Semantic Part Segmentation using Compositional Model combining Shape and Appearance

In this paper, we study the problem of semantic part segmentation for animals. This is more challenging than standard object detection, object segmentation and pose estimation tasks because semantic parts of animals often have similar appearance and highly varying shapes. To tackle these challenges, we build a mixture of compositional models to represent the object boundary and the boundaries of semantic parts. And we incorporate edge, appearance, and semantic part cues into the compositional model. Given part-level segmentation annotation, we develop a novel algorithm to learn a mixture of compositional models under various poses and viewpoints for certain animal classes. Furthermore, a linear complexity algorithm is offered for efficient inference of the compositional model using dynamic programming. We evaluate our method for horse and cow using a newly annotated dataset on Pascal VOC 2010 which has pixelwise part labels. Experimental results demonstrate the effectiveness of our method

Measurement of the Crab Nebula Spectrum Past 100 TeV with HAWC

We present TeV gamma-ray observations of the Crab Nebula, the standard reference source in ground-based gamma-ray astronomy, using data from the High Altitude Water Cherenkov (HAWC) Gamma-Ray Observatory. In this analysis we use two independent energy-estimation methods that utilize extensive air shower variables such as the core position, shower angle, and shower lateral energy distribution. In contrast, the previously published HAWC energy spectrum roughly estimated the shower energy with only the number of photomultipliers triggered. This new methodology yields a much improved energy resolution over the previous analysis and extends HAWC's ability to accurately measure gamma-ray energies well beyond 100 TeV. The energy spectrum of the Crab Nebula is well fit to a log parabola shape $\left(\frac{dN}{dE} = \phi_0 \left(E/\textrm{7 TeV}\right)^{-\alpha-\beta\ln\left(E/\textrm{7 TeV}\right)}\right)$ with emission up to at least 100 TeV. For the first estimator, a ground parameter that utilizes fits to the lateral distribution function to measure the charge density 40 meters from the shower axis, the best-fit values are $\phi_o$=(2.35$\pm$0.04$^{+0.20}_{-0.21}$)$\times$10$^{-13}$ (TeV cm$^2$ s)$^{-1}$, $\alpha$=2.79$\pm$0.02$^{+0.01}_{-0.03}$, and $\beta$=0.10$\pm$0.01$^{+0.01}_{-0.03}$. For the second estimator, a neural network which uses the charge distribution in annuli around the core and other variables, these values are $\phi_o$=(2.31$\pm$0.02$^{+0.32}_{-0.17}$)$\times$10$^{-13}$ (TeV cm$^2$ s)$^{-1}$, $\alpha$=2.73$\pm$0.02$^{+0.03}_{-0.02}$, and $\beta$=0.06$\pm$0.01$\pm$0.02. The first set of uncertainties are statistical; the second set are systematic. Both methods yield compatible results. These measurements are the highest-energy observation of a gamma-ray source to date.Comment: published in Ap

Automated Fovea Detection Based on Unsupervised Retinal Vessel Segmentation Method

The Computer Assisted Diagnosis systems could save workloads and give objective diagnostic to ophthalmologists. At first level of automated screening of systems feature extraction is the fundamental step. One of these retinal features is the fovea. The fovea is a small fossa on the fundus, which is represented by a deep-red or red-brown color in color retinal images. By observing retinal images, it appears that the main vessels diverge from the optic nerve head and follow a specific course that can be geometrically modeled as a parabola, with a common vertex inside the optic nerve head and the fovea located along the apex of this parabola curve. Therefore, based on this assumption, the main retinal blood vessels are segmented and fitted to a parabolic model. With respect to the core vascular structure, we can thus detect fovea in the fundus images. For the vessel segmentation, our algorithm addresses the image locally where homogeneity of features is more likely to occur. The algorithm is composed of 4 steps: multi-overlapping windows, local Radon transform, vessel validation, and parabolic fitting. In order to extract blood vessels, sub-vessels should be extracted in local windows. The high contrast between blood vessels and image background in the images cause the vessels to be associated with peaks in the Radon space. The largest vessels, using a high threshold of the Radon transform, determines the main course or overall configuration of the blood vessels which when fitted to a parabola, leads to the future localization of the fovea. In effect, with an accurate fit, the fovea normally lies along the slope joining the vertex and the focus. The darkest region along this line is the indicative of the fovea. To evaluate our method, we used 220 fundus images from a rural database (MUMS-DB) and one public one (DRIVE). The results show that, among 20 images of the first public database (DRIVE) we detected fovea in 85% of them. Also for the MUMS-DB database among 200 images we detect fovea correctly in 83% on them