Multi-Conjugate Adaptive Optics images of the Trapezium Cluster

Multi-Conjugate Adaptive Optics (MCAO) combines the advantages of standard adaptive optics, which provides high contrast and high spatial resolution, and of wide field ~1' imaging. Up to recently, MCAO for astronomy was limited to laboratory experiments. In this paper, we present the first scientific results obtained with the first MCAO instrument put on the sky. We present a new study of the Trapezium cluster using deep MCAO images with a field of view of 1'x1' obtained at the VLT. We have used deep J, H and Ks images recently obtained with the prototype MCAO facility MAD at the VLT in order to search for new members and new multiple systems in the Trapezium cluster. On bright targets (Ks~9mag), these images allow us to reach DeltaKs~6mag as close as 0.4" We report the detection of 128 sources, including 10 new faint objects in the magnitude range between 16.1<Ks<17.9mag. In addition to all previously known multiple systems with separations greater than 0.1", we confirm the multiplicity of TCC-055. We also report the detection in J, H and Ks of a very red extended embedded protostellar object, HC419, previously detected in the thermal infrared only. The analysis of the first MCAO images obtained on the sky demonstrates not only the technical feasibility of MCAO but also its great potential and versatility in terms of scientific outputs.Comment: High resolution version available on http://arrakeen.free.fr/pub/madorion.pdf Accepted 25 Sep. 2007 for publication in A&A, 14 pages, 11 figure

The ESO-Sculptor Faint Galaxy Redshift Survey: The Photometric Sample

We present the photometric sample of a faint galaxy survey carried out in the southern hemisphere, using CCDs on the 3.60m and NTT-3.5m telescopes at La Silla (ESO). The survey area is a continuous strip of 0.2 deg x 1.53 deg located at high galactic latitude (-83 deg) in the Sculptor constellation. The photometric survey provides total magnitudes in the bands B, V (Johnson) and R (Cousins) to limiting magnitudes of 24.5, 24.0, 23.5 respectively. To these limits, the catalog contains about 9500, 12150, 13000 galaxies in B, V, R bands respectively and is the first large digital multi-colour photometric catalog at this depth. This photometric survey also provides the entry catalog for a fully-sampled redshift survey of ~ 700 galaxies with R < 20.5 (Bellanger et al. 1995). In this paper, we describe the photometric observations and the steps used in the data reduction. The analysis of objects and the star-galaxy separation with a neural network are performed using SExtractor, a new photometric software developed by E. Bertin (1996). The photometric accuracy of the resulting catalog is ~ 0.05 mag for R < 22. The differential galaxy number counts in B, V, R are in good agreement with previously published CCD studies and confirm the evidence for significant evolution at faint magnitudes as compared to a standard non evolving model (by factors 3.6, 2.6, 2.1). The galaxy colour distributions B-R, B-V of our sample show a blueing trend of ~ 0.5 mag between 21 < R < 23.5 in contrast to the V-R colour distribution where no significant evolution is observed.Comment: LATEX, 18 Postscript figures, 20 pages. To appear July 1997. Modified version of article. Abstract corrected for missing lin

Large-scale variations of the dust optical properties in the Galaxy

We present an analysis of the dust optical properties at large scale, for the whole galactic anticenter hemisphere. We used the 2MASS Extended Source Catalog to obtain the total reddening on each galaxy line of sight and we compared this value to the IRAS 100 microns surface brightness converted to extinction by Schlegel et al (1998). We performed a careful examination and correction of the possible systematic effects resulting from foreground star contamination, redshift contribution and galaxy selection bias. We also evaluated the contribution of dust temperature variations and interstellar clumpiness to our method. The correlation of the near-infrared extinction to the far-infrared optical depth shows a discrepancy for visual extinction greater than 1 mag with a ratio A_V(FIR) / A_V(gal) = 1.31 +- 0.06. We attribute this result to the presence of fluffy/composite grains characterized by an enhanced far--infrared emissivity. Our analysis, applied to half of the sky, provides new insights on the dust grains nature suggesting fluffy grains are found not only in some very specific regions but in all directions for which the visual extinction reaches about 1 mag.Comment: 10 pages, 11 figures, accepted for publication in A&

A Method of Drusen Measurement Based on the Geometry of Fundus Reflectance

BACKGROUND: The hallmarks of age-related macular degeneration, the leading cause of blindness in the developed world, are the subretinal deposits known as drusen. Drusen identification and measurement play a key role in clinical studies of this disease. Current manual methods of drusen measurement are laborious and subjective. Our purpose was to expedite clinical research with an accurate, reliable digital method. METHODS: An interactive semi-automated procedure was developed to level the macular background reflectance for the purpose of morphometric analysis of drusen. 12 color fundus photographs of patients with age-related macular degeneration and drusen were analyzed. After digitizing the photographs, the underlying background pattern in the green channel was leveled by an algorithm based on the elliptically concentric geometry of the reflectance in the normal macula: the gray scale values of all structures within defined elliptical boundaries were raised sequentially until a uniform background was obtained. Segmentation of drusen and area measurements in the central and middle subfields (1000 μm and 3000 μm diameters) were performed by uniform thresholds. Two observers using this interactive semi-automated software measured each image digitally. The mean digital measurements were compared to independent stereo fundus gradings by two expert graders (stereo Grader 1 estimated the drusen percentage in each of the 24 regions as falling into one of four standard broad ranges; stereo Grader 2 estimated drusen percentages in 1% to 5% intervals). RESULTS: The mean digital area measurements had a median standard deviation of 1.9%. The mean digital area measurements agreed with stereo Grader 1 in 22/24 cases. The 95% limits of agreement between the mean digital area measurements and the more precise stereo gradings of Grader 2 were -6.4 % to +6.8 % in the central subfield and -6.0 % to +4.5 % in the middle subfield. The mean absolute differences between the digital and stereo gradings 2 were 2.8 +/- 3.4% in the central subfield and 2.2 +/- 2.7% in the middle subfield. CONCLUSIONS: Semi-automated, supervised drusen measurements may be done reproducibly and accurately with adaptations of commercial software. This technique for macular image analysis has potential for use in clinical research

Adaptive Nonparametric Image Parsing

In this paper, we present an adaptive nonparametric solution to the image parsing task, namely annotating each image pixel with its corresponding category label. For a given test image, first, a locality-aware retrieval set is extracted from the training data based on super-pixel matching similarities, which are augmented with feature extraction for better differentiation of local super-pixels. Then, the category of each super-pixel is initialized by the majority vote of the $k$-nearest-neighbor super-pixels in the retrieval set. Instead of fixing $k$ as in traditional non-parametric approaches, here we propose a novel adaptive nonparametric approach which determines the sample-specific k for each test image. In particular, $k$ is adaptively set to be the number of the fewest nearest super-pixels which the images in the retrieval set can use to get the best category prediction. Finally, the initial super-pixel labels are further refined by contextual smoothing. Extensive experiments on challenging datasets demonstrate the superiority of the new solution over other state-of-the-art nonparametric solutions.Comment: 11 page

Fair comparison of skin detection approaches on publicly available datasets

Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the research community, nevertheless a fair comparison among approaches is very difficult due to the lack of a common benchmark and a unified testing protocol. In this work, we investigate the most recent researches in this field and we propose a fair comparison among approaches using several different datasets. The major contributions of this work are an exhaustive literature review of skin color detection approaches, a framework to evaluate and combine different skin detector approaches, whose source code is made freely available for future research, and an extensive experimental comparison among several recent methods which have also been used to define an ensemble that works well in many different problems. Experiments are carried out in 10 different datasets including more than 10000 labelled images: experimental results confirm that the best method here proposed obtains a very good performance with respect to other stand-alone approaches, without requiring ad hoc parameter tuning. A MATLAB version of the framework for testing and of the methods proposed in this paper will be freely available from https://github.com/LorisNann