The integration of local chromatic motion signals is sensitive to contrast polarity

Peer reviewedPublisher PD

The development and application of audit criteria for assessing knowledge exchange plans in health research grant applications.

Background: Research funders expect evidence of end user engagement and impact plans in research proposals. Drawing upon existing frameworks, we developed audit criteria to help researchers and their institutions assess the knowledge exchange plans of health research proposals. Findings: Criteria clustered around five themes: problem definition; involvement of research users; public and patient engagement; dissemination and implementation; and planning, management and evaluation of knowledge exchange. We applied these to a sample of grant applications from one research institution in the United Kingdom to demonstrate feasibility. Conclusion: Our criteria may be useful as a tool for researcher self-assessment and for research institutions to assess the quality of knowledge exchange plans and identify areas for systematic improvement

Low levels of specularity support operational color constancy, particularly when surface and illumination geometry can be inferred

We tested whether surface specularity alone supports operational color constancy—the ability to discriminate changes in illumination or reflectance. Observers viewed short animations of illuminant or reflectance changes in rendered scenes containing a single spherical surface and were asked to classify the change. Performance improved with increasing specularity, as predicted from regularities in chromatic statistics. Peak performance was impaired by spatial rearrangements of image pixels that disrupted the perception of illuminated surfaces but was maintained with increased surface complexity. The characteristic chromatic transformations that are available with nonzero specularity are useful for operational color constancy, particularly if accompanied by appropriate perceptual organization

Learning to Use Illumination Gradients as an Unambiguous Cue to Three Dimensional Shape

The luminance and colour gradients across an image are the result of complex interactions between object shape, material and illumination. Using such variations to infer object shape or surface colour is therefore a difficult problem for the visual system. We know that changes to the shape of an object can affect its perceived colour, and that shading gradients confer a sense of shape. Here we investigate if the visual system is able to effectively utilise these gradients as a cue to shape perception, even when additional cues are not available. We tested shape perception of a folded card object that contained illumination gradients in the form of shading and more subtle effects such as inter-reflections. Our results suggest that observers are able to use the gradients to make consistent shape judgements. In order to do this, observers must be given the opportunity to learn suitable assumptions about the lighting and scene. Using a variety of different training conditions, we demonstrate that learning can occur quickly and requires only coarse information. We also establish that learning does not deliver a trivial mapping between gradient and shape; rather learning leads to the acquisition of assumptions about lighting and scene parameters that subsequently allow for gradients to be used as a shape cue. The perceived shape is shown to be consistent for convex and concave versions of the object that exhibit very different shading, and also similar to that delivered by outline, a largely unrelated cue to shape. Overall our results indicate that, although gradients are less reliable than some other cues, the relationship between gradients and shape can be quickly assessed and the gradients therefore used effectively as a visual shape cue

Combining S-cone and luminance signals adversely affects discrimination of objects within backgrounds

The visual system processes objects embedded in complex scenes that vary in both luminance and colour. In such scenes, colour contributes to the segmentation of objects from backgrounds, but does it also affect perceptual organisation of object contours which are already defined by luminance signals, or are these processes unaffected by colour’s presence? We investigated if luminance and chromatic signals comparably sustain processing of objects embedded in backgrounds, by varying contrast along the luminance dimension and along the two cone-opponent colour directions. In the first experiment thresholds for object/non-object discrimination of Gaborised shapes were obtained in the presence and absence of background clutter. Contrast of the component Gabors was modulated along single colour/luminance dimensions or co-modulated along multiple dimensions simultaneously. Background clutter elevated discrimination thresholds only for combined S-(L + M) and L + M signals. The second experiment replicated and extended this finding by demonstrating that the effect was dependent on the presence of relatively high S-(L + M) contrast. These results indicate that S-(L + M) signals impair spatial vision when combined with luminance. Since S-(L + M) signals are characterised by relatively large receptive fields, this is likely to be due to an increase in the size of the integration field over which contour-defining information is summed

Funktionelle Prinzipien der Objekt- und Gesichtserkennung

Objekterkennung ist ein sehr komplexes Problem. Neben der Tatsache, dass Objekte von unterschiedlichen Ansichten aus unterschiedlich aussehen, aber trotzdem zu einem Objekt zusammengehören, besteht noch das unterbestimmte und damit eigentlich unlösbare Problem von einer zweidimensionalen Abbildung auf die dreidimen sionale Struktur des Objekts zu schliessen. Dies wird erst dadurch möglich, dass wir Vorwissen über unsere Welt besitzen und damit die Interpretationsmöglichkeiten drastisch einschränken. Objekterkennung umfasst sowohl die Kategorisierung von Objekten in eine Kategorie, als auch die Identifikation eines bestimmten Objektes. Die Anforderungen an das System sind deshalb konträr: Neben der Generalisierung steht die Spezifität. Man unterscheidet 3 Hierarchieebenen, auf denen Objekterkennung statt fin den kann. Die derzeit diskutierten Objekterkennungsmodelle unterscheiden sich vor allem darin, ob eine dreidimensionale Rekonstruktion des gesehenen Objekts stattfindet oder nicht. In dem hier favorisierten Modell werden Objekte durch eine Anzahl von spezifischen Ansichten repräsentiert, dadurch werden gemessene Reaktionszeitunterschiede und Fehlerraten auf unterschiedlichen Hierarchieebenen am besten erklärt