Class-Agnostic Counting

Nearly all existing counting methods are designed for a specific object class. Our work, however, aims to create a counting model able to count any class of object. To achieve this goal, we formulate counting as a matching problem, enabling us to exploit the image self-similarity property that naturally exists in object counting problems. We make the following three contributions: first, a Generic Matching Network (GMN) architecture that can potentially count any object in a class-agnostic manner; second, by reformulating the counting problem as one of matching objects, we can take advantage of the abundance of video data labeled for tracking, which contains natural repetitions suitable for training a counting model. Such data enables us to train the GMN. Third, to customize the GMN to different user requirements, an adapter module is used to specialize the model with minimal effort, i.e. using a few labeled examples, and adapting only a small fraction of the trained parameters. This is a form of few-shot learning, which is practical for domains where labels are limited due to requiring expert knowledge (e.g. microbiology). We demonstrate the flexibility of our method on a diverse set of existing counting benchmarks: specifically cells, cars, and human crowds. The model achieves competitive performance on cell and crowd counting datasets, and surpasses the state-of-the-art on the car dataset using only three training images. When training on the entire dataset, the proposed method outperforms all previous methods by a large margin.Comment: Asian Conference on Computer Vision (ACCV), 201

OpenCFU, a New Free and Open-Source Software to Count Cell Colonies and Other Circular Objects

Counting circular objects such as cell colonies is an important source of information for biologists. Although this task is often time-consuming and subjective, it is still predominantly performed manually. The aim of the present work is to provide a new tool to enumerate circular objects from digital pictures and video streams. Here, I demonstrate that the created program, OpenCFU, is very robust, accurate and fast. In addition, it provides control over the processing parameters and is implemented in an in- tuitive and modern interface. OpenCFU is a cross-platform and open-source software freely available at http://opencfu.sourceforge.net

Image Processing Instrumentation for Giardia lamblia Detection

Currently, the identification and enumeration of Giardia Iamblia cysts are based upon microscopic methods requiring individuals proficient in this area. It is a tedious process which consumes time that could be constructively used elsewhere. This project attempts to alleviate that burden by employing a computer to automatically process Indirect Fluorescent Antibody (IFA) prepared slides using digital image processing techniques. A computer controlled frame grabber, in conjunction with a CCD TV camera mounted on the epi-fluorescence microscope phototube, captures the light intensities of the objects in view under the microscope objective. The captured image is stored as pixels, with each pixel having a numerical value that can be altered using linear contrast enhancement and bit-slicing to emphasize the cysts and eliminate the majority of unwanted objects from the image. The altered image is then analyzed by a vector trace routine for typical area and perimeters characteristic to Giardia lamblia cysts. Objects in the image matching these characteristics are most likely cysts and are added to a running tally of the number of cysts present on the slide

1SXPS: A deep Swift X-ray Telescope point source catalog with light curves and spectra

We present the 1SXPS (Swift-XRT Point Source) catalog of 151,524 X-ray point-sources detected by the Swift-XRT in 8 years of operation. The catalog covers 1905 square degrees distributed approximately uniformly on the sky. We analyze the data in two ways. First we consider all observations individually, for which we have a typical sensitivity of ~3e-13 erg/cm2/s (0.3--10 keV). Then we co-add all data covering the same location on the sky: these images have a typical sensitivity of ~9e-14 erg/cm2/s (0.3--10 keV). Our sky coverage is nearly 2.5 times that of 3XMM-DR4, although the catalog is a factor of ~1.5 less sensitive. The median position error is 5.5" (90% confidence), including systematics. Our source detection method improves on that used in previous XRT catalogs and we report >68,000 new X-ray sources. The goals and observing strategy of the Swift satellite allow us to probe source variability on multiple timescales, and we find ~30,000 variable objects in our catalog. For every source we give positions, fluxes, time series (in four energy bands and two hardness ratios), estimates of the spectral properties, spectra and spectral fits for the brightest sources, and variability probabilities in multiple energy bands and timescales.Comment: 27 pages, 19 figures; accepted for publication in ApJS. The accompanying website, http://www.swift.ac.uk/1SXPS is live; the Vizier entry should be available shortl

Elliptical Weighted HOLICs for Weak Lensing Shear Measurement. part1:Definitions and isotropic PSF correction

We develop a new method to estimate gravitational shear by adopting an elliptical weight function to measure background galaxy images. In doing so, we introduce a new concept of "zero plane" which is an imaginal source plane where shapes of all sources are perfect circles, and regard the intrinsic shear as the result of an imaginal lensing distortion. This makes the relation between the observed shear, the intrinsic shear and lensing distortion more simple and thus higher-order calculation more easy. The elliptical weight function allows us to measure the mutiplemoment of shape of background galaxies more precisely by weighting highly to brighter parts of image and moreover to reduce systematic error due to insufficient expansion of the weight function in the original approach of KSB. Point Spread Function(PSF) correction in E-HOLICs methods becomes more complicated than those in KSB methods. In this paper we studied isotropic PSF correction in detail. By adopting the lensing distortion as the ellipticity of the weight function, we are able to show that the shear estimation in E-HOLICs method reduces to solve a polynomial in the absolute magnitude of the distortion. We compare the systematic errors between our approach and KSB using STEP2 simulation. It is confirmed that KSB method overestimate the input shear for images with large ellipticities, and E-HOLICs correctly estimate the input shear even for such images. Anisotropic PSF correction and analysis of real data will be presented in forthcoming paper.Comment: 30 pages, 8 figures, submitted to Ap