An environment for relation mining over richly annotated corpora: the case of GENIA

BACKGROUND: The biomedical domain is witnessing a rapid growth of the amount of published scientific results, which makes it increasingly difficult to filter the core information. There is a real need for support tools that 'digest' the published results and extract the most important information. RESULTS: We describe and evaluate an environment supporting the extraction of domain-specific relations, such as protein-protein interactions, from a richly-annotated corpus. We use full, deep-linguistic parsing and manually created, versatile patterns, expressing a large set of syntactic alternations, plus semantic ontology information. CONCLUSION: The experiments show that our approach described is capable of delivering high-precision results, while maintaining sufficient levels of recall. The high level of abstraction of the rules used by the system, which are considerably more powerful and versatile than finite-state approaches, allows speedy interactive development and validation

The Brightness of Colour

Background: The perception of brightness depends on spatial context: the same stimulus can appear light or dark depending on what surrounds it. A less well-known but equally important contextual phenomenon is that the colour of a stimulus can also alter its brightness. Specifically, stimuli that are more saturated (i.e. purer in colour) appear brighter than stimuli that are less saturated at the same luminance. Similarly, stimuli that are red or blue appear brighter than equiluminant yellow and green stimuli. This non-linear relationship between stimulus intensity and brightness, called the Helmholtz-Kohlrausch (HK) effect, was first described in the nineteenth century but has never been explained. Here, we take advantage of the relative simplicity of this 'illusion' to explain it and contextual effects more generally, by using a simple Bayesian ideal observer model of the human visual ecology. We also use fMRI brain scans to identify the neural correlates of brightness without changing the spatial context of the stimulus, which has complicated the interpretation of related fMRI studies.Results: Rather than modelling human vision directly, we use a Bayesian ideal observer to model human visual ecology. We show that the HK effect is a result of encoding the non-linear statistical relationship between retinal images and natural scenes that would have been experienced by the human visual system in the past. We further show that the complexity of this relationship is due to the response functions of the cone photoreceptors, which themselves are thought to represent an efficient solution to encoding the statistics of images. Finally, we show that the locus of the response to the relationship between images and scenes lies in the primary visual cortex (V1), if not earlier in the visual system, since the brightness of colours (as opposed to their luminance) accords with activity in V1 as measured with fMRI.Conclusions: The data suggest that perceptions of brightness represent a robust visual response to the likely sources of stimuli, as determined, in this instance, by the known statistical relationship between scenes and their retinal responses. While the responses of the early visual system (receptors in this case) may represent specifically the statistics of images, post receptor responses are more likely represent the statistical relationship between images and scenes. A corollary of this suggestion is that the visual cortex is adapted to relate the retinal image to behaviour given the statistics of its past interactions with the sources of retinal images: the visual cortex is adapted to the signals it receives from the eyes, and not directly to the world beyond

What Are Lightness Illusions and Why Do We See Them?

Lightness illusions are fundamental to human perception, and yet why we see them is still the focus of much research. Here we address the question by modelling not human physiology or perception directly as is typically the case but our natural visual world and the need for robust behaviour. Artificial neural networks were trained to predict the reflectance of surfaces in a synthetic ecology consisting of 3-D “dead-leaves” scenes under non-uniform illumination. The networks learned to solve this task accurately and robustly given only ambiguous sense data. In addition—and as a direct consequence of their experience—the networks also made systematic “errors” in their behaviour commensurate with human illusions, which includes brightness contrast and assimilation—although assimilation (specifically White’s illusion) only emerged when the virtual ecology included 3-D, as opposed to 2-D scenes. Subtle variations in these illusions, also found in human perception, were observed, such as the asymmetry of brightness contrast. These data suggest that “illusions” arise in humans because (i) natural stimuli are ambiguous, and (ii) this ambiguity is resolved empirically by encoding the statistical relationship between images and scenes in past visual experience. Since resolving stimulus ambiguity is a challenge faced by all visual systems, a corollary of these findings is that human illusions must be experienced by all visual animals regardless of their particular neural machinery. The data also provide a more formal definition of illusion: the condition in which the true source of a stimulus differs from what is its most likely (and thus perceived) source. As such, illusions are not fundamentally different from non-illusory percepts, all being direct manifestations of the statistical relationship between images and scenes

Automated Plant Identification using Artificial Neural Networks

This paper describes a method of training an artificial neural network, specifically a multilayer perceptron (MLP), to act as a tool to help identify plants using morphological characters collected automatically from images of botanical herbarium specimens. A methodology is presented here to provide a practical way for taxonomists to use neural networks as automated identification tools, by collating results from a collection of neural networks. A case study is provided using data extracted from specimens of the genus Tilia in the Herbarium of the Royal Botanic Gardens, Kew, UK

Automatic Extraction of Leaf Characters from Herbarium Specimens

Herbarium specimens are a vital resource in botanical taxonomy. Many herbaria are undertaking large-scale digitization projects to improve access and to preserve delicate specimens, and in doing so are creating large sets of images. Leaf characters are important for describing taxa and distinguishing between them and they can be measured from herbarium specimens. Here, we demonstrate that herbarium images can be analysed using suitable software and that leaf characters can be extracted automatically. We describe a low-cost digitization process that we use to create a set of 1,895 images of Tilia L. specimens, and novel botanical image processing software. The output of the software is a set of leaf characters. As a demonstration of this approach, we extract the length and width of a large number of leaves automatically from images of whole herbarium specimens. We show that the lengths and widths that we extract are very strongly correlated with values in a published account of cultivated species, but are also consistently smaller. We discuss some particular features of herbarium specimens that may affect the results of this form of analysis, and consider further applications to extract characters such as leaf shape and margin characters

Automatic Extraction of Leaf Characters from Herbarium Specimens

Herbarium specimens are a vital resource in botanical taxonomy. Many herbaria are undertaking large-scale digitization projects to improve access and to preserve delicate specimens, and in doing so are creating large sets of images. Leaf characters are important for describing taxa and distinguishing between them and they can be measured from herbarium specimens. Here, we demonstrate that herbarium images can be analysed using suitable software and that leaf characters can be extracted automatically. We describe a low-cost digitization process that we use to create a set of 1,895 images of Tilia L. specimens, and novel botanical image processing software. The output of the software is a set of leaf characters. As a demonstration of this approach, we extract the length and width of a large number of leaves automatically from images of whole herbarium specimens. We show that the lengths and widths that we extract are very strongly correlated with values in a published account of cultivated species, but are also consistently smaller. We discuss some particular features of herbarium specimens that may affect the results of this form of analysis, and consider further applications to extract characters such as leaf shape and margin characters