Eritrea and Eritrean Refugees

This Dutch book was produced for the Dutch Central Agency for the Reception of Asylum Seekers (COA), in order to give broad background information on Eritrea and Eritrean refugees for COA employees and other interested parties, such as municipalities. It covers topics such as history, politics, migration and education. The book is a collaborative effort by different authors

An Exponential filter model predicts lightness illusions

Lightness, or perceived reflectance of a surface, is influenced by surrounding context. This is demonstrated by the Simultaneous Contrast Illusion (SCI), where a gray patch is perceived lighter against a black background and vice versa. Conversely, assimilation is where the lightness of the target patch moves toward that of the bounding areas and can be demonstrated in White's effect. Blakeslee and McCourt (1999) introduced an oriented difference-of-Gaussian (ODOG) model that is able to account for both contrast and assimilation in a number of lightness illusions and that has been subsequently improved using localized normalization techniques. We introduce a model inspired by image statistics that is based on a family of exponential filters, with kernels spanning across multiple sizes and shapes. We include an optional second stage of normalization based on contrast gain control. Our model was tested on a well-known set of lightness illusions that have previously been used to evaluate ODOG and its variants, and model lightness values were compared with typical human data. We investigate whether predictive success depends on filters of a particular size or shape and whether pooling information across filters can improve performance. The best single filter correctly predicted the direction of lightness effects for 21 out of 27 illusions. Combining two filters together increased the best performance to 23, with asymptotic performance at 24 for an arbitrarily large combination of filter outputs. While normalization improved prediction magnitudes, it only slightly improved overall scores in direction predictions. The prediction performance of 24 out of 27 illusions equals that of the best performing ODOG variant, with greater parsimony. Our model shows that V1-style orientation-selectivity is not necessary to account for lightness illusions and that a low-level model based on image statistics is able to account for a wide range of both contrast and assimilation effects.15 page(s

Color Invariant Snakes

Snakes provide high-level information in the form of continuity constraints and minimum energy constraints related to the contour shape and image features. These image features are usually based on intensity edges. However, intensity edges may appear in the scene without a material/color transition to support it. As a consequence, when using intensity edges as image features, the image segmentation results obtained by snakes may be negatively affected by the imaging-process (e.g. shadows, shading and highlights) . In this paper, we aim at using color invariant gradient information to guide the deformation process to obtain snake boundaries which correspond to material boundaries in images discounting the disturbing influences of surface orientation, illumination, shadows and highlights. Experiments conducted on various color images show that the proposed color invariant snake successfully find material contours discounting other "accidental" edges types (e.g. shadows, shading and highlight transitions). Comparison with intensity-based snakes shows that the intensity-based snake is dramatically outperformed by the presented color invariant snake

Spatially pooled contrast responses predict neural and perceptual similarity of naturalistic image categories.

The visual world is complex and continuously changing. Yet, our brain transforms patterns of light falling on our retina into a coherent percept within a few hundred milliseconds. Possibly, low-level neural responses already carry substantial information to facilitate rapid characterization of the visual input. Here, we computationally estimated low-level contrast responses to computer-generated naturalistic images, and tested whether spatial pooling of these responses could predict image similarity at the neural and behavioral level. Using EEG, we show that statistics derived from pooled responses explain a large amount of variance between single-image evoked potentials (ERPs) in individual subjects. Dissimilarity analysis on multi-electrode ERPs demonstrated that large differences between images in pooled response statistics are predictive of more dissimilar patterns of evoked activity, whereas images with little difference in statistics give rise to highly similar evoked activity patterns. In a separate behavioral experiment, images with large differences in statistics were judged as different categories, whereas images with little differences were confused. These findings suggest that statistics derived from low-level contrast responses can be extracted in early visual processing and can be relevant for rapid judgment of visual similarity. We compared our results with two other, well- known contrast statistics: Fourier power spectra and higher-order properties of contrast distributions (skewness and kurtosis). Interestingly, whereas these statistics allow for accurate image categorization, they do not predict ERP response patterns or behavioral categorization confusions. These converging computational, neural and behavioral results suggest that statistics of pooled contrast responses contain information that corresponds with perceived visual similarity in a rapid, low-level categorization task