Photometric Attributes Research Articles

Cool green-emissive Y2Si2O7:Tb3+ nanophosphor: auto-combustion synthesis and structural and photoluminescence characteristics with good thermal stability for lighting applications.

A cheap, versatile, sustainable and energy-efficient gel-combustion method was applied to develop a series of green-emitting down-converted Y2Si2O7:Tb3+ (YPS:Tb3+) nanophosphors. Employing XRD-based Rietveld refinement approach, the phase purity and crystallographic evaluation of the produced phosphor were conducted, revealing a triclinic crystal with P1̄ space group. EDX and TEM analyses were performed on the synthesized samples to determine their elemental composition and morphological properties. Diffuse reflectance spectra yielded 5.61 eV and 5.79 eV optical energy band gaps for the host and the optimized (0.04 mole of Tb3+) sample, respectively. UV light has the ability to excite the nanocrystalline phosphor in an efficient manner, leading to significant luminosity qualities attributed to the radiative relaxation of 5D4 → 7FJ (J = 6, 5, 4, 3). The bi-exponential decay function was derived by the PL decay curves. With an activation energy of 0.2206 eV, the Y1.96Si2O7:0.04Tb3+ phosphor exhibits good thermal quenching capabilities. Improved photometric attributes including CIE coordinates, CCT and color purity confirmed the green glow, indicating a strong competitor for cool-green emission in lighting applications.

Two-Fold and Symmetric Repeatability Rates for Comparing Keypoint燚etectors

The repeatability rate is an important measure for evaluating and comparing the performance of keypoint detectors. Several repeatability rate measurements were used in the literature to assess the effectiveness of keypoint detectors. While these repeatability rates are calculated for pairs of images, the general assumption is that the reference image is often known and unchanging compared to other images in the same dataset. So, these rates are asymmetrical as they require calculations in only one direction. In addition, the image domain in which these computations take place substantially affects their values. The presented scatter diagram plots illustrate how these directional repeatability rates vary in relation to the size of the neighboring region in each pair of images. Therefore, both directional repeatability rates for the same image pair must be included when comparing different keypoint detectors. This paper, firstly, examines several commonly utilized repeatability rate measures for keypoint detector evaluations. The researcher then suggests computing a two-fold repeatability rate to assess keypoint detector performance on similar scene images. Next, the symmetric mean repeatability rate metric is computed using the given two-fold repeatability rates. Finally, these measurements are validated using well-known keypoint detectors on different image groups with various geometric and photometric attributes.

Canonical Grading Scales of Corneal and Conjunctival Staining Based on Psychophysical and Physical Attributes.

PurposeIn this study, we apply psychophysical scaling principles based on physical (photometric) attributes of images to better understand the factors involved in clinician judgement of ocular surface staining and, using that knowledge, to develop photographic scales for the assessment of staining for dry eye (DE) and related conditions.MethodsSubjects with noninfectious ocular surface staining were enrolled at five clinical sites. Following instillation of fluorescein, photographs of corneal staining were taken every 30 seconds for at least 5 minutes. The same procedure was followed for conjunctival staining after instillation of 2 µl of 1% lissamine green. A subset of the best corneal and bulbar conjunctival staining images were anonymized and a spectroradiometer measured photometric attributes (luminance and chromaticity). The images were scaled psychophysically by study investigators, who participated in constructing grading scales based on physical and psychophysical analyses. The final grading scales were refined following consultation with outside DE experts.ResultsPhotographs were collected from 142 subjects (81% women), with an average age of 58 ± 17 years; 89% were diagnosed with DE. There was a monotonic relationship between between physical measurements and psychophysically scaled staining of both corneal (fluorescein) and bulbar (lissamine green) staining. Michelson contrast and u’ (chromaticity) accounted for 66% and 64% of the variability in the psychophysically scaled images of fluorescein corneal and lissamine green conjunctival staining, respectively.Translational RelevanceThis paper provides examples of the first ever clinically usable ocular surface staining scales validated using psychophysical scaling and the physical attributes (luminance and chromaticity) of the staining itself. In addition, it provides a generalizable method for the development of other clinical scales of ocular appearance.

Characterization of mammographic masses based on local photometric attributes

This paper proposes Local Photometric Attributes (LPA) for the characterization of mammographic masses as benign or malignant. LPA measures the local information over the optical density image which suppresses the background region and provides more details about the mass lesion. The evaluation of the proposed approach is conducted by incorporating the mammograms of two benchmark databases—mini-MIAS and DDSM where a ten-fold cross validation technique is employed with different classifiers—Fishers Linear Discriminant Analysis, Random forest, and Support vector machine after filtering the optimal set of features by utilizing stepwise logistic regression method. The best performance achieved by the introduced approach in terms of an area under the receiver operating characteristic (ROC) curve (Az value) and accuracy (Acc) are 0.94 and 86.90%, respectively for the mini-MIAS dataset while the same for the DDSM dataset are 0.89 and 80.76%, respectively. The competitive nature of the proposed scheme is evident by comparing the obtained results with schemes in the state-of-the-arts.

Photometric Redshift Analysis using Supervised Learning Algorithms and Deep Learning

We present a catalogue of galaxy photometric redshifts for the Sloan Digital Sky Survey (SDSS) Data Release 12. We use various supervised learning algorithms to calculate redshifts using photometric attributes on a spectroscopic training set. Two training sets are analysed in this paper. The first training set consists of 995,498 galaxies with redshifts up to z ≈ 0.8. On the first training set, we achieve a cost function of 0.00501 and a root mean squared error value of 0.0707 using the XGBoost algorithm. We achieved an outlier rate of 2.1% and 86.81%, 95.83%, 97.90% of our data points lie within one, two, and three standard deviation of the mean respectively. The second training set consists of 163,140 galaxies with redshifts up to z ≈ 0.2 and is merged with the Galaxy Zoo 2 full catalog. We also experimented on convolutional neural networks to predict five morphological features (Smooth, Features/Disk, Star, Edge-on, Spiral). We achieve a root mean squared error of 0.117 when validated against an unseen dataset with over 200 epochs. Morphological features from the Galaxy Zoo, trained with photometric features are found to consistently improve the accuracy of photometric redshifts.

What is a Hole? Discovering Access Holes in Disaster Rubble with Functional and Photometric Attributes

The collapse of buildings and other structures in heavily populated areas often results in human victims becoming trapped within the resulting rubble. This rubble is often unstable, difficult to traverse, and dangerous for emergency first responders tasked with finding, stabilizing, and extricating entombed or hidden victims through access holes in the rubble. Recent work in scene mapping and reconstruction using photometric color and metric depth (RGB‐D) data collected by unmanned aerial vehicles (UAVs) suggests the possibility of automatically identifying potential access holes into the interior of rubble. This capability would greatly improve search operations by directing the limited human search capacity to areas where access holes might exist. This paper presents a novel approach to automatically identifying access holes in rubble. The investigation begins by defining an access hole in terms that allow for their algorithmic identification as a potential means of accessing the interior of rubble. This definition captures the functional and photometric attributes of holes. From this definition, a set of hole‐related features for detection is presented. Experiments were conducted using RGB‐D data collected over a real‐world disaster training facility using a UAV. Empirical evaluation suggests the efficacy of the proposed approach for successfully identifying potential access holes in disaster rubble.

Characteristics of clockwise and counterclockwise spiral galaxies

AbstractWe compared measurements of 126 501 spiral galaxies to test whether the photometry of galaxies that rotate clockwise is different from the photometry of galaxies that rotate counterclockwise for the purpose of testing whether there is a link between photometry and spin direction of galaxies. The rotation directionality of the galaxies was determined by converting the galaxy image to its radial intensity plot, and then the galaxies in each 30° RA sector were separated into clockwise and counterclockwise rotating galaxies. The mean and standard deviation of SDSS DR7 photometric attributes of clockwise and counterclockwise rotating galaxies were then compared. The results show no significant difference between galaxies that rotate clockwise and galaxies that rotate counterclockwise. (© 2014 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

SOMz: photometric redshift PDFs with self-organizing maps and random atlas

In this paper, we explore the applicability of the unsupervised machine learning technique of self-organizing maps (SOM) to estimate galaxy photometric redshift probability density functions (PDFs). This technique takes a spectroscopic training set, and maps the photometric attributes, but not the redshifts, to a two-dimensional surface by using a process of competitive learning where neurons compete to more closely resemble the training data multidimensional space. The key feature of a SOM is that it retains the topology of the input set, revealing correlations between the attributes that are not easily identified. We test three different 2D topological mapping: rectangular, hexagonal and spherical, by using data from the Deep Extragalactic Evolutionary Probe 2 survey. We also explore different implementations and boundary conditions on the map and also introduce the idea of a random atlas, where a large number of different maps are created and their individual predictions are aggregated to produce a more robust photometric redshift PDF. We also introduced a new metric, the I-score, which efficiently incorporates different metrics, making it easier to compare different results (from different parameters or different photometric redshift codes). We find that by using a spherical topology mapping we obtain a better representation of the underlying multidimensional topology, which provides more accurate results that are comparable to other, state-of-the-art machine learning algorithms. Our results illustrate that unsupervised approaches have great potential for many astronomical problems, and in particular for the computation of photometric redshifts.

New insights into galaxy structure from galphat- I. Motivation, methodology and benchmarks for Sérsic models

We introduce a new galaxy image decomposition tool, GALPHAT (GALaxy PHotometric ATtributes), to provide full posterior probability distributions and reliable confidence intervals for all model parameters. GALPHAT is designed to yield a high speed and accurate likelihood computation, using grid interpolation and Fourier rotation. We benchmark this approach using an ensemble of simulated Sersic model galaxies over a wide range of observational conditions: the signal-to-noise ratio S/N, the ratio of galaxy size to the PSF and the image size, and errors in the assumed PSF; and a range of structural parameters: the half-light radius $r_e$ and the Sersic index $n$. We characterise the strength of parameter covariance in Sersic model, which increases with S/N and $n$, and the results strongly motivate the need for the full posterior probability distribution in galaxy morphology analyses and later inferences. The test results for simulated galaxies successfully demonstrate that, with a careful choice of Markov chain Monte Carlo algorithms and fast model image generation, GALPHAT is a powerful analysis tool for reliably inferring morphological parameters from a large ensemble of galaxies over a wide range of different observational conditions. (abridged)

MULTIRESOLUTION IMAGE PERCEPTUAL GROUPING USING TOPOLOGICAL STRUCTURE EMBEDDED IN MANIFOLD

Contour grouping is the precondition for high-level visual tasks such as region-of-interest detection and object recognition. Those methods adopting local geometric features and simple photometric attributes always lead to unreliable result or bad robustness. In this paper, we propose a grouping method using multiresolution analysis and one-dimensional manifold. By using multiresolution analysis, grouping seeds and initial clusters are detected as precondition for describing topological structure. Then one-dimensional manifold is applied to discovering intrinsic order of topological structure, which corresponds to coordinates of edges in a closed contour. These two improvements enhance our method's robustness relative to those using local features or linear combinations of them. Experiments on different classes of real images show competence of our method.

Adaptively merging large-scale range data with reflectance properties

In this paper, we tackle the problem of geometric and photometric modeling of large intricately shaped objects. Typical target objects we consider are cultural heritage objects. When constructing models of such objects, we are faced with several important issues that have not been addressed in the past-issues that mainly arise due to the large amount of data that has to be handled. We propose two novel approaches to efficiently handle such large amounts of data: A highly adaptive algorithm for merging range images and an adaptive nearest-neighbor search to be used with the algorithm. We construct an integrated mesh model of the target object in adaptive resolution, taking into account the geometric and/or photometric attributes associated with the range images. We use surface curvature for the geometric attributes and (laser) reflectance values for the photometric attributes. This adaptive merging framework leads to a significant reduction in the necessary amount of computational resources. Furthermore, the resulting adaptive mesh models can be of great use for applications such as texture mapping, as we will briefly demonstrate. Additionally, we propose an additional test for the k-d tree nearest-neighbor search algorithm. Our approach successfully omits back-tracking, which is controlled adaptively depending on the distance to the nearest neighbor. Since the main consumption of computational cost lies in the nearest-neighbor search, the proposed algorithm leads to a significant speed-up of the whole merging process. In this paper, we present the theories and algorithms of our approaches with pseudo code and apply them to several real objects, including large-scale cultural assets.

Supervised learning of large perceptual organization: graph spectral partitioning and learning automata

Perceptual organization offers an elegant framework to group low-level features that are likely to come from a single object. We offer a novel strategy to adapt this grouping process to objects in a domain. Given a set of training images of objects in context, the associated learning process decides on the relative importance of the basic salient relationships such as proximity, parallelness, continuity, junctions, and common region toward segregating the objects from the background. The parameters of the grouping process are cast as probabilistic specifications of Bayesian networks that need to be learned. This learning is accomplished using a team of stochastic automata in an N-player cooperative game framework. The grouping process, which is based on graph partitioning is able to form large groups from relationships defined over a small set of primitives and is fast. We statistically demonstrate the robust performance of the grouping and the learning frameworks on a variety of real images. Among the interesting conclusions is the significant role of photometric attributes in grouping and the ability to form large salient groups from a set of local relations, each defined over a small number of primitives.

Wrap&Zip decompression of the connectivity of triangle meshes compressed with Edgebreaker

The Edgebreaker compression (Rossignac, 1999; King and Rossignac, 1999) is guaranteed to encode any unlabeled triangulated planar graph of t triangles with at most 1.84t bits. It stores the graph as a CLERS string—a sequence of t symbols from the set { C,L,E,R,S} , each represented by a 1, 2 or 3 bit code. We show here that, in practice, the string can be further compressed to between 0.91t and 1.26t bits using an entropy code. These results improve over the 2.3t bits code proposed by Keeler and Westbrook (1995) and over the various 3D triangle mesh compression techniques published recently (Gumhold and Strasser, 1998; Itai and Rodeh, 1982; Naor, 1990; Touma and Gotsman, 1988; Turan, 1984), which exhibit either larger constants or cannot guarantee a linear worst case storage complexity. The decompression proposed by Rossignac (1999) is complicated and exhibits a non-linear time complexity. The main contribution reported here is a simpler and efficient decompression algorithm, called Wrap&Zip, which has a linear time and space complexity. Wrap&Zip reads the CLERS string and builds a triangle tree—a triangulated, simply connected, topological polygon without interior vertices. During that process, it uses a simple rule to orient the external edges of this polygon and to “zip” (i.e., identify) all pairs of adjacent external edges that are oriented away from their common vertex. Because triangulated planar graphs can only model the connectivity (triangle/vertex incidence) of 3D triangle meshes that are homeomorphic to a sphere, we introduce here simple extensions of the Edgebreaker and Wrap&Zip techniques for compressing more general, manifold, triangle meshes with holes and handles. Manifold representations of non-manifold meshes are discussed by Rossignac and Cardoze (1999) and Gueziec et al. (1998, 1999). The simple and efficient solution provided by the combination of Wrap&Zip with of the work reported in (Rossignac, 1999; King and Rossignac, 1999) yields a simple and effective solution for compressing the connectivity information of large and small triangle meshes that must be downloaded over the Internet. The CLERS stream may be interleaved with an encoding of the vertex coordinates and photometric attributes enabling inline decompression. The availability of local incidence information permits to use, during decompression, the location and attributes of neighboring vertices to predict new ones, and thus supports most of the recently proposed vertex compression techniques (Deering, 1995; Gumhold and Strasser, 1999; Taubin and Rossignac, 1998; Touma and Gotsman, 1998).