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Статті в журналах з теми "Point scale"

Автор: Grafiati

Опубліковано: 10 березня 2023

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1

Lin, Baowei, Fasheng Wang, Yi Sun, Wen Qu, Zheng Chen, and Shuo Zhang. "Boundary points based scale invariant 3D point feature." Journal of Visual Communication and Image Representation 48 (October 2017): 136–48. http://dx.doi.org/10.1016/j.jvcir.2017.05.007.

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2

Landrum, R. Eric. "Scaling Issues in Faculty Evaluations." Psychological Reports 84, no. 1 (February 1999): 178–80. http://dx.doi.org/10.2466/pr0.1999.84.1.178.

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148 students were asked to evaluate particular survey questions with scales that varied in number of scale points, type of labeling, and labeling of anchors only vs all scale points. No significant difference was found between variations on 4-and 5-point scales, so these data were collapsed. Analysis indicated that students generated scale-point exemplars significantly better for 4-point scales (96.7%) than for 5-point (87.8%) and 10-point (79.3%) scales. These results are discussed with respect to the administration of student evaluations and the care required in selecting a scale type and interpreting the subsequent outcomes.

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3

Lin, Baowei, Toru Tamaki, Fangda Zhao, Bisser Raytchev, Kazufumi Kaneda, and Koji Ichii. "Scale alignment of 3D point clouds with different scales." Machine Vision and Applications 25, no. 8 (September 12, 2014): 1989–2002. http://dx.doi.org/10.1007/s00138-014-0633-2.

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4

Dawes, John. "Do Data Characteristics Change According to the Number of Scale Points Used? An Experiment Using 5-Point, 7-Point and 10-Point Scales." International Journal of Market Research 50, no. 1 (January 2008): 61–104. http://dx.doi.org/10.1177/147078530805000106.

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5

Gunderman, Richard B., and Stephen Chan. "The 13-Point Likert Scale." Academic Radiology 20, no. 11 (November 2013): 1466–67. http://dx.doi.org/10.1016/j.acra.2013.04.010.

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6

Dalal, Dev K., Nathan T. Carter, and Christopher J. Lake. "Middle Response Scale Options are Inappropriate for Ideal Point Scales." Journal of Business and Psychology 29, no. 3 (September 25, 2013): 463–78. http://dx.doi.org/10.1007/s10869-013-9326-5.

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7

Et.al, Santanu Choudhury. "Reliability and Validity of Compulsive Buying Scale Without Middle Point." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 10, 2021): 3604–10. http://dx.doi.org/10.17762/turcomat.v12i3.1640.

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Анотація:

Objective: The study aims at finding out the affect of reliability and validity for the compulsive buying behavior scale by Valence, d’ Astou’s and Fortier Scale without middle point. Methodology: Responses across 5 to 9 point scales are obtained to calculate the reliability and validity of compulsive buying behavior scale by Valence, d’ Astou’s and Fortier Scale. Cronbach’s alpha is used to measure the internal reliability of the scale. To compare the reliability coefficients among different scale points Feldt test and Hakstian-Whalen test are used. Convergent validity for measuring inter-correlations between scales with different numbers of response categories is used and Fisher’s –r to –z transformation is used to test population correlation coefficient. Conclusion: From study it is concluded that there is no change in reliability and validity when the middle point is dropped from the compulsive buying scale.

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8

Lin, Baowei, Fasheng Wang, Fangda Zhao, and Yi Sun. "Scale invariant point feature (SIPF) for 3D point clouds and 3D multi-scale object detection." Neural Computing and Applications 29, no. 5 (May 18, 2017): 1209–24. http://dx.doi.org/10.1007/s00521-017-2964-1.

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9

Kartika, Nadya Larasati, and Nurul Alfiyati. "ANALYSIS OF AIRY POINT APPLICATION ON LINE SCALE CALIBRATION IN RCM LIPI." Jurnal Standardisasi 20, no. 3 (January 16, 2019): 189. http://dx.doi.org/10.31153/js.v20i3.717.

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<p>Calibration of line scale in the Research Center for Metrology LIPI Indonesia (RCM-LIPI) is traceable to one dimensional measuring machine (SIP machine). Capability of the SIP machine table used as the base of line scale, however, covers only up to 400 mm and can be extended to 1000 mm using shifting methods. Supporting point as a method to maintain line scale still straight during measurement should be applied on machine table. For line scale ranged 400 mm, supporting point can be attached because the whole artefact is on the table, while some of the industrial instruments have scales above 400 mm, this mean slightly difficult to attach supporting point. This paper described an appropriate supporting points setting and their influence in line scale calibration ranged 500 mm by designed two systems supporting point attachment, they are L = 500 mm and L = 350 mm. As for the supporting points, airy points were used, because they were the most suitable for line scales with the pattern at the top plane. Each design is analysed using error graph and E<sub>n </sub>score to determine the most suitable design for 500 mm line scale calibration<em>.</em></p>

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10

Wang, Guang Xue, Yong Chun Liu, and Huan He. "A New Multi-Scale Harris Interesting Point Detector." Applied Mechanics and Materials 602-605 (August 2014): 1950–55. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1950.

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The interesting point is important features in images, which is frequently used for image registration, scene analysis, object tracking. Many algorithms for detecting the interesting points have been developed up to now. Among them, the Harris interesting point detector is proved most stable against rotation and noise, while it is also very sensitive to scale change. In order to solve this problem, a new multi-scale Harris interesting point detection algorithm is proposed in this paper. The algorithm consists of two main stages. In stage one, we build a multi-scale representation for Harris measure and find candidate interesting points at each scale level. In stage two, a novel measure is defined to measure the stability of each point. Based on the measure, the final interesting point is selected from candidates. The experiments on both optical and sar data sets have shown that, compared with stand Harris interesting point detector and some other multi-scale interesting point detectors, our algorithm is more robust.

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11

Lawless, Harry T., Richard Popper, and Beverley J. Kroll. "A comparison of the labeled magnitude (LAM) scale, an 11-point category scale and the traditional 9-point hedonic scale." Food Quality and Preference 21, no. 1 (January 2010): 4–12. http://dx.doi.org/10.1016/j.foodqual.2009.06.009.

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12

Fidler, V., and N. J. D. Nagelkerke. "AGREEMENT ON A TWO–POINT SCALE." Statistica Neerlandica 40, no. 1 (March 1986): 13–20. http://dx.doi.org/10.1111/j.1467-9574.1986.tb01160.x.

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13

Kadamen, Jayren, and George Sithole. "AUTOMATICALLY DETERMINING SCALE WITHIN UNSTRUCTURED POINT CLOUDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 617–24. http://dx.doi.org/10.5194/isprs-archives-xli-b3-617-2016.

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Анотація:

Three dimensional models obtained from imagery have an arbitrary scale and therefore have to be scaled. Automatically scaling these models requires the detection of objects in these models which can be computationally intensive. Real-time object detection may pose problems for applications such as indoor navigation. This investigation poses the idea that relational cues, specifically height ratios, within indoor environments may offer an easier means to obtain scales for models created using imagery. The investigation aimed to show two things, (a) that the size of objects, especially the height off ground is consistent within an environment, and (b) that based on this consistency, objects can be identified and their general size used to scale a model. To test the idea a hypothesis is first tested on a terrestrial lidar scan of an indoor environment. Later as a proof of concept the same test is applied to a model created using imagery. The most notable finding was that the detection of objects can be more readily done by studying the ratio between the dimensions of objects that have their dimensions defined by human physiology. For example the dimensions of desks and chairs are related to the height of an average person. In the test, the difference between generalised and actual dimensions of objects were assessed. A maximum difference of 3.96% (2.93<i>cm</i>) was observed from automated scaling. By analysing the ratio between the heights (distance from the floor) of the tops of objects in a room, identification was also achieved.

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14

Kadamen, Jayren, and George Sithole. "AUTOMATICALLY DETERMINING SCALE WITHIN UNSTRUCTURED POINT CLOUDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 617–24. http://dx.doi.org/10.5194/isprsarchives-xli-b3-617-2016.

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Анотація:

Three dimensional models obtained from imagery have an arbitrary scale and therefore have to be scaled. Automatically scaling these models requires the detection of objects in these models which can be computationally intensive. Real-time object detection may pose problems for applications such as indoor navigation. This investigation poses the idea that relational cues, specifically height ratios, within indoor environments may offer an easier means to obtain scales for models created using imagery. The investigation aimed to show two things, (a) that the size of objects, especially the height off ground is consistent within an environment, and (b) that based on this consistency, objects can be identified and their general size used to scale a model. To test the idea a hypothesis is first tested on a terrestrial lidar scan of an indoor environment. Later as a proof of concept the same test is applied to a model created using imagery. The most notable finding was that the detection of objects can be more readily done by studying the ratio between the dimensions of objects that have their dimensions defined by human physiology. For example the dimensions of desks and chairs are related to the height of an average person. In the test, the difference between generalised and actual dimensions of objects were assessed. A maximum difference of 3.96% (2.93<i>cm</i>) was observed from automated scaling. By analysing the ratio between the heights (distance from the floor) of the tops of objects in a room, identification was also achieved.

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15

Lindeberg, Tony. "Scale Selection Properties of Generalized Scale-Space Interest Point Detectors." Journal of Mathematical Imaging and Vision 46, no. 2 (September 20, 2012): 177–210. http://dx.doi.org/10.1007/s10851-012-0378-3.

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16

Choi, Seong Hee, Miok Yu, and Chul-Hee Choi. "Comparisons of 4-Point GRBAS, 7-Point-GRBAS, and CAPE-V for Auditory Perceptual Evaluation of Dysphonia." Audiology and Speech Research 17, no. 2 (April 30, 2021): 206–19. http://dx.doi.org/10.21848/asr.200086.

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Purpose: The GRBAS (grade, roughness, breathiness, asthenia, strain) scale, a 4-point scale, has been most widely used for judging auditory-perceptual severity for dysphonia. However, in current clinical practice, sometimes a more fragmented 0.5 scheme is being used if the original GRBAS scale is ambiguous to assess. Thus, the aim of the present study was to compare the dysphonia severity using the three auditory-perceptual evaluation tools, 4-point, 7-point GRBAS scale, and CAPEV and provide the information regarding differentiation of perceptual severity and acoustic correlations.Methods: Voice samples for sustained vowel and connected speech were obtained from 101 dysphonic patients. Auditory-perceptual assessments of dysphonia severity were performed by two certified experienced speech-language pathologists specializing in voice disorders using the grade of a 4-point and a 7-point GRBAS, OS of CAPE-V and also were compared with cepstral measures [cepstrum peak prominence (CPP), low/high spectral ratio].Results: OS and G of inter-rater reliability of dysphonia using CAPE-V and 4-point GRBAS scales were good (ICC > 0.800) while G of 7-point GRBAS was less strong (ICC > 0.681) than OS of CAPE-V and G of 4-point GRBAS scale. The highest correlation with CPP was the OS of CAPE-V in both vowel and connected speech, while L/H ratio showed low correlation with dysphonia severity. The OS of CAPE-V in vowel and connected speech differed significantly in G of both GRBAS scales. On the 4-point GRBAS scale, CPP showed only the difference between G1-G2, G2-G3 groups in /a/ vowel, whereas in the connected speech, the difference between G0-G1, G1-G2, and G2-G3 groups was relatively differentiated. Meanwhile, on a 7-point GRBAS scale, CPP showed only differences between G1.5-G2 groups in sustained vowel, while CPP showed no differences between G0-G0.5, G0.5-G1, G1-G1.5, G2-G2.5, and G2.5-G3 groups respectively in connected speech.Conclusion: 7-point GRBAS scale showed a higher correlation with CPP measures than the original GRBAS, but reduced the discrimination of dysphonia severity compared to the 4-point GRBAS scale. Consequently, modified 7-point GRBAS scale could be useful as a clinically-perceptual evaluation tool, along with the original GRBAS scale and CAPE-V.

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17

Brown, Sandra. "10-point analog scale not equivalent to a 10-point questionnaire." Journal of Cataract & Refractive Surgery 35, no. 9 (September 2009): 1651. http://dx.doi.org/10.1016/j.jcrs.2009.05.017.

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18

Zhou, Junjie, Hongqiang Wei, Guiyun Zhou, and Lihui Song. "Separating Leaf and Wood Points in Terrestrial Laser Scanning Data Using Multiple Optimal Scales." Sensors 19, no. 8 (April 18, 2019): 1852. http://dx.doi.org/10.3390/s19081852.

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The separation of leaf and wood points is an essential preprocessing step for extracting many of the parameters of a tree from terrestrial laser scanning data. The multi-scale method and the optimal scale method are two of the most widely used separation methods. In this study, we extend the optimal scale method to the multi-optimal-scale method, adaptively selecting multiple optimal scales for each point in the tree point cloud to increase the distinctiveness of extracted geometric features. Compared with the optimal scale method, our method achieves higher separation accuracy. Compared with the multi-scale method, our method achieves more stable separation accuracy with a limited number of optimal scales. The running time of our method is greatly reduced when the optimization strategy is applied.

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19

EWING, D., W. K. GEORGE, M. M. ROGERS, and R. D. MOSER. "Two-point similarity in temporally evolving plane wakes." Journal of Fluid Mechanics 577 (April 19, 2007): 287–307. http://dx.doi.org/10.1017/s0022112006003260.

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The governing equations for the two-point correlations of the turbulent fluctuating velocity in the temporally evolving wake were analysed to determine whether they could have equilibrium similarity solutions. It was found that these equations could have such solutions for a finite-Reynolds-number wake, where the two-point velocity correlations could be written as a product of a time-dependent scale and a function dependent only on similarity variables. It is therefore possible to collapse the two-point measures of all the scales of motions in the temporally evolving wake using a single set of similarity variables. As in an earlier single-point analysis, it was found that the governing equations for the equilibrium similarity solutions could not be reduced to a form that was independent of a growth-rate dependent parameter. Thus, there is not a single ‘universal’ solution that describes the state of the large-scale structures, so that the large-scale structures in the far field may depend on how the flow is generated.The predictions of the similarity analysis were compared to the data from two direct numerical simulations of the temporally evolving wakes examined previously. It was found that the two-point velocity spectra of these temporally evolving wakes collapsed reasonably well over the entire range of scales when they were scaled in the manner deduced from the equilibrium similarity analysis. Thus, actual flows do seem to evolve in a manner consistent with the equilibrium similarity solutions.

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20

CELANI, ANTONIO, MARCO MARTINS AFONSO, and ANDREA MAZZINO. "Point-source scalar turbulence." Journal of Fluid Mechanics 583 (July 4, 2007): 189–98. http://dx.doi.org/10.1017/s0022112007006520.

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The statistics of a passive scalar randomly emitted from a point source is investigated analytically for the Kraichnan ensemble. Attention is focused on the two-point equal-time scalar correlation function, a statistical indicator widely used both in experiments and in numerical simulations. The only source of inhomogeneity/anisotropy is the injection mechanism, the advecting velocity being here statistically homogeneous and isotropic. The main question we address is on the possible existence of an inertial range of scales and a consequent scaling behaviour. The question arises from the observation that for a point source the injection scale is formally zero and the standard cascade mechanism cannot thus be taken for granted. We find from first principles that an intrinsic integral scale, whose value depends on the distance from the source, emerges as a result of sweeping effects. For separations smaller than this integral scale a standard forward cascade occurs. This is characterized by a Kolmogorov–Obukhov power-law behaviour as in the homogeneous case, except that the dissipation rate is also dependent on the distance from the source. Finally, we also find that the combined effect of a finite inertial-range extent and of inhomogeneities causes the emergence of subleading anisotropic corrections to the leading isotropic term, that are here quantified and discussed.

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21

Zang, Y., and B. Yang. "OPTIMAL INFORMATION EXTRACTION OF LASER SCANNING DATASET BY SCALE-ADAPTIVE REDUCTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 2209–13. http://dx.doi.org/10.5194/isprs-archives-xlii-3-2209-2018.

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3D laser technology is widely used to collocate the surface information of object. For various applications, we need to extract a good perceptual quality point cloud from the scanned points. To solve the problem, most of existing methods extract important points based on a fixed scale. However, geometric features of 3D object come from various geometric scales. We propose a multi-scale construction method based on radial basis function. For each scale, important points are extracted from the point cloud based on their importance. We apply a perception metric Just-Noticeable-Difference to measure degradation of each geometric scale. Finally, scale-adaptive optimal information extraction is realized. Experiments are undertaken to evaluate the effective of the proposed method, suggesting a reliable solution for optimal information extraction of object.

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22

Zhao, Genping, Weiguang Zhang, Yeping Peng, Heng Wu, Zhuowei Wang, and Lianglun Cheng. "PEMCNet: An Efficient Multi-Scale Point Feature Fusion Network for 3D LiDAR Point Cloud Classification." Remote Sensing 13, no. 21 (October 27, 2021): 4312. http://dx.doi.org/10.3390/rs13214312.

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Point cloud classification plays a significant role in Light Detection and Ranging (LiDAR) applications. However, most available multi-scale feature learning networks for large-scale 3D LiDAR point cloud classification tasks are time-consuming. In this paper, an efficient deep neural architecture denoted as Point Expanded Multi-scale Convolutional Network (PEMCNet) is developed to accurately classify the 3D LiDAR point cloud. Different from traditional networks for point cloud processing, PEMCNet includes successive Point Expanded Grouping (PEG) units and Absolute and Relative Spatial Embedding (ARSE) units for representative point feature learning. The PEG unit enables us to progressively increase the receptive field for each observed point and aggregate the feature of a point cloud at different scales but without increasing computation. The ARSE unit following the PEG unit furthermore realizes representative encoding of points relationship, which effectively preserves the geometric details between points. We evaluate our method on both public datasets (the Urban Semantic 3D (US3D) dataset and Semantic3D benchmark dataset) and our new collected Unmanned Aerial Vehicle (UAV) based LiDAR point cloud data of the campus of Guangdong University of Technology. In comparison with four available state-of-the-art methods, our methods ranked first place regarding both efficiency and accuracy. It was observed on the public datasets that with a 2% increase in classification accuracy, over 26% improvement of efficiency was achieved at the same time compared to the second efficient method. Its potential value is also tested on the newly collected point cloud data with over 91% of classification accuracy and 154 ms of processing time.

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23

Kwong, WJ, and DS Pathak. "Validation of the Eleven-Point Pain Scale in the Measurement of Migraine Headache Pain." Cephalalgia 27, no. 4 (April 2007): 336–42. http://dx.doi.org/10.1111/j.1468-2982.2007.01283.x.

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The four-point pain scale (none, mild, moderate, severe) and the 11-point pain scale (0 = no pain, 10 = pain as bad as it could be) have been used in migraine studies to assess treatment efficacy. The primary objective of this study was to investigate the validity and responsiveness of the 11-point pain scale using the four-point pain scale as a benchmark. Using data from 95 migraine patients recruited from headache clinics, this study found that 11-point pain scale scores were highly correlated with four-point pain scores. The correlations between the pain scales were significantly higher than the correlations with quality of life measures such as functional ability and emotional feelings. The 11-point pain scale was 55% more sensitive than the four-point pain scale in detecting clinically important differences. The strong linear relationship between the two pain scales allowed researchers to transform four-point pain scores to 11-point pain scores using regression weights.

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24

Guislain, Maximilien, Julie Digne, Raphaëlle Chaine, and Gilles Monnier. "Fine scale image registration in large-scale urban LIDAR point sets." Computer Vision and Image Understanding 157 (April 2017): 90–102. http://dx.doi.org/10.1016/j.cviu.2016.12.004.

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25

PLATT, DANIEL E., and FEREYDOON FAMILY. "SCALING OF THE POINT-POINT CORRELATION FUNCTION OF DLA." Fractals 01, no. 02 (June 1993): 229–37. http://dx.doi.org/10.1142/s0218348x9300023x.

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The scaling of the point-point correlation function of DLA has shown itself to be more complex and challenging than had at first been imagined. This paper determines from the results of previous studies that a more general scaling rule must be applied, at least robust enough to allow different moments of the point-point correlation function to scale non-trivially, and for the scale length to scale non-trivially. In this sense, it is seen that the scaling problem is analogous to that of multifractal scaling. The authors present a group-theoretic derivation of the most general scaling transformations possible, and show that it is adequately robust to explain the non-trivial scaling observed in the moments of the point-point correlation function.

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26

Jiang, Yan, Shu Fan Hou, and Yi Nan Lu. "A Multilevel Feature Points Detecting Method upon Point-Octree for Point-Based Models." Advanced Materials Research 490-495 (March 2012): 115–19. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.115.

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Анотація:

In this paper, we present a multilevel feature points extracting and displaying method for point-based models to improve the fast detecting of the multilevel feature of large-scale models. Our algorithm is built upon a non uniform partitioning feature point-octree data structure. We adopt covariance analysis method to estimate the surface variation, and feature parameter in local surfaces to describe the feature. The bigger the feature parameter is, the greater likelihood the point becomes a feature point. During the process of building point-octree, feature parameter is related to each LOD node. When displaying, we combin this feature point-octree with an improved particle swarm optimizer (PSO) algorithm to implement multilevel feature fast displaying according to viewpoint and feature level.

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27

Wang, Bin, Hao Wang, and Dongmei Song. "A Filtering Method for LiDAR Point Cloud Based on Multi-Scale CNN with Attention Mechanism." Remote Sensing 14, no. 23 (December 6, 2022): 6170. http://dx.doi.org/10.3390/rs14236170.

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Анотація:

Point cloud filtering is an important prerequisite for three-dimensional surface modeling with high precision based on LiDAR data. To cope with the issues of low filtering accuracy or excessive model complexity in traditional filtering algorithms, this paper proposes a filtering method for LiDAR point cloud based on a multi-scale convolutional neural network incorporated with the attention mechanism. Firstly, a regular image patch centering on each point is constructed based on the elevation information of point clouds. As thus, the point cloud filtering problem is transformed into the image classification problem. Then, considering the ability of multi-scale convolution to extract features at different scales and the potential of the attention mechanism to capture key information in images, a multi-scale convolutional neural network framework is constructed, and the attention mechanism is incorporated to coordinate multi-scale convolution kernel with channel and spatial attention modules. After this, the feature maps of the LiDAR point clouds can be acquired at different scales. For these feature maps, the weights of each channel layer and different spatial regions can be further tuned adaptively, which makes the network training more targeted, thereby improving the model performance for image classification and eventually separating of ground points and non-ground points preferably. Finally, the proposed method is compared with the cloth simulation filtering method (CSF), deep neural network method (DNN), k-nearest neighbor method (KNN), deep convolutional neural network method (DCNN) and scale-irrelevant and terrain-adaptive method (SITA) for the standard ISPRS dataset of point cloud filtering and the filter dataset of Qinghai. The experimental results show that the proposed method can obtain lower classification errors, which proves the superiority of this method in point cloud filtering.

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28

Wang, Ziwei, Sijie Yan, Long Wu, Xiaojian Zhang, and BinJiang Chen. "Robust point clouds registration with point-to-point lp distance constraints in large-scale metrology." ISPRS Journal of Photogrammetry and Remote Sensing 189 (July 2022): 23–35. http://dx.doi.org/10.1016/j.isprsjprs.2022.04.024.

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29

Habibi, Reza. "Change point detection in scale family distributions." Statistica Neerlandica 63, no. 3 (August 2009): 347–52. http://dx.doi.org/10.1111/j.1467-9574.2009.00427.x.

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30

Kim, Chang Bae. "Large-Scale Fixed Point of Forced Plasmas." Journal of the Korean Physical Society 50, no. 4 (April 14, 2007): 1052. http://dx.doi.org/10.3938/jkps.50.1052.

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31

Richardson, D. B. "Refined Overcrowding Scale Based on Point Occupancy." Academic Emergency Medicine 12, Supplement 1 (May 1, 2005): 23. http://dx.doi.org/10.1197/j.aem.2005.03.055.

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32

Pei, Tao, Jianhuan Gao, Ting Ma, and Chenghu Zhou. "Multi-scale decomposition of point process data." GeoInformatica 16, no. 4 (August 3, 2012): 625–52. http://dx.doi.org/10.1007/s10707-012-0165-8.

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33

Phillips, Jonathan D. "Vanishing point: Scale independence in geomorphological hierarchies." Geomorphology 266 (August 2016): 66–74. http://dx.doi.org/10.1016/j.geomorph.2016.05.012.

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34

Mikolajczyk, Krystian, and Krystian Mikolajczyk. "Scale & Affine Invariant Interest Point Detectors." International Journal of Computer Vision 60, no. 1 (October 2004): 63–86. http://dx.doi.org/10.1023/b:visi.0000027790.02288.f2.

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Munley, Patrick H., Dharm S. Bains, and William D. Bloem. "F Scale Elevation and PTSD MMPI Profiles." Psychological Reports 73, no. 2 (October 1993): 363–70. http://dx.doi.org/10.2466/pr0.1993.73.2.363.

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MMPI profiles for 87 PTSD veteran inpatients were classified and studied according to MMPI F Scale elevation. Mean MMPI profiles and frequency of high two-point code types were studied for different levels of F Scale elevation. Similar mean profile configurations were found for subgroups with F ≥ 70 with Scales 2 and 8 appearing as the two highest clinical scales. For F < 70 the configuration was different in that Scale 8 was not one of the two highest scales. The 2-8/8-2 high two-point code was the modal high two-point code for the total sample but the relative frequency of high two-point codes did vary somewhat within and across levels of F Scale elevation.

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36

Park, Seong-Hun, Min-Ah Son, Dong-Hee Park, and Byeong-Keun Choi. "Study on Three-point Support Balancing using Real Scale Test Rotor." Transactions of the Korean Society for Noise and Vibration Engineering 29, no. 2 (April 20, 2019): 216–21. http://dx.doi.org/10.5050/ksnve.2019.29.2.216.

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37

Eutsler, Jared, and Bradley Lang. "Rating Scales in Accounting Research: The Impact of Scale Points and Labels." Behavioral Research in Accounting 27, no. 2 (July 1, 2015): 35–51. http://dx.doi.org/10.2308/bria-51219.

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ABSTRACT Rating scales are one of the most widely used tools in behavioral research. Decisions regarding scale design can have a potentially profound effect on research findings. Despite this importance, an analysis of extant literature in top accounting journals reveals a wide variety of rating scale compositions. The purpose of this paper is to experimentally investigate the impact of scale characteristics on participants' responses. Two experiments are conducted that manipulate the number of scale points and the corresponding labels to study their influence on the statistical properties of the resultant data. Results suggest that scale design impacts the statistical characteristics of response data and emphasize the importance of labeling all scale points. A scale with all points labeled effectively minimizes response bias, maximizes variance, maximizes power, and minimizes error. This analysis also suggests variance may be maximized when the scale length is set at 7 points. Although researchers commonly believe using additional scale points will maximize variance, results indicate increasing scale points beyond 7 does not increase variance. Taken together, a fully labeled 7-point scale may provide the greatest benefits to researchers. The importance of scale labels provides a significant contribution to accounting research as only 5 percent of the accounting studies reviewed have reported scales with all points labeled.

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Morse, Jennifer S., Steven C. Schallhorn, Keith Hettinger, and David Tanzer. "Reply: 10-point analog scale not equivalent to a 10-point questionnaire." Journal of Cataract & Refractive Surgery 35, no. 9 (September 2009): 1651–53. http://dx.doi.org/10.1016/j.jcrs.2009.05.016.

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Protonotarios, Emmanouil D., Buzz Baum, Alan Johnston, Ginger L. Hunter, and Lewis D. Griffin. "An absolute interval scale of order for point patterns." Journal of The Royal Society Interface 11, no. 99 (October 6, 2014): 20140342. http://dx.doi.org/10.1098/rsif.2014.0342.

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Human observers readily make judgements about the degree of order in planar arrangements of points (point patterns). Here, based on pairwise ranking of 20 point patterns by degree of order, we have been able to show that judgements of order are highly consistent across individuals and the dimension of order has an interval scale structure spanning roughly 10 just-notable-differences (jnd) between disorder and order. We describe a geometric algorithm that estimates order to an accuracy of half a jnd by quantifying the variability of the size and shape of spaces between points. The algorithm is 70% more accurate than the best available measures. By anchoring the output of the algorithm so that Poisson point processes score on average 0, perfect lattices score 10 and unit steps correspond closely to jnds, we construct an absolute interval scale of order. We demonstrate its utility in biology by using this scale to quantify order during the development of the pattern of bristles on the dorsal thorax of the fruit fly.

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Colvin, Kimberly F., Guher Gorgun, and Sijun Zhang. "Comparing Interpretations of the Rosenberg Self-Esteem Scale With 4-, 5-, and 101-Point Scales." Journal of Psychoeducational Assessment 38, no. 6 (April 6, 2020): 762–66. http://dx.doi.org/10.1177/0734282920915063.

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The Rosenberg Self-Esteem Scale was administered with a 1–4, 1–5, or 0–100 scale to 819 participants, to compare score interpretations across the different versions. A rating scale utility analysis revealed that the categories in the 101-point scale were used inconsistently; based on the analysis, adjacent categories were collapsed resulting in a 7-point scale with almost identical psychometric properties as the original. The interpretations based on the 101-point scale could lead to misinterpretations when compared with the 4- and 5-point versions.

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Hackel, T., N. Savinov, L. Ladicky, J. D. Wegner, K. Schindler, and M. Pollefeys. "SEMANTIC3D.NET: A NEW LARGE-SCALE POINT CLOUD CLASSIFICATION BENCHMARK." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-1/W1 (May 30, 2017): 91–98. http://dx.doi.org/10.5194/isprs-annals-iv-1-w1-91-2017.

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This paper presents a new 3D point cloud classification benchmark data set with over four billion manually labelled points, meant as input for data-hungry (deep) learning methods. We also discuss first submissions to the benchmark that use deep convolutional neural networks (CNNs) as a work horse, which already show remarkable performance improvements over state-of-the-art. CNNs have become the de-facto standard for many tasks in computer vision and machine learning like semantic segmentation or object detection in images, but have no yet led to a true breakthrough for 3D point cloud labelling tasks due to lack of training data. With the massive data set presented in this paper, we aim at closing this data gap to help unleash the full potential of deep learning methods for 3D labelling tasks. Our semantic3D.net data set consists of dense point clouds acquired with static terrestrial laser scanners. It contains 8 semantic classes and covers a wide range of urban outdoor scenes: churches, streets, railroad tracks, squares, villages, soccer fields and castles. We describe our labelling interface and show that our data set provides more dense and complete point clouds with much higher overall number of labelled points compared to those already available to the research community. We further provide baseline method descriptions and comparison between methods submitted to our online system. We hope semantic3D.net will pave the way for deep learning methods in 3D point cloud labelling to learn richer, more general 3D representations, and first submissions after only a few months indicate that this might indeed be the case.

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42

Liu, Minshi, Yi Long, Ling Zhang, and Guifang He. "A Method for extracting multi-scale stay feature of trajectory based on OPTICS." Abstracts of the ICA 1 (July 15, 2019): 1. http://dx.doi.org/10.5194/ica-abs-1-222-2019.

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<p><strong>Abstract.</strong> With the popularity of mobile positioning devices, mobile trajectory data is rapidly increasing. The trajectory stay feature extraction is to find the staying part of the moving object from the moving trajectory, which is the basis of the semantic matching of the staying segment. The trajectory stay segment extraction can be used to analyze tourist visit recommendation and mode, special landmark extraction such as gas station, destination prediction, rental car passenger advice, and even traffic guidance. Most existing researches extract the single-scale stay feature of the trajectory through the spatiotemporal characteristics of the trajectory or the geographical environment. However, the stay feature of the trajectory is multi-scale. For example, at a city scale, the stay of a tourist trajectory is composed of several scenic spots; however, at the scenic scale, a scenic stop may contain a number of subtle attraction. Therefore, this paper proposes to extract trajectory multi-scale stay feature based on OPTICS method. Since the trajectory data is different from the conventional discrete point set, but an ordered point string, the OPTICS method needs to be modified to accommodate the clustering of the trajectory point string. Method improvements include the following four aspects:</p><p>(1)The correction of the two-point distance definition of the trajectory. Since the trajectory points are ordered, thedistance between the two points of the trajectory is not the linear distance between the two points, but the sum of thelengths of the trajectory polylines between the two points.</p><p>(2)The count calculation of points in the ε neighborhood of the center points needs to be improved. When the timeinterval of the trajectory is equal, the calculation method is consistent with OPTICS; and when the time interval of thetrajectory sampling is not equal, the number of points represented by one trajectory points should be related to its timeinterval. When the time interval is large, the point should represent more points. Therefore, a time coefficient is defined,which is defined as the ratio of the track point time interval to the basic time interval. The total number of the points isthe sum of time coefficient m of each points. By improving as above, the algorithm can be adapted to trajectory datasampled at unequal time intervals. In addition, when searching for the field of points, it is not necessary to search theentire set of trajectory points, but only need to start with the center point, and search forward and backward until thedistance between the two points is greater than the threshold ε.</p><p>(3)Improvement of clustering order method: When generating the clustering order, the OPTICS needs to center thecurrent point in order, and correct the reachable distance value of each point in the ε neighborhood of the point, then thepoint at the minimum distance is selected as the subsequent point. For the trajectory data, the subsequent point on thetrajectory is obviously the point which the reachable distance is the shortest in the ε neighborhood of the current point,and so the clustering order is consistent with the trajectory points sequence. Therefore, the improved algorithm only needsto calculate the reachable distance of a subsequent point of the current point, thereby quickly generating a clusteringsequence.</p><p>(4)The scale of the staying feature is automatically controlled by reachable distance. Although the OPTICS is capableof generating multi-level clustering results, it does not measure the hierarchy. In this paper, the scale of each stay featureis measured by the reachable distance range of a cluster, and the relationship between the reachable distance and the stayfeature scale is established, so that the multi-scale stay feature can be obtained automatically.</p><p>This paper compares this method with other methods using a variety of trajectory data. The results show that the method can extract the track stay feature faster and more accurately, and can automatically control the stay feature extraction level.</p>

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43

Zeng, Ni, Jinlong Li, Yu Zhang, Xiaorong Gao, and Lin Luo. "Scattered Train Bolt Point Cloud Segmentation Based on Hierarchical Multi-Scale Feature Learning." Sensors 23, no. 4 (February 10, 2023): 2019. http://dx.doi.org/10.3390/s23042019.

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In view of the difficulty of using raw 3D point clouds for component detection in the railway field, this paper designs a point cloud segmentation model based on deep learning together with a point cloud preprocessing mechanism. First, a special preprocessing algorithm is designed to resolve the problems of noise points, acquisition errors, and large data volume in the actual point cloud model of the bolt. The algorithm uses the point cloud adaptive weighted guided filtering for noise smoothing according to the noise characteristics. Then retaining the key points of the point cloud, this algorithm uses the octree to partition the point cloud and carries out iterative farthest point sampling in each partition for obtaining the standard point cloud model. The standard point cloud model is then subjected to hierarchical multi-scale feature extraction to obtain global features, which are combined with local features through a self-attention mechanism, while linear interpolation is used to further expand the perceptual field of local features of the model as a basis for segmentation, and finally the segmentation is completed. Experiments show that the proposed algorithm could deal with the scattered bolt point cloud well, realize the segmentation of train bolt and background, and could achieve high segmentation accuracy, which has important practical significance for train safety detection.

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44

Morinaga, Makoto, Thu Lan Nguyen, Shigenori Yokoshima, Koji Shimoyama, Takashi Morihara, and Takashi Yano. "The Effect of an Alternative Definition of “Percent Highly Annoyed” on the Exposure–Response Relationship: Comparison of Noise Annoyance Responses Measured by ICBEN 5-Point Verbal and 11-Point Numerical Scales." International Journal of Environmental Research and Public Health 18, no. 12 (June 9, 2021): 6258. http://dx.doi.org/10.3390/ijerph18126258.

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Since the development of the 5-point verbal and 11-point numerical scales for measuring noise annoyance by the ICBEN Team 6, these scales have been widely used in socio-acoustic surveys worldwide, and annoyance responses have been easily compared internationally. However, both the top two categories of the 5-point verbal scale and the top three ones of the 11-point numerical scale are correspond to high annoyance, so it is difficult to precisely compare annoyance responses. Therefore, we calculated differences in day–evening–night-weighted sound pressure levels (Lden) by comparing values corresponding to 10% highly annoyed (HA) on Lden_%HA curves obtained from measurements in 40 datasets regarding surveys conducted in Japan and Vietnam. The results showed that the Lden value corresponding to 10% HA using the 5-point verbal scale was approximately 5 dB lower than that of the 11-point numerical scale. Thus, some correction is required to compare annoyance responses measured by the 5-point verbal and the 11-point numerical scales. The results of this study were also compared with those of a survey in Switzerland.

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45

Ao, W., L. Wang, and J. Shan. "POINT CLOUD CLASSIFICATION BY FUSING SUPERVOXEL SEGMENTATION WITH MULTI-SCALE FEATURES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 919–25. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-919-2019.

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<p><strong>Abstract.</strong> Point cloud classification is quite a challenging task due to the existence of noises, occlusion and various object types and sizes. Currently, the commonly used statistics-based features cannot accurately characterize the geometric information of a point cloud. This limitation often leads to feature confusion and classification mistakes (e.g., points of building corners and vegetation always share similar statistical features in a local neighbourhood, such as curvature, sphericity, etc). This study aims at solving this problem by leveraging the advantage of both the supervoxel segmentation and multi-scale features. For each point, its multi-scale features within different radii are extracted. Simultaneously, the point cloud is partitioned into simple supervoxel segments. After that, the class probability of each point is predicted by the proposed SegMSF approach that combines multi-scale features with the supervoxel segmentation results. At the end, the effect of data noises is supressed by using a global optimization that encourages spatial consistency of class labels. The proposed method is tested on both airborne laser scanning (ALS) and mobile laser scanning (MLS) point clouds. The experimental results demonstrate that the proposed method performs well in terms of classifying objects of different scales and is robust to noise.</p>

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46

Shao, Yuyuan, Guofeng Tong, Hao Peng, Mingwei Ma, and Jindong Zhang. "DPC-Net: Distributed Point Convolution Network for large-scale point clouds semantic segmentation." Cobot 1 (July 29, 2022): 15. http://dx.doi.org/10.12688/cobot.17468.1.

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Background: Applying convolution neural networks for large-scale 3D point clouds semantic segmentation is quiet challenging, due to the unordered characteristics of 3D data and the computation burden of large-scale point clouds. Methods: To solve these problems, we designed DPC-Net (Distributed Point Convolution Network). The input point clouds of DPC-Net are partitioned by the K-nearest neighbor strategy and reordered based on Euclidean distance. For reducing computation and memory consumption while retaining critical features, the random sampling strategy is used and a distributed point convolution operation is designed. Our novel convolution method extracts parallel local geometric information including space distance and angle features, respectively. Furthermore, our proposed method could be easily and efficiently embedded into many networks for point clouds semantic segmentation. Results: Extensive experimental results on the Semantic3D and CSPC (Complex Scene Point Cloud) datasets indicate that the proposed DPC-Net not only obtains state-of-the-art performances but also reduces semantic segmentation time. Conclusions: In general, we present an efficient and lightweight deep convolutional network, DPC-Net, which captures local geometric features and local contextual information to predict point labels.

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47

Zeng, Bo, Yabo Dong, and Dongming Lu. "A Point-to-Point Interference Measurement Approach for Large-Scale Wireless Sensor Networks." International Journal of Distributed Sensor Networks 8, no. 10 (October 1, 2012): 919815. http://dx.doi.org/10.1155/2012/919815.

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Interference measurement is an important part for use in links scheduling protocol in wireless sensor networks (WSNs). To know wireless interference among nodes in the network is essential to guarantee the efficiency of links scheduling protocol. This work presents a point-to-point interference measurement approach based on time division technique for large-scale low-power WSNs. We first propose an optimized time-slots assignment to reduce the time-slot requirement of interference measurement for large-scale WSNs. We then mitigate the problem of communication by using a subtree-based information distribution method. The accuracy of interference measurement is controlled by a control factor that can be specified by user based on the application requirement to achieve a tradeoff between the overhead of measuring interference and measurement accuracy. Our simulation results indicate that our approach has low-energy and-communication overhead when compared with baseline.

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48

Yun, Jae-Seong, and Jae-Young Sim. "Virtual Point Removal for Large-Scale 3D Point Clouds with Multiple Glass Planes." IEEE Transactions on Pattern Analysis and Machine Intelligence 43, no. 2 (February 1, 2021): 729–44. http://dx.doi.org/10.1109/tpami.2019.2933818.

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49

Holmes, Donald S., and A. Erhan Mergen. "Converting survey results from four-point to five-point scale: a case study." Total Quality Management & Business Excellence 25, no. 1-2 (May 23, 2013): 175–82. http://dx.doi.org/10.1080/14783363.2013.799330.

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50

NIWA, Takeru, and Hiroshi MASUDA. "Efficient Collision Detection for Large-Scale Point-Clouds." Journal of the Japan Society for Precision Engineering 81, no. 8 (2015): 788–92. http://dx.doi.org/10.2493/jjspe.81.788.

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