One way to obtain realizations for more complex operators is cascading the DTCNN equivalences of the constituent elemental operators. Here it is shown that this straightforward mapping mostly yields a non-optimal solution with respect to the required amount of hardware. A hardware reduction scheme of morphologically designed DTCNNs is proposed which includes the introduction of time variant templates and the identification of non-elementary expressions for which a single layer DTCNN exists.

Morphological expressions for DTCNN functions

Morphology is a mathematical discipline that provides algebraic manipulation for images. Such a formal framework is very well suited to the design of discrete-time cellular neural networks (DTCNNs). It is shown how morphological expressions can be transformed by algebraic rules to provide an efficient DTCNN implementation for complex operations on images. In fact, most specialized DTCNNs published in literature prove to be straight morphological derivatives, as exemplified in the design of a public transport ticketing system.

Composite morphological functions for DT-CNNs

Mathematical morphology is a powerful means to specify image manipulations; discrete-time cellular neural networks (DT-CNN) is the fast realization. The attractive combination has been sufficiently shown for simple problems but tends to fail in efficiency for more complex ones. The paper introduces a complement and argument swap (CAS) equivalence that allows to solve an image processing problem through a small library of representative efficient designs.