Probabilistic Combination of Noisy Points and Planes for RGB-D Odometry

This work proposes a visual odometry method that combines points and plane primitives, extracted from a noisy depth camera. Depth measurement uncertainty is modelled and propagated through the extraction of geometric primitives to the frame-to-frame motion estimation, where pose is optimized by weighting the residuals of 3D point and planes matches, according to their uncertainties. Results on an RGB-D dataset show that the combination of points and planes, through the proposed method, is able to perform well in poorly textured environments, where point-based odometry is bound to fail.Comment: Accepted to TAROS 201

The Question Asking Skills of Preschool Teacher Candidates: Turkey and America Example

Question asking is an important skill that teachers should use during class activities. Teachers need to get used to this ability while they are teacher candidates. The aim of this research is to identify the cognitive taxonomy and the structure of the questions asked by the candidate of preschool teachers and to compare the questioning skills of preschool teacher candidates in Michigan, USA and Turkey. The participants were selected from people who are both senior students and have preschool teacher experience. The participant teacher candidates of the present study have stated that they have not taken a questioning skill course, but the topic was taught in some of the courses. In this research, we used document review, a kind of qualitative research technique. Document evaluation was based on question formation forms. The candidate of preschool teachers were asked to write questions on “Question Formation Form”. The questions asked by participant teacher candidates were analyzed and the percent frequency tables of the questions are given. As a result, we observed that teacher candidates have mainly asked questions in the first three levels of the taxonomy. Our results also showed that the teacher candidates from Turkey have rarely asked application questions and candidates from the US have usually asked comprehension questions. When examining structure of the questions, the teacher candidates in Turkey asked more than twice as many close-ended questions than the teacher candidates in the US

RPNet: an End-to-End Network for Relative Camera Pose Estimation

This paper addresses the task of relative camera pose estimation from raw image pixels, by means of deep neural networks. The proposed RPNet network takes pairs of images as input and directly infers the relative poses, without the need of camera intrinsic/extrinsic. While state-of-the-art systems based on SIFT + RANSAC, are able to recover the translation vector only up to scale, RPNet is trained to produce the full translation vector, in an end-to-end way. Experimental results on the Cambridge Landmark dataset show very promising results regarding the recovery of the full translation vector. They also show that RPNet produces more accurate and more stable results than traditional approaches, especially for hard images (repetitive textures, textureless images, etc). To the best of our knowledge, RPNet is the first attempt to recover full translation vectors in relative pose estimation

Effectiveness of forestry related Best Management Practices in the Trout Creek Watershed, Colorado

This report was accepted as Thesis in partial fulfillment of the requirements for the Masters of Science for Nani Bay Teves in Spring 2005.June 2005.Includes bibliographical references (pages 79-83).In multiuse forests the majority of nonpoint source pollution is typically sediment. Best management practices (BMPs) are implemented to reduce or prevent this pollutant, however little research has been done to quantify the effectiveness of individual types of BMPs. The overall goal of this project was to evaluate the effectiveness of three BMPs implemented to reduce sediment in Trout Creek: cattle fences, off-road vehicle signs, and road culverts. The effectiveness of the combined BMPs in the land use area was evaluated by comparing water quality and Wolman pebble counts with an upstream reference area. The reference area was selected based on soil type, vegetation type, elevation, and absence of cattle grazing and off-road vehicle use. Despite the difficulty of finding an exact reference area, the study results suggest that fences and culverts are effective, but signs are ineffective.United States Department of the Interior, Geological Survey, Contract number 01HQGR0077

Beyond single-photon localization at the edge of a Photonic Band Gap

We study spontaneous emission in an atomic ladder system, with both transitions coupled near-resonantly to the edge of a photonic band gap continuum. The problem is solved through a recently developed technique and leads to the formation of a ``two-photon+atom'' bound state with fractional population trapping in both upper states. In the long-time limit, the atom can be found excited in a superposition of the upper states and a ``direct'' two-photon process coexists with the stepwise one. The sensitivity of the effect to the particular form of the density of states is also explored.Comment: to appear in Physical Review

ClassCut for Unsupervised Class Segmentation

Abstract. We propose a novel method for unsupervised class segmentation on a set of images. It alternates between segmenting object instances and learning a class model. The method is based on a segmentation energy defined over all images at the same time, which can be optimized efficiently by techniques used before in interactive segmentation. Over iterations, our method progressively learns a class model by integrating observations over all images. In addition to appearance, this model captures the location and shape of the class with respect to an automatically determined coordinate frame common across images. This frame allows us to build stronger shape and location models, similar to those used in object class detection. Our method is inspired by interactive segmentation methods [1], but it is fully automatic and learns models characteristic for the object class rather than specific to one particular object/image. We experimentally demonstrate on the Caltech4, Caltech101, and Weizmann horses datasets that our method (a) transfers class knowledge across images and this improves results compared to segmenting every image independently; (b) outperforms Grabcut [1] for the task of unsupervised segmentation; (c) offers competitive performance compared to the state-of-the-art in unsupervised segmentation and in particular it outperforms the topic model [2].

PENGARUH JUMLAH KREDIT DAN LIKUIDITAS TERHADAP TINGKAT PROFITABILITAS: Studi Kasus Pada KSP Kembang Ende

This study aimed to determine the effect of the amount of credit and liquidity on the level of profitability of the Kembang Ende Savings and Loans Cooperative. The population of this study is the overall financial statements, balance sheets, and statistical data in the Kembang Ende Savings and Loans Cooperative. Based on the purposive sampling method, this study used samples of financial statements, balance sheets, and statistical data in the Kembang Ende Savings and Loans Cooperative from 2016 to 2020. The results of this study (1) the number of credits affects profitability, this is evidenced by the value of t count variable number of credits > t table (5.737> 4.303) with a significance value of t count of 0.029 < 0.05. (2) liquidity does not affect profitability, this is evidenced by the t value of the liquidity variable < t table (3.389<4.303) with a significance value of 0.077>0.05. The influence of the number of credit variables on profitability is 93.6%, while the remaining 16.4% is influenced by other variables that are not included in this research model

MinMax Radon Barcodes for Medical Image Retrieval

Content-based medical image retrieval can support diagnostic decisions by clinical experts. Examining similar images may provide clues to the expert to remove uncertainties in his/her final diagnosis. Beyond conventional feature descriptors, binary features in different ways have been recently proposed to encode the image content. A recent proposal is "Radon barcodes" that employ binarized Radon projections to tag/annotate medical images with content-based binary vectors, called barcodes. In this paper, MinMax Radon barcodes are introduced which are superior to "local thresholding" scheme suggested in the literature. Using IRMA dataset with 14,410 x-ray images from 193 different classes, the advantage of using MinMax Radon barcodes over \emph{thresholded} Radon barcodes are demonstrated. The retrieval error for direct search drops by more than 15\%. As well, SURF, as a well-established non-binary approach, and BRISK, as a recent binary method are examined to compare their results with MinMax Radon barcodes when retrieving images from IRMA dataset. The results demonstrate that MinMax Radon barcodes are faster and more accurate when applied on IRMA images.Comment: To appear in proceedings of the 12th International Symposium on Visual Computing, December 12-14, 2016, Las Vegas, Nevada, US

Target Mass Monitoring and Instrumentation in the Daya Bay Antineutrino Detectors

The Daya Bay experiment measures sin^2 2{\theta}_13 using functionally identical antineutrino detectors located at distances of 300 to 2000 meters from the Daya Bay nuclear power complex. Each detector consists of three nested fluid volumes surrounded by photomultiplier tubes. These volumes are coupled to overflow tanks on top of the detector to allow for thermal expansion of the liquid. Antineutrinos are detected through the inverse beta decay reaction on the proton-rich scintillator target. A precise and continuous measurement of the detector's central target mass is achieved by monitoring the the fluid level in the overflow tanks with cameras and ultrasonic and capacitive sensors. In addition, the monitoring system records detector temperature and levelness at multiple positions. This monitoring information allows the precise determination of the detectors' effective number of target protons during data taking. We present the design, calibration, installation and in-situ tests of the Daya Bay real-time antineutrino detector monitoring sensors and readout electronics.Comment: 22 pages, 20 figures; accepted by JINST. Changes in v2: minor revisions to incorporate editorial feedback from JINS