The magic words: Using computers to uncover mental associations for use in magic trick design.

The use of computational systems to aid in the design of magic tricks has been previously explored. Here further steps are taken in this direction, introducing the use of computer technology as a natural language data sourcing and processing tool for magic trick design purposes. Crowd sourcing of psychological concepts is investigated; further, the role of human associative memory and its exploitation in magical effects is explored. A new trick is developed and evaluated: a physical card trick partially designed by a computational system configured to search for and explore conceptual spaces readily understood by spectators.

Manufacturing Magic and Computational Creativity.

This paper describes techniques in computational creativity, blending mathematical modeling and psychological insight, to generate new magic tricks. The details of an explicit computational framework capable of creating new magic tricks are summarized, and evaluated against a range of contemporary theories about what constitutes a creative system. To allow further development of the proposed system we situate this approach to the generation of magic in the wider context of other areas of application in computational creativity in performance arts. We show how approaches in these domains could be incorporated to enhance future magic generation systems, and critically review possible future applications of such magic generating computers.

Magic in the machine: a computational magician's assistant.

A human magician blends science, psychology, and performance to create a magical effect. In this paper we explore what can be achieved when that human intelligence is replaced or assisted by machine intelligence. Magical effects are all in some form based on hidden mathematical, scientific, or psychological principles; often the parameters controlling these underpinning techniques are hard for a magician to blend to maximize the magical effect required. The complexity is often caused by interacting and often conflicting physical and psychological constraints that need to be optimally balanced. Normally this tuning is done by trial and error, combined with human intuitions. Here we focus on applying Artificial Intelligence methods to the creation and optimization of magic tricks exploiting mathematical principles. We use experimentally derived data about particular perceptual and cognitive features, combined with a model of the underlying mathematical process to provide a psychologically valid metric to allow optimization of magical impact. In the paper we introduce our optimization methodology and describe how it can be flexibly applied to a range of different types of mathematics based tricks. We also provide two case studies as exemplars of the methodology at work: a magical jigsaw, and a mind reading card trick effect. We evaluate each trick created through testing in laboratory and public performances, and further demonstrate the real world efficacy of our approach for professional performers through sales of the tricks in a reputable magic shop in London.

The biological significance of color constancy: an agent-based model with bees foraging from flowers under varied illumination.

The perceived color of an object depends on its spectral reflectance and the spectral composition of the illuminant. Thus when the illumination changes, the light reflected from the object also varies. This would result in a different color sensation if no color constancy mechanism is put in place-that is, the ability to form consistent representation of colors across various illuminants and background scenes. We explore the quantitative benefits of various color constancy algorithms in an agent-based model of foraging bees, where agents select flower color based on reward. Each simulation is based on 100 "meadows" with five randomly selected flower species with empirically determined spectral reflectance properties, and each flower species is associated with realistic distributions of nectar rewards. Simulated foraging bees memorize the colors of flowers that they have experienced as most rewarding, and their task is to discriminate against other flower colors with lower rewards, even in the face of changing illumination conditions. We compared the performance of von Kries, White Patch, and Gray World constancy models with (hypothetical) bees with perfect color constancy, and color-blind bees. A bee equipped with trichromatic color vision but no color constancy performed only ∼20% better than a color-blind bee (relative to a maximum improvement at 100% for perfect color constancy), whereas the most powerful recovery of reflectance in the face of changing illumination was generated by a combination of von Kries photoreceptor adaptation and a White Patch calibration (∼30% improvement relative to a bee without color constancy). However, none of the tested algorithms generated perfect color constancy.

Judging political affiliation from faces of UK MPs.

Subjects were shown photographs of UK MPs' faces and asked to judge their political affiliations. Participants were unable to correctly distinguish between Conservative and Labour politicians. However, their responses were used to create computer-generated idealised faces representative of each party, which independent evaluators could correctly identify. These faces give an indication of the mental images we might reference when imagining MPs from the two main UK political parties.

Biologically inspired framework for spatial and spectral velocity estimations.

The multichannel gradient model (McGM) is an established, biologically plausible framework for the robust extraction of image velocity. Here we describe the McGM extension into color space and report the resulting performance improvement. Our new model, in contrast to existing approaches that process color channels separately, incorporates spectral energy measures to form a local description of the stimulus chromatic spatio-temporal structure from which we can recover both spatial and spectral velocities. We present a range of comparative experiments on synthetic and natural test data that demonstrate that our new method reduces errors and is more robust over a range of viewing environments.

FReD: the floral reflectance database--a web portal for analyses of flower colour.

BACKGROUND: Flower colour is of great importance in various fields relating to floral biology and pollinator behaviour. However, subjective human judgements of flower colour may be inaccurate and are irrelevant to the ecology and vision of the flower's pollinators. For precise, detailed information about the colours of flowers, a full reflectance spectrum for the flower of interest should be used rather than relying on such human assessments. METHODOLOGY/PRINCIPAL FINDINGS: The Floral Reflectance Database (FReD) has been developed to make an extensive collection of such data available to researchers. It is freely available at http://www.reflectance.co.uk. The database allows users to download spectral reflectance data for flower species collected from all over the world. These could, for example, be used in modelling interactions between pollinator vision and plant signals, or analyses of flower colours in various habitats. The database contains functions for calculating flower colour loci according to widely-used models of bee colour space, reflectance graphs of the spectra and an option to search for flowers with similar colours in bee colour space. CONCLUSIONS/SIGNIFICANCE: The Floral Reflectance Database is a valuable new tool for researchers interested in the colours of flowers and their association with pollinator colour vision, containing raw spectral reflectance data for a large number of flower species.

Identifying regions that carry the best information about global facial configurations.

Regions of the face are not equally important in conveying information about configural change. The bubbles spatial occlusion technique has proved to be a good method for revealing which areas carry diagnostic facial information for different perceptual categorization tasks. We have applied it here within a performance-driven mimicry system implemented using a computer-generated model of the face designed to automatically retarget the behavior of one face onto another face. Our bubbles technique, mapping an occluded face into a PCA model of the same face, revealed the areas around and including the mouth and eyebrows as the most important for facial image reconstruction. These regions overlapped with but interestingly were not identical to areas of maximum pixel-value variance. Here we show a system that is indifferent to stimulus content and uses the correlation between vectors in face space as a criterion, rather than just pixel-value correlation, identifies the eyebrows and mouth as important regions. This implies that the importance of the eyebrows and the mouth in dynamic face perception may depend not on the information content of the features per se but on the degree to which these regions of the face provide information about the global form of the face.

A semi-automated approach to balancing of bottom-up salience for predicting change detection performance.

Previous change blindness studies have failed to address the importance of balancing low-level visual salience when producing experimental stimuli for a change detection task. Therefore, prior results suggesting that top-down processes influence change detection may be contaminated by low-level saliency differences in the stimuli used. Here we present a novel technique for generating semi-automated balanced modifications to a scene, handled by a genetic algorithm coupled with a computational model for bottom-up saliency. The saliency model obtains global saliency values for input images by analysing peaks in feature contrast maps. This quantification approach facilitates the generation of experimental stimuli using natural images and is an extension to a recently investigated approach using only low-level stimuli (Verma & McOwan, 2009). In this exemplar study, subjects were asked to detect changes in a flicker task containing the original scene image (A) and a synthesised modified version (A'). We find under the conditions where global saliency is balanced between A and A' as well as between all modifications (all instantiations of A') that low-level saliency is indeed a reasonable estimator of change detection performance in comparison with high-level measures such as mouse-click densities. When the saliency of the changes are similar, addition/removal changes are detected more readily than colour changes to the scene.

Motion-induced position shifts in global dynamic Gabor arrays.

Objects in motion appear shifted in space. For global motion stimuli we can ask whether the shift depends on the local or global motion. We constructed arrays of randomly oriented Gaussian enveloped drifting sine gratings (dynamic Gabors) whose speed was set such that the normal component of motion was consistent with a single global velocity. The array appears shifted in space in the direction of the global motion. The size of the shift is the same as for arrays of uniformly oriented dynamic Gabors that are moving in the same direction at the same global speed. Arrays made up of vertically oriented gratings whose speeds were set to the horizontal component of the random array elements were shifted less far. This shows that motion-induced position shifts of coherently moving surface patches are generated after the completion of the global motion computation.

Generating customised experimental stimuli for visual search using Genetic Algorithms shows evidence for a continuum of search efficiency.

Here we present a new empirically validated computational paradigm that generates on demand stimuli with user-defined levels of salience for use in visual search tasks. Combining a Genetic Algorithm with a biologically motivated model for image saliency we are able to breed a range of customised texture elements for use in psychophysical experiments. We review these psychophysical studies, showing for the first time that an explicit and predictable continuum of search efficiency exists in human visual search.

A real-time automated system for the recognition of human facial expressions.

A fully automated, multistage system for real-time recognition of facial expression is presented. The system uses facial motion to characterize monochrome frontal views of facial expressions and is able to operate effectively in cluttered and dynamic scenes, recognizing the six emotions universally associated with unique facial expressions, namely happiness, sadness, disgust, surprise, fear, and anger. Faces are located using a spatial ratio template tracker algorithm. Optical flow of the face is subsequently determined using a real-time implementation of a robust gradient model. The expression recognition system then averages facial velocity information over identified regions of the face and cancels out rigid head motion by taking ratios of this averaged motion. The motion signatures produced are then classified using Support Vector Machines as either nonexpressive or as one of the six basic emotions. The completed system is demonstrated in two simple affective computing applications that respond in real-time to the facial expressions of the user, thereby providing the potential for improvements in the interaction between a computer user and technology.