Fairplay or Greed: Mandating University Responsibility Toward Student Inventors

Over twenty years have passed since the enactment of The Patent and Trademark Law Amendments Act (Bayh-Dole Act) and universities continue to struggle with their technology transfer infrastructures. Lost in that struggle are those who could be considered the backbone of university research: the students. Graduate and undergraduate students remain baffled by the patent assignment and technology transfer processes within their various institutions. Efforts should be undertaken by universities to clarify the student\u27s position in the creative process

Organization of Architectures for Cognitive Vision Systems

This volume is a post-event proceedings volume and contains selected papers based on the presentations given, and the lively discussions that ensued, during a seminar held in Dagstuhl Castle, Germany, in October 2003. Co-sponsored by ECVision, the cognitive vision network of excellence, it was organized to further strengthen cooperation between research groups from different countries working in the field of cognitive vision systems

A Nonlinear, Image-content Dependent Measure of Image Quality

In recent years, considerable research effort has been devoted to the development of useful descriptors for image quality. The attempts have been hampered by i n complete understanding of the operation of the human visual system. This has made it difficult to relate physical measures and perceptual traits. A new model for determination of image quality is proposed. Its main feature is that it tries to invoke image content into consideration. The model builds upon a theory of image linearization, which means that the information in an image can well enough be represented using linear segments or structures within local spatial regions and frequency ranges. This implies a l so a suggestion that information in an image has to do with one- dimensional correlations. This gives a possibility to separate image content from noise in images, and measure them both. Also a hypothesis is proposed that the visual system of humans does in fact perform such a linearization

A Nonlinear, Image-content Dependent Measure of Image Quality

In recent years, considerable research effort has been devoted to the development of useful descriptors for image quality. The attempts have been hampered by i n complete understanding of the operation of the human visual system. This has made it difficult to relate physical measures and perceptual traits. A new model for determination of image quality is proposed. Its main feature is that it tries to invoke image content into consideration. The model builds upon a theory of image linearization, which means that the information in an image can well enough be represented using linear segments or structures within local spatial regions and frequency ranges. This implies a l so a suggestion that information in an image has to do with one- dimensional correlations. This gives a possibility to separate image content from noise in images, and measure them both. Also a hypothesis is proposed that the visual system of humans does in fact perform such a linearization

From Multidimensional Signals to the Generation of Responses

It has become increasingly apparent that perception cannot be treated in isolation from the response generation, firstly because a very high degree of integration is required between different levels of percepts and corresponding response primitives. Secondly, it turns out that the response to be produced at a given instance is as much dependent upon the state of the system, as the percepts impinging upon the system. The state of the system is in consequence the combination of the responses produced and the percepts associated with these responses. Thirdly, it has become apparent that many classical aspects of perception, such as geometry, probably do not belong to the percept domain of a Vision system, but to the response domain. There are not yet solutions available to all of these problems. In consequence, this overview will focus on what are considered crucial problems for the future, rather than on the solutions available today. It will discuss hierarchical architectures for combination of percept and response primitives, and the concept of combined percept-response invariances as important structural elements for Vision. It will be maintained that learning is essential to obtain the necessary exibility and adaptivity. In consequence, it will be argued that invariances for the purpose of vision are not geometrical but derived from the percept-response interaction with the environment. The issue of information representation becomes extremely important in distributed structures of the types foreseen, where uncertainty of information has to be stated for update of models and associated data

An Associative Perception-Action Structure using a Localized Space Variant Information Representation

Most of the processing in vision today uses spatially invariant operations. This gives efficient and compact computing structures, with the conventional convenient separation between data and operations. This also goes well with conventional Cartesian representation of data. Currently, there is a trend towards context dependent processing in various forms. This implies that operations will no longer be spatially invariant, but vary over the image dependent upon the image content. There are many ways in which such a contextual control can be implemented. Mechanisms can be added for the modification of operator behavior within the conventional computing structure. This has been done e.g. for the implementation of adaptive filtering. In order to obtain sufficient flexibility and power in the computing structure, it is necessary to go further than that. To achieve sufficiently good adaptivity, it is necessary to ensure that sufficiently complex control strategies can be represented. It is becoming increasingly apparent that this can not be achieved through prescription or program specification of rules. The reason being that these rules will be dauntingly complex and can not be be dealt with in sufficient detail. At the same time that we require the implementation of a spatially variant processing, this implies the requirement for a spatially variant information representation. Otherwise a sufficiently effective and flexible contextual control can not be implemented. This paper outlines a new structure for effective space variant processing. It utilises a new type of localized information representation, which can be viewed as outputs from band pass filters such as wavelets. A unique and important feature is that convex regions can be built up from a single layer of associating nodes. The specification of operations is made through learning or action controlled association

Magnitude Representation of Features in Image Analysis

We have in the preceding sections studlied the use of magnitude representation for feature variables. There are several indications that such a representation may be used in biological visual systerms. The natural introduction of a nonlinearity may be most useful for many purposes. This has been studied for the implementation of penalty function operations. Such operations show great promise as they can be made very specific based on their zero-crossing property. There is a great deal of indication that inhibition or penalty mechanisms are very important in neural systems. It has e.g. been found that in the cerebellar structure almost all synapses are inhibitory. This could indicate that inhibitory or penalty matching is a primary mechanism in biological vision systems