Focusing and orienting spatial attention differently modulate crowding in central and peripheral vision

The allocation of attentional resources to a particular location or object in space involves two distinct processes: an orienting process and a focusing process. Indeed, it has been demonstrated that performance of different visual tasks can be improved when a cue, such as a dot, anticipates the position of the target (orienting), or when its dimensions (as in the case of a small square) inform about the size of the attentional window (focusing). Here, we examine the role of these two components of visuo-spatial attention (orienting and focusing) in modulating crowding in peripheral (Experiment 1 and Experiment 3a) and foveal (Experiment 2 and Experiment 3b) vision. The task required to discriminate the orientation of a target letter "T,'' close to acuity threshold, presented with left and right "H'' flankers, as a function of target-flanker distance. Three cue types have been used: a red dot, a small square, and a big square. In peripheral vision (Experiment 1 and Experiment 3a), we found a significant improvement with the red dot and no advantage when a small square was used as a cue. In central vision (Experiment 2 and Experiment 3b), only the small square significantly improved participants' performance, reducing the critical distance needed to recover target identification. Taken together, the results indicate a behavioral dissociation of orienting and focusing attention in their capability of modulating crowding. In particular, we confirmed that orientation of attention can modulate crowding in visual periphery, while we found that focal attention can modulate foveal crowdin

Accurate and reliable segmentation of the optic disc in digital fundus images

We describe a complete pipeline for the detection and accurate automatic segmentation of the optic disc in digital fundus images. This procedure provides separation of vascular information and accurate inpainting of vessel-removed images, symmetry-based optic disc localization, and fitting of incrementally complex contour models at increasing resolutions using information related to inpainted images and vessel masks. Validation experiments, performed on a large dataset of images of healthy and pathological eyes, annotated by experts and partially graded with a quality label, demonstrate the good performances of the proposed approach. The method is able to detect the optic disc and trace its contours better than the other systems presented in the literature and tested on the same data. The average error in the obtained contour masks is reasonably close to the interoperator errors and suitable for practical applications. The optic disc segmentation pipeline is currently integrated in a complete software suite for the semiautomatic quantification of retinal vessel properties from fundus camera images (VAMPIRE)

ESO Imaging Survey: infrared observations of CDF-S and HDF-S

This paper presents infrared data obtained from observations carried out at the ESO 3.5m New Technology Telescope (NTT) of the Hubble Deep Field South (HDF-S) and the Chandra Deep Field South (CDF-S). These data were taken as part of the ESO Imaging Survey (EIS) program, a public survey conducted by ESO to promote follow-up observations with the VLT. In the HDF-S field the infrared observations cover an area of ~53 square arcmin, encompassing the HST WFPC2 and STIS fields, in the JHKs passbands. The seeing measured in the final stacked images ranges from 0.79" to 1.22" and the median limiting magnitudes (AB system, 2" aperture, 5sigma detection limit) are J_AB~23.0, H_AB~22.8 and K_AB~23.0 mag. Less complete data are also available in JKs for the adjacent HST NICMOS field. For CDF-S, the infrared observations cover a total area of \~100 square arcmin, reaching median limiting magnitudes (as defined above) of J_AB~23.6 and K_AB~22.7 mag. For one CDF-S field H-band data are also available. This paper describes the observations and presents the results of new reductions carried out entirely through the un-supervised, high-throughput EIS Data Reduction System and its associated EIS/MVM C++-based image processing library developed, over the past 5 years, by the EIS project and now publicly available. The paper also presents source catalogs extracted from the final co-added images which are used to evaluate the scientific quality of the survey products, and hence the performance of the software. This is done comparing the results obtained in the present work with those obtained by other authors from independent data and/or reductions carried out with different software packages and techniques. The final science-grade catalogs and co-added images are available at CDS.Comment: Accepted for publication in A&A, 13 pages, 12 figures; a full resolution version of the paper is available from http://www.astro.ku.dk/~lisbeth/eisdata/papers/4528.pdf ; related catalogs and images are available through http://www.astro.ku.dk/~lisbeth/eisdata

Detecting Differential Item and Step Functioning with Rating Scale and Partial Credit Trees

Several statistical procedures have been suggested for detecting differential item functioning (DIF) and differential step functioning (DSF) in polytomous items. However, standard procedures are designed for the comparison of pre-specified reference and focal groups, such as males and females. Here, we propose a framework for the detection of DIF and DSF in polytomous items under the rating scale and partial credit model, that employs a model-based recursive partitioning algorithm. In contrast to existing procedures, with this approach no pre-specification of reference and focal groups is necessary, because they are detected in a data-driven way. The resulting groups are characterized by (combinations of) covariates and thus directly interpretable. The statistical background and construction of the new procedures are introduced along with an instructive example. Four simulation studies illustrate and compare their statistical properties to the well-established likelihood ratio test (LRT). While both the LRT and the new procedures respect a given significance level, the new procedures are in most cases equally (simple DIF groups) or more powerful (complex DIF groups) and can also detect DSF. The sensitivity to model misspecification is investigated. An application example with empirical data illustrates the practical use. A software implementation of the new procedures is freely available in the R system for statistical computing