Comparing Feature Detectors: A bias in the repeatability criteria, and how to correct it

Most computer vision application rely on algorithms finding local correspondences between different images. These algorithms detect and compare stable local invariant descriptors centered at scale-invariant keypoints. Because of the importance of the problem, new keypoint detectors and descriptors are constantly being proposed, each one claiming to perform better (or to be complementary) to the preceding ones. This raises the question of a fair comparison between very diverse methods. This evaluation has been mainly based on a repeatability criterion of the keypoints under a series of image perturbations (blur, illumination, noise, rotations, homotheties, homographies, etc). In this paper, we argue that the classic repeatability criterion is biased towards algorithms producing redundant overlapped detections. To compensate this bias, we propose a variant of the repeatability rate taking into account the descriptors overlap. We apply this variant to revisit the popular benchmark by Mikolajczyk et al., on classic and new feature detectors. Experimental evidence shows that the hierarchy of these feature detectors is severely disrupted by the amended comparator.Comment: Fixed typo in affiliation

The Chandra Source Catalog

The Chandra Source Catalog (CSC) is a general purpose virtual X-ray astrophysics facility that provides access to a carefully selected set of generally useful quantities for individual X-ray sources, and is designed to satisfy the needs of a broad-based group of scientists, including those who may be less familiar with astronomical data analysis in the X-ray regime. The first release of the CSC includes information about 94,676 distinct X-ray sources detected in a subset of public ACIS imaging observations from roughly the first eight years of the Chandra mission. This release of the catalog includes point and compact sources with observed spatial extents <~ 30''. The catalog (1) provides access to the best estimates of the X-ray source properties for detected sources, with good scientific fidelity, and directly supports scientific analysis using the individual source data; (2) facilitates analysis of a wide range of statistical properties for classes of X-ray sources; and (3) provides efficient access to calibrated observational data and ancillary data products for individual X-ray sources, so that users can perform detailed further analysis using existing tools. The catalog includes real X-ray sources detected with flux estimates that are at least 3 times their estimated 1 sigma uncertainties in at least one energy band, while maintaining the number of spurious sources at a level of <~ 1 false source per field for a 100 ks observation. For each detected source, the CSC provides commonly tabulated quantities, including source position, extent, multi-band fluxes, hardness ratios, and variability statistics, derived from the observations in which the source is detected. In addition to these traditional catalog elements, for each X-ray source the CSC includes an extensive set of file-based data products that can be manipulated interactively.Comment: To appear in The Astrophysical Journal Supplement Series, 53 pages, 27 figure

Red blood cell segmentation and classification method using MATLAB

Red blood cells (RBCs) are the most important kind of blood cell. Its diagnosis is very important process for early detection of related disease such as malaria and anemia before suitable follow up treatment can be proceed. Some of the human disease can be showed by counting the number of red blood cells. Red blood cell count gives the vital information that help diagnosis many of the patient’s sickness. Conventional method under blood smears RBC diagnosis is applying light microscope conducted by pathologist. This method is time-consuming and laborious. In this project an automated RBC counting is proposed to speed up the time consumption and to reduce the potential of the wrongly identified RBC. Initially the RBC goes for image pre-processing which involved global thresholding. Then it continues with RBCs counting by using two different algorithms which are the watershed segmentation based on distance transform, and the second one is the artificial neural network (ANN) classification with fitting application depend on regression method. Before applying ANN classification there are step needed to get feature extraction data that are the data extraction using moment invariant. There are still weaknesses and constraints due to the image itself such as color similarity, weak edge boundary, overlapping condition, and image quality. Thus, more study must be done to handle those matters to produce strong analysis approach for medical diagnosis purpose. This project build a better solution and help to improve the current methods so that it can be more capable, robust, and effective whenever any sample of blood cell is analyzed. At the end of this project it conducted comparison between 20 images of blood samples taken from the medical electronic laboratory in Universiti Tun Hussein Onn Malaysia (UTHM). The proposed method has been tested on blood cell images and the effectiveness and reliability of each of the counting method has been demonstrated

Active shape models with focus on overlapping problems applied to plant detection and soil pore analysis

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The Ultraviolet Attenuation Law in Backlit Spiral Galaxies

(Abridged) The effective extinction law (attenuation behavior) in galaxies in the emitted ultraviolet is well known only for actively star-forming objects and combines effects of the grain properties, fine structure in the dust distribution, and relative distributions of stars and dust. We use GALEX, XMM Optical Monitor, and HST data to explore the UV attenuation in the outer parts of spiral disks which are backlit by other UV-bright galaxies, starting with candidates provided by Galaxy Zoo participants. Our analysis incorporates galaxy symmetry, using non-overlapping regions of each galaxy to derive error estimates on the attenuation measurements. The entire sample has an attenuation law close to the Calzetti et al. (1994) form; the UV slope for the overall sample is substantially shallower than found by Wild et al. (2011), a reasonable match to the more distant galaxies in our sample but not to the weighted combination including NGC 2207. The nearby, bright spiral NGC 2207 alone gives accuracy almost equal to the rest of our sample, and its outer arms have a very low level of foreground starlight. This "grey" law can be produced from the distribution of dust alone, without a necessary contribution from differential escape of stars from dense clouds. The extrapolation needed to compare attenution between backlit galaxies at moderate redshifts, and local systems from SDSS data, is mild enough to allow use of galaxy overlaps to trace the cosmic history of dust. For NGC 2207, the covering factor of clouds with small optical attenuation becomes a dominant factor farther into the ultraviolet, which opens the possibility that widespread diffuse dust dominates over dust in star-forming regions deep into the ultraviolet. Comparison with published radiative-transfer models indicates that the role of dust clumping dominates over differences in grain populations, at this spatial resolution.Comment: In press, Astronomical Journa

The Functional Microarchitecture of the Mouse Barrel Cortex

Cortical maps, consisting of orderly arrangements of functional columns, are a hallmark of the organization of the cerebral cortex. However, the microorganization of cortical maps at the level of single neurons is not known, mainly because of the limitations of available mapping techniques. Here, we used bulk loading of Ca2+ indicators combined with two-photon microscopy to image the activity of multiple single neurons in layer (L) 2/3 of the mouse barrel cortex in vivo. We developed methods that reliably detect single action potentials in approximately half of the imaged neurons in L2/3. This allowed us to measure the spiking probability following whisker deflection and thus map the whisker selectivity for multiple neurons with known spatial relationships. At the level of neuronal populations, the whisker map varied smoothly across the surface of the cortex, within and between the barrels. However, the whisker selectivity of individual neurons recorded simultaneously differed greatly, even for nearest neighbors. Trial-to-trial correlations between pairs of neurons were high over distances spanning multiple cortical columns. Our data suggest that the response properties of individual neurons are shaped by highly specific subcolumnar circuits and the momentary intrinsic state of the neocortex