Surface Reflectance Estimation and Natural Illumination Statistics

Humans recognize optical reflectance properties of surfaces such as metal, plastic, or paper from a single image without knowledge of illumination. We develop a machine vision system to perform similar recognition tasks automatically. Reflectance estimation under unknown, arbitrary illumination proves highly underconstrained due to the variety of potential illumination distributions and surface reflectance properties. We have found that the spatial structure of real-world illumination possesses some of the statistical regularities observed in the natural image statistics literature. A human or computer vision system may be able to exploit this prior information to determine the most likely surface reflectance given an observed image. We develop an algorithm for reflectance classification under unknown real-world illumination, which learns relationships between surface reflectance and certain features (statistics) computed from a single observed image. We also develop an automatic feature selection method.

How do Humans Determine Reflectance Properties under Unknown Illumination?

Under normal viewing conditions, humans find it easy to distinguish between objects made out of different materials such as plastic, metal, or paper. Untextured materials such as these have different surface reflectance properties, including lightness and gloss. With single isolated images and unknown illumination conditions, the task of estimating surface reflectance is highly underconstrained, because many combinations of reflection and illumination are consistent with a given image. In order to work out how humans estimate surface reflectance properties, we asked subjects to match the appearance of isolated spheres taken out of their original contexts. We found that subjects were able to perform the task accurately and reliably without contextual information to specify the illumination. The spheres were rendered under a variety of artificial illuminations, such as a single point light source, and a number of photographically-captured real-world illuminations from both indoor and outdoor scenes. Subjects performed more accurately for stimuli viewed under real-world patterns of illumination than under artificial illuminations, suggesting that subjects use stored assumptions about the regularities of real-world illuminations to solve the ill-posed problem.

Shape Recipes: Scene Representations that Refer to the Image

The goal of low-level vision is to estimate an underlying scene, given an observed image. Real-world scenes (e.g., albedos or shapes) can be very complex, conventionally requiring high dimensional representations which are hard to estimate and store. We propose a low-dimensional representation, called a scene recipe, that relies on the image itself to describe the complex scene configurations. Shape recipes are an example: these are the regression coefficients that predict the bandpassed shape from bandpassed image data. We describe the benefits of this representation, and show two uses illustrating their properties: (1) we improve stereo shape estimates by learning shape recipes at low resolution and applying them at full resolution; (2) Shape recipes implicitly contain information about lighting and materials and we use them for material segmentation.

Automated Program Recognition by Graph Parsing

Recognizing standard computational structures (cliches) in a program can help an experienced programmer understand the program. We develop a graph parsing approach to automating program recognition in which programs and cliches are represented in an attributed graph grammar formalism and recognition is achieved by graph parsing. In studying this approach, we evaluate our representation's ability to suppress many common forms of variation which hinder recognition. We investigate the expressiveness of our graph grammar formalism for capturing programming cliches. We empirically and analytically study the computational cost of our recognition approach with respect to two medium-sized, real-world simulator programs.

Toward A Model Of Children's Story Comprehension

How does a person answer questions about children's stories? For example, consider 'Janet wanted Jack's paints. She looked at the picture he was painting and said 'Those paints make your picture look funny.' The question to ask is 'Why did Janet say that?'. We propose a model which answers such questions by relating the story to background real world knowledge. The model tries to generate and answer important questions about the story as it goes along. Within this model we examine two questions about the story as it goes along. Within this model we examine two problems, how to organize this real world knowledge, and how it enters into more traditional linguistic questions such as deciding noun phrase reference.

CO2Y the intelligent green solution: minimising carbon emissions by maximising shared travel opportunity.

It is in the interests of everybody that the environment is protected. In view of the recent leaps in environmental awareness it would seem timely and sensible, therefore, for people to pool vehicle resources to minimise the damaging impact of emissions. However, this is often contrary to how complex social systems behave – local decisions made by self-interested individuals often have emergent effects that are in the interests of nobody. For software engineers a major challenge is to help facilitate individual decision-making such that individual preferences can be met, which, when accumulated, minimise adverse effects at the level of the transport system. We introduce this general problem through a concrete example based on vehicle-sharing. Firstly, we outline the kind of complex transportation problem that is directly addressed by our technology (CO2y™ - pronounced “cosy”), and also show how this differs from other more basic software solutions. The CO2y™ architecture is then briefly introduced. We outline the practical advantages of the advanced, intelligent software technology that is designed to satisfy a number of individual preference criteria and thereby find appropriate matches within a population of vehicle-share users. An example scenario of use is put forward, i.e., minimisation of grey-fleets within a medium-sized company. Here we comment on some of the underlying assumptions of the scenario, and how in a detailed real-world situation such assumptions might differ between different companies, and individual users. Finally, we summarise the paper, and conclude by outlining how the problem of pooled transportation is likely to benefit from the further application of emergent, nature-inspired computing technologies. These technologies allow systems-level behaviour to be optimised with explicit representation of individual actors. With these techniques we hope to make real progress in facing the complexity challenges that transportation problems produce.

Freedom to learn: a radically revised pedagogy to facilitate lifewide learning in the academic curriculum.

Higher education has progressed fairly steadily to a common pedagogical approach which centres on the idea of alignment. In this arrangement, intended learning outcomes are identified and declared; learning activities which will enable the desired learning and development to be achieved are conceived and undertaken with the support of appropriate and effective teaching; and assessment which calls for these outcomes is (ideally) carefully designed and implemented. All three elements are aligned in advance. The same principles and practices underpinned by notions of alignment have been applied to date in most of the purposeful schemes for personal development planning. In this chapter I argue that lifewide learning, wherein learning and development often occur incidentally in multiple and varied real-world situations throughout an individual’s life course, calls for a different approach, and a different pedagogy. Higher education should therefore visualise lifewide learning as an emergent phenomenon wherein the outcomes of learning emerge later on, and are often unintended. Consequently, they cannot be defined in advance of the activities through which they are formed. The main purpose of this chapter is to offer some practical ideas to support the development of pedagogies that would enable programme designers to embed in their programmes the principle and practice of lifewide education.

Effects of High Dissolved Inorganic and Organic Carbon Availability on the Physiology of the Hard Coral Acropora millepora from the Great Barrier Reef

Coral reefs are facing major global and local threats due to climate change-induced increases in dissolved inorganic carbon (DIC) and because of land-derived increases in organic and inorganic nutrients. Recent research revealed that high availability of labile dissolved organic carbon (DOC) negatively affects scleractinian corals. Studies on the interplay of these factors, however, are lacking, but urgently needed to understand coral reef functioning under present and near future conditions. This experimental study investigated the individual and combined effects of ambient and high DIC (pCO2 403 μatm/ pHTotal 8.2 and 996 μatm/pHTotal 7.8) and DOC (added as Glucose 0 and 294 μmol L-1, background DOC concentration of 83 μmol L-1) availability on the physiology (net and gross photosynthesis, respiration, dark and light calcification, and growth) of the scleractinian coral Acropora millepora (Ehrenberg, 1834) from the Great Barrier Reef over a 16 day interval. High DIC availability did not affect photosynthesis, respiration and light calcification, but significantly reduced dark calcification and growth by 50 and 23%, respectively. High DOC availability reduced net and gross photosynthesis by 51% and 39%, respectively, but did not affect respiration. DOC addition did not influence calcification, but significantly increased growth by 42%. Combination of high DIC and high DOC availability did not affect photosynthesis, light calcification, respiration or growth, but significantly decreased dark calcification when compared to both controls and DIC treatments. On the ecosystem level, high DIC concentrations may lead to reduced accretion and growth of reefs dominated by Acropora that under elevated DOC concentrations will likely exhibit reduced primary production rates, ultimately leading to loss of hard substrate and reef erosion. It is therefore important to consider the potential impacts of elevated DOC and DIC simultaneously to assess real world scenarios, as multiple rather than single factors influence key physiological processes in coral reefs.

Novelty and Habituation: the Driving Forces in Early Stage Learning for Developmental Robotics

Meng, Q., & Lee, M. (2005). Novelty and Habituation: the Driving Forces in Early Stage Learning for Developmental Robotics. Wermter, S., Palm, G., & Elshaw, M. (Eds.), In: Biomimetic Neural Learning for Intelligent Robots: Intelligent Systems, Cognitive Robotics, and Neuroscience. (pp. 315-332). (Lecture Notes in Computer Science). Springer Berlin Heidelberg.

Novelty and Habituation: the Driving Forces in Early Stage Learning for Developmental Robotics

Q. Meng and M. H. Lee, Novelty and Habituation: the Driving Forces in Early Stage Learning for Developmental Robotics, AI-Workshop on NeuroBotics, University of Ulm, Germany. September 2004.

Rough feature selection for intelligent classifiers

Q. Shen. Rough feature selection for intelligent classifiers. LNCS Transactions on Rough Sets, 7:244-255, 2007.

Performing Feature Selection with ACO

R. Jensen, 'Performing Feature Selection with ACO. Swarm Intelligence and Data Mining,' A. Abraham, C. Grosan and V. Ramos (eds.), Studies in Computational Intelligence, vol. 34, pp. 45-73. 2006.

Subsethood-based fuzzy modelling and classification

K. Rasmani and Q. Shen. Subsethood-based fuzzy modelling and classification. Proceedings of the 2004 UK Workshop on Computational Intelligence, pages 181-188.

Computational Intelligence and Feature Selection : Rough and Fuzzy Approaches

Computational Intelligence and Feature Selection provides a high level audience with both the background and fundamental ideas behind feature selection with an emphasis on those techniques based on rough and fuzzy sets, including their hybridizations. It introduces set theory, fuzzy set theory, rough set theory, and fuzzy-rough set theory, and illustrates the power and efficacy of the feature selections described through the use of real-world applications and worked examples. Program files implementing major algorithms covered, together with the necessary instructions and datasets, are available on the Web.

ROSSI: Emergence of communication in Robots through Sensorimotor and Social Interaction

ROSSI: Emergence of communication in Robots through Sensorimotor and Social Interaction, T. Ziemke, A. Borghi, F. Anelli, C. Gianelli, F. Binkovski, G. Buccino, V. Gallese, M. Huelse, M. Lee, R. Nicoletti, D. Parisi, L. Riggio, A. Tessari, E. Sahin, International Conference on Cognitive Systems (CogSys 2008), University of Karlsruhe, Karlsruhe, Germany, 2008 Sponsorship: EU-FP7