Sabanci-Okan system at ImageClef 2011: plant identication task

We describe our participation in the plant identication task of ImageClef 2011. Our approach employs a variety of texture, shape as well as color descriptors. Due to the morphometric properties of plants, mathematical morphology has been advocated as the main methodology for texture characterization, supported by a multitude of contour-based shape and color features. We submitted a single run, where the focus has been almost exclusively on scan and scan-like images, due primarily to lack of time. Moreover, special care has been taken to obtain a fully automatic system, operating only on image data. While our photo results are low, we consider our submission successful, since besides being our rst attempt, our accuracy is the highest when considering the average of the scan and scan-like results, upon which we had concentrated our eorts

Morphological granulometry for classification of evolving and ordered texture images.

In this work we investigate the use of morphological granulometric moments as texture descriptors to predict time or class of texture images which evolve over time or follow an intrinsic ordering of textures. A cubic polynomial regression was used to model each of several granulometric moments as a function of time or class. These models are then combined and used to predict time or class. The methodology was developed on synthetic images of evolving textures and then successfully applied to classify a sequence of corrosion images to a point on an evolution time scale. Classification performance of the new regression approach is compared to that of linear discriminant analysis, neural networks and support vector machines. We also apply our method to images of black tea leaves, which are ordered according to granule size, and very high classification accuracy was attained compared to existing published results for these images. It was also found that granulometric moments provide much improved classification compared to grey level co-occurrence features for shape-based texture images

The Dynamical Distinction between Elliptical and Lenticular Galaxies in Distant Clusters: Further Evidence for the Recent Origin of S0 Galaxies

We examine resolved spectroscopic data obtained with the Keck II telescope for 44 spheroidal galaxies in the fields of two rich clusters, Cl0024+16 (z=0.40) and MS0451-03 (z=0.54), and contrast this with similar data for 23 galaxies within the redshift interval 0.3<z<0.65 in the GOODS northern field. For each galaxy we examine the case for systemic rotation, derive central stellar velocity dispersions sigma and photometric ellipticities, epsilon. Using morphological classifications obtained via Hubble Space Telescope imaging as the basis, we explore the utility of our kinematic quantities in distinguishing between pressure-supported ellipticals and rotationally-supported lenticulars (S0s). We demonstrate the reliability of using the v/(1-epsilon) vs sigma and v/sigma vs epsilon distributions as discriminators, finding that the two criteria correctly identify 63%+-3% and 80%+-2% of S0s at z~0.5, respectively, along with 76%+8-3% and 79%+-2% of ellipticals. We test these diagnostics using equivalent local data in the Coma cluster, and find that the diagnostics are similarly accurate at z=0. Our measured accuracies are comparable to the accuracy of visual classification of morphologies, but avoid the band-shifting and surface brightness effects that hinder visual classification at high redshifts. As an example application of our kinematic discriminators, we then examine the morphology-density relation for elliptical and S0 galaxies separately at z~0.5. We confirm, from kinematic data alone, the recent growth of rotationally-supported spheroidals. We discuss the feasibility of extending the method to a more comprehensive study of cluster and field galaxies to z~1, in order to verify in detail the recent density-dependent growth of S0 galaxies.Comment: 7 pages, 4 figures, updated with version accepted to Ap

The Morphological Content of Ten EDisCS Clusters at 0.5 < z < 0.8

We describe Hubble Space Telescope (HST) imaging of 10 of the 20 ESO Distant Cluster Survey (EDisCS) fields. Each ~40 square arcminute field was imaged in the F814W filter with the Advanced Camera for Surveys Wide Field Camera. Based on these data, we present visual morphological classifications for the ~920 sources per field that are brighter than I_auto=23 mag. We use these classifications to quantify the morphological content of 10 intermediate-redshift (0.5 < z < 0.8) galaxy clusters within the HST survey region. The EDisCS results, combined with previously published data from seven higher redshift clusters, show no statistically significant evidence for evolution in the mean fractions of elliptical, S0, and late-type (Sp+Irr) galaxies in clusters over the redshift range 0.5 < z < 1.2. In contrast, existing studies of lower redshift clusters have revealed a factor of ~2 increase in the typical S0 fraction between z=0.4 and z=0, accompanied by a commensurate decrease in the Sp+Irr fraction and no evolution in the elliptical fraction. The EDisCS clusters demonstrate that cluster morphological fractions plateau beyond z ~ 0.4. They also exhibit a mild correlation between morphological content and cluster velocity dispersion, highlighting the importance of careful sample selection in evaluating evolution. We discuss these findings in the context of a recently proposed scenario in which the fractions of passive (E,S0) and star-forming (Sp,Irr) galaxies are determined primarily by the growth history of clusters.Comment: 18 pages, 7 figures; To be published in ApJ; minor changes made to table label

Classification of ordered texture images using regression modelling and granulometric features

Structural information available from the granulometry of an image has been used widely in image texture analysis and classification. In this paper we present a method for classifying texture images which follow an intrinsic ordering of textures, using polynomial regression to express granulometric moments as a function of class label. Separate models are built for each individual moment and combined for back-prediction of the class label of a new image. The methodology was developed on synthetic images of evolving textures and tested using real images of 8 different grades of cut-tear-curl black tea leaves. For comparison, grey level co-occurrence (GLCM) based features were also computed, and both feature types were used in a range of classifiers including the regression approach. Experimental results demonstrate the superiority of the granulometric moments over GLCM-based features for classifying these tea images