ASSESSMENT OF FEATURE DETECTORS AND DESCRIPTORS IN REMOTE IMAGES OF PLANETARY BODIES

<p><strong>Abstract.</strong> Algorithms to detect, describe, and match common features in image sets are expanding to new worlds with the integration of the OpenCV feature matching framework in a popular mapping tool for planetary images (ISIS3). These algorithms provide a new approach to register images and build image-based control networks. However, the natural landscape of the Moon and other planetary bodies pose new challenges such as numerous features that appear similar (i.e., impact craters and boulders). In addition, planetary image sets vary in scale, orientation, and noise properties, especially when conducting cross instrument and cross mission comparisons. This study assesses a collection of common feature detector and descriptor algorithms to examine how they adapt to these challenges. With our analysis, we did not identify an ideal detector and descriptor combination that exists for our diverse lunar dataset. However, we did identify where particular algorithms succeed and identify their shortcomings. By knowing these capabilities, users can identify the proper set of algorithms to apply to an image set given the presence of noise and variations to scale and orientation.</p>

High-capacity preconscious processing in concurrent groupings of colored dots

Grouping is a perceptual process in which a subset of stimulus components (a group) is selected for a subsequent—typically implicit—perceptual computation. Grouping is a critical precursor to segmenting objects from the background and ultimately to object recognition. Here, we study grouping by color. We present subjects with 300-ms exposures of 12 dots colored with the same but unknown identical color interspersed among 14 dots of seven different colors. To indicate grouping, subjects point-click the remembered centroid (“center of gravity”) of the set of homogeneous dots, of heterogeneous dots, or of all dots. Subjects accurately judge all of these centroids. Furthermore, after a single stimulus exposure, subjects can judge both the heterogeneous and homogeneous centroids, that is, subjects simultaneously group by similarity and by dissimilarity. The centroid paradigm reveals the relative weight of each dot among targets and distractors to the underlying grouping process, offering a more detailed, quantitative description of grouping than was previously possible. A change detection experiment reveals that conscious memory contains less than two dots and their locations, whereas an ideal detector would have to perfectly process at least 15 of 26 dots to match the subjects’ centroid judgments—indicating an extraordinary capacity for preconscious grouping. A different color set yielded identical results. Grouping theories that rely on predefined feature maps would fail to explain these results. Rather, the results indicate that preconscious grouping is automatic, flexible, and rapid, and a far more complex process than previously believed.

I2-induced SC–SC transformation within two-dimensional Zn(ii)-triazole framework: an ideal detector of cyano-containing molecules

A SC–SC transformation process driven by I2 has been shown to generate a 2D + 1D → 2D interpenetrated architecture from a 2D + 2D → 2D network. For the first time we demonstrate a selective sensor toward cyano-containing molecules.

An ideal detector composed of a 3D Gd-based coordination polymer for DNA and Hg2+ ion

A 3D Gd-based CP was employed as an effective fluorescent sensing platform for DNA and Hg2+ ion detection with sensitivity and selectivity, due to its ability to highly quench fluorescence and its different affinities toward ssDNA and dsDNA.

Detection of the ultimate content of uranium of depleted ammunition in different materials

The goal of this work was to determine the ultimate detectable content of uranium arising from depleted uranium ammunition in different natural materials, namely, soil, gravel, wood by ?-spectrometry. The United Nations Environment Programme (UNEP) report has confirmed following findings: (a) it is not possible to detect uranium-containing ammunition by any available instrument in the soil deeper than 40 cm; (b) the scintillation detector MC PHAR, due to its high sensitivity (above 80 keV), is considered to be the ideal detector for low energy ?-irradiation, which is characteristic for depleted uranium.