Готові списки джерел за темами / Fingertip Localization

Добірка наукової літератури з теми "Fingertip Localization"

Автор: Grafiati
Опубліковано: 19 червня 2021
Оновлено: 1 лютого 2022

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Зміст

  1. Статті в журналах
  2. Дисертації

Статті в журналах з теми "Fingertip Localization":

1

Borman, H., and G. Özcan. "Chondroid syringoma at the fingertip: an unusual localization." European Journal of Plastic Surgery 21, no. 6 (July 30, 1998): 311–13. http://dx.doi.org/10.1007/s002380050104.

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2

Suau, Xavier, Marcel Alcoverro, Adolfo López-Méndez, Javier Ruiz-Hidalgo, and Josep R. Casas. "Real-time fingertip localization conditioned on hand gesture classification." Image and Vision Computing 32, no. 8 (August 2014): 522–32. http://dx.doi.org/10.1016/j.imavis.2014.04.015.

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3

Bobich, L. R., J. P. Warren, J. D. Sweeney, S. I. Helms Tillery, and M. Santello. "Spatial localization of electrotactile stimuli on the fingertip in humans." Somatosensory & Motor Research 24, no. 6 (January 2007): 179–88. http://dx.doi.org/10.1080/08990220701637232.

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4

Segil, Jacob, Radhen Patel, John Klingner, Richard F. ff Weir, and Nikolaus Correll. "Multi-modal prosthetic fingertip sensor with proximity, contact, and force localization capabilities." Advances in Mechanical Engineering 11, no. 4 (April 2019): 168781401984464. http://dx.doi.org/10.1177/1687814019844643.

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5

Hsia, Tzu Hsuan, Shogo Okamoto, Yasuhiro Akiyama, and Yoji Yamada. "HumTouch: Localization of Touch on Semi-Conductive Surfaces by Sensing Human Body Antenna Signal." Sensors 21, no. 3 (January 28, 2021): 859. http://dx.doi.org/10.3390/s21030859.

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HumTouch is a touch sensing technology utilizing the environmental electromagnetic wave. The method can be realized using conductive and semi-conductive materials by simply attaching electrodes to the object’s surface. In this study, we compared three methods for localizing a touch on 20×16cm2 and 40×36cm2 papers, on which four or eight electrodes were attached to record the voltages leaked from the human fingertip. The number and positions of the electrodes and the data processing of the voltages differed according to the localization methods. By constructing a kernel regression analysis model between the electrode outputs and the actual physical locations, the touched locations were estimated. Each of the three methods was tested via leave-one-out cross validation. Out of the three methods discussed, two exhibited superior performances in terms of the estimation errors. Of these two methods, one simply uses the voltages recorded by the four electrodes attached on the middle of paper edges as inputs to the regression system. The other uses differential outputs of electrode pairs as the inputs. The smallest mean location errors were 0.31 cm on 20×16cm2 paper and 0.27 cm on 40×36cm2 paper, which are smaller than the size of a fingertip.
6

Wang, Guijin, Cairong Zhang, Xinghao Chen, Xiangyang Ji, Jing-Hao Xue, and Hang Wang. "Bi-Stream Pose-Guided Region Ensemble Network for Fingertip Localization From Stereo Images." IEEE Transactions on Neural Networks and Learning Systems 31, no. 12 (December 2020): 5153–65. http://dx.doi.org/10.1109/tnnls.2020.2964037.

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7

Tsiogka, Aikaterini, Helena Belyayeva, Stavros Sianos, and Dimitrios Rigopoulos. "Transillumination: A Diagnostic Tool to Assess Subungual Glomus Tumors." Skin Appendage Disorders 7, no. 3 (2021): 231–33. http://dx.doi.org/10.1159/000514011.

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Diagnosis of subungual glomus tumors is mainly based on clinical symptoms, including paroxysmal pain, tenderness, and cold intolerance. Dermoscopy, ultrasonography, and MRI constitute further diagnostic tools, commonly performed to demarcate the tumor before surgery. Herein, we present 2 cases of subungual glomus tumors, which could be diagnosed after fingertip transillumination, highlighting that this technique can serve as an easy, noninvasive, and cost-effective adjuvant diagnostic tool, to facilitate the clinical diagnosis of subungual glomus tumors as well as their localization during preoperative assessment.
8

Piccirilli, Massimo, Patrizia D'Alessandro, Alessandro Germani, Virginia Boccardi, Martina Pigliautile, Viola Ancarani, and Maria Stefania Dioguardi. "Age-related decline in interhemispheric transfer of tactile information: The fingertip cross-localization task." Journal of Clinical Neuroscience 77 (July 2020): 75–80. http://dx.doi.org/10.1016/j.jocn.2020.05.035.

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9

Liang, Hui, Junsong Yuan, Daniel Thalmann, and Zhengyou Zhang. "Model-based hand pose estimation via spatial-temporal hand parsing and 3D fingertip localization." Visual Computer 29, no. 6-8 (May 8, 2013): 837–48. http://dx.doi.org/10.1007/s00371-013-0822-4.

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10

Gong, Dunwei, and Ke Liu. "A multi-objective optimization model and its evolution-based solutions for the fingertip localization problem." Pattern Recognition 74 (February 2018): 385–405. http://dx.doi.org/10.1016/j.patcog.2017.09.001.

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Статті в журналах

Дисертації з теми "Fingertip Localization":

1

Hölscher, Phillip. "Deep Learning for estimation of fingertip location in 3-dimensional point clouds : An investigation of deep learning models for estimating fingertips in a 3D point cloud and its predictive uncertainty." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176675.

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Анотація:
Sensor technology is rapidly developing and, consequently, the generation of point cloud data is constantly increasing. Since the recent release of PointNet, it is possible to process this unordered 3-dimensional data directly in a neural network. The company TLT Screen AB, which develops cutting-edge tracking technology, seeks to optimize the localization of the fingertips of a hand in a point cloud. To do so, the identification of relevant 3D neural network models for modeling hands and detection of fingertips in various hand orientations is essential. The Hand PointNet processes point clouds of hands directly and generate estimations of fixed points (joints), including fingertips, of the hands. Therefore, this model was selected to optimize the localization of fingertips for TLT Screen AB and forms the subject of this research. The model has advantages over conventional convolutional neural networks (CNN). First of all, in contrast to the 2D CNN, the Hand PointNet can use the full 3-dimensional spatial information. Compared to the 3D CNN, moreover, it avoids unnecessarily voluminous data and enables more efficient learning. The model was trained and evaluated on the public dataset MRSA Hand. In contrast to previously published work, the main object of this investigation is the estimation of only 5 joints, for the fingertips. The behavior of the model with a reduction from the usual 21 to 11 and only 5 joints are examined. It is found that the reduction of joints contributed to an increase in the mean error of the estimated joints. Furthermore, the examination of the distribution of the residuals of the estimate for fingertips is found to be less dense. MC dropout to study the prediction uncertainty for the fingertips has shown that the uncertainty increases when the joints are decreased. Finally, the results show that the uncertainty is greatest for the prediction of the thumb tip. Starting from the tip of the thumb, it is observed that the uncertainty of the estimates decreases with each additional fingertip.

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