The Power Is in Your Hands: 3D Analysis of Hand Gestures in Naturalistic Video

2013 ◽  
Author(s):  
Eshed Ohn-Bar ◽  
Mohan M. Trivedi
Keyword(s):  
3D Analysis ◽  
Hand Gestures

Direct phase determination in electron crystallography: small organic molecules

1992 ◽  
Vol 50 (2) ◽  
pp. 1166-1167
Author(s):  
Douglas L. Dorset
Keyword(s):  
Organic Molecules ◽  
Data Sets ◽  
Temperature Structure ◽  
3D Analysis ◽  
Intensity Data ◽  
Electron Crystallography ◽  
Phase Determination ◽  
Measured Intensity ◽  
3D Data

The quantitative use of electron diffraction intensity data for the determination of crystal structures represents the pioneering achievement in the electron crystallography of organic molecules, an effort largely begun by B. K. Vainshtein and his co-workers. However, despite numerous representative structure analyses yielding results consistent with X-ray determination, this entire effort was viewed with considerable mistrust by many crystallographers. This was no doubt due to the rather high crystallographic R-factors reported for some structures and, more importantly, the failure to convince many skeptics that the measured intensity data were adequate for ab initio structure determinations.We have recently demonstrated the utility of these data sets for structure analyses by direct phase determination based on the probabilistic estimate of three- and four-phase structure invariant sums. Examples include the structure of diketopiperazine using Vainshtein's 3D data, a similar 3D analysis of the room temperature structure of thiourea, and a zonal determination of the urea structure, the latter also based on data collected by the Moscow group.

Stroop-Like Interference Between Structural and Functional Hand Gestures Within Objects

2013 ◽  
Author(s):  
Margaux Larre-Perez ◽  
Pierre Jacob ◽  
Therese Collins
Keyword(s):  
Hand Gestures

Statistical 3D Analysis and Modeling of Complex Particle Systems based on Tomographic Image Data

2019 ◽  
Vol 56 (12) ◽  
pp. 787-796
Author(s):  
O. Furat ◽  
B. Prifling ◽  
D. Westhoff ◽  
M. Weber ◽  
V. Schmidt
Keyword(s):  
Image Data ◽  
Particle Systems ◽  
3D Analysis ◽  
Tomographic Image ◽  
Complex Particle

SIMILARITIES AND DIFFERENT FEATURES OF HAND GESTURES IN AMERICAN AND KYRGYZ CULTURES

2019 ◽  
pp. 19-22
Author(s):  
Avtandil kyzy Ya
Keyword(s):  
Everyday Life ◽  
Facial Expressions ◽  
Main Part ◽  
Body Language ◽  
Hand Gestures ◽  
Common Definition ◽  
The World ◽  
The Common ◽  
Definition Of ◽  
Different Cultures

Abstract: This paper highlights similarities and different features of the category of kinesics “hand gestures”, its frequency usage and acceptance by different individuals in two different cultures. This study shows its similarities, differences and importance of the gestures, for people in both cultures. Consequently, kinesics study was mentioned as a main part of body language. As indicated in the article, the study kinesics was not presented in the Kyrgyz culture well enough, though Kyrgyz people use hand gestures a lot in their everyday life. The research paper begins with the common definition of hand gestures as a part of body language, several handshake categories like: the finger squeeze, the limp fish, the two-handed handshake were explained by several statements in the English and Kyrgyz languages. Furthermore, this article includes definitions and some idioms containing hand, shake, squeeze according to the Oxford and Academic Dictionary to show readers the figurative meanings of these common words. The current study was based on the books of writers Allan and Barbara Pease “The definite book of body language” 2004, Romana Lefevre “Rude hand gestures of the world”2011 etc. Key words: kinesics, body language, gestures, acoustics, applause, paralanguage, non-verbal communication, finger squeeze, perceptions, facial expressions. Аннотация. Бул макалада вербалдык эмес сүйлѳшүүнүн бѳлүгү болуп эсептелген “колдордун жандоо кыймылы”, алардын эки башка маданиятта колдонулушу, айырмачылыгы жана окшош жактары каралган. Макаланын максаты болуп “колдордун жандоо кыймылынын” мааниси, айырмасы жана эки маданиятта колдонулушу эсептелет. Ошону менен бирге, вербалдык эмес сүйлѳшүүнүн бѳлүгү болуп эсептелген “кинесика” илими каралган. Берилген макалада кѳрсѳтүлгѳндѳй, “кинесика” илими кыргыз маданиятында толугу менен изилденген эмес, ошого карабастан “кинесика” илиминин бѳлүгү болуп эсептелген “колдордун жандоо кыймылы” кыргыз элинин маданиятында кѳп колдонулат. Андан тышкары, “колдордун жандоо кыймылынын” бир нече түрү, англис жана кыргыз тилдеринде ма- селен аркылуу берилген.Тѳмѳнкү изилдѳѳ ишин жазууда чет элдик жазуучулардын эмгектери колдонулду. Түйүндүү сѳздѳр: кинесика, жандоо кыймылы, акустика,кол чабуулар, паралингвистика, вербалдык эмес баарлашуу,кол кысуу,кабыл алуу сезими. Аннотация. В данной статье рассматриваются сходства и различия “жестикуляции” и частота ее использования, в американской и кыргызской культурах. Следовательно, здесь было упомянуто понятие “кинесика” как основная часть языка тела. Как указано в статье, “кинесика” не была представлена в кыргызской культуре достаточно хорошо, хотя кыргызский народ часто использует жестикуляцию в повседневной жизни. Исследовательская работа начинается с общего определения “жестикуляции” как части языка тела и несколько категорий жестикуляции, таких как: сжатие пальца, слабое рукопожатие, рукопожатие двумя руками, были объяснены несколькими примерами на английском и кыргызском языках. Кроме того, эта статья включает определения слов “рука”, “рукопожатие”, “сжатие” и некоторые идиомы, содержащие данных слов согласно Оксфордскому и Академическому словарю, чтобы показать читателям их образное значение. Данное исследование было основано на книгах писателей Аллана и Барбары Пиз «Определенная книга языка тела» 2004 года, Романа Лефевра «Грубые жестикуляции мира» 2011 года и т.д. Ключевые слова: кинесика, язык жестов, жесты, акустика, аплодисменты, паралингвистика, невербальная коммуникация, сжатие пальца, чувство восприятия, выражение лиц.

Analysis of Hand Gestures by Computer Perspective Vision

2020 ◽  
Author(s):  
Vijayaraghavan D ◽  
Harini K R ◽  
Vithya Ganeshan ◽  
Sushmidha S
Keyword(s):  
Hand Gestures

Computer Control with Hand Gestures Using Computer Vision

2020 ◽  
Author(s):  
Nirmala J S ◽  
Ajeet Kumar ◽  
Adith Jose E A ◽  
Kapil Kumar ◽  
Abhishek R Malvadkar
Keyword(s):  
Computer Vision ◽  
Computer Control ◽  
Hand Gestures

Kinematic Analysis of the Reconstruction Error of A Calibration Volume for 3d Analysis in Swimming~!2009-07-05~!2009-11-09~!2010-04-20~!

2010 ◽  
Vol 3 (1) ◽  
pp. 28-30 ◽  
Author(s):  
S. Brandao ◽  
P. Figueiredo ◽  
P. Goncalves ◽  
J. P. Vilas-Boas ◽  
R. J. Fernandes
Keyword(s):  
Kinematic Analysis ◽  
Reconstruction Error ◽  
3D Analysis

Indian Sign Language Recognition on PYNQ Board

2020 ◽  
Vol 13 ◽  
Author(s):  
Sukhendra Singh ◽  
G. N. Rathna ◽  
Vivek Singhal
Keyword(s):  
Sign Language ◽  
Hand Gesture ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Hand Gestures ◽  
Depth Images ◽  
Kinect Camera ◽  
Impaired People ◽  
Web Camera ◽  
Indian Sign Language

Introduction: Sign language is the only way to communicate for speech-impaired people. But this sign language is not known to normal people so this is the cause of barrier in communicating. This is the problem faced by speech impaired people. In this paper, we have presented our solution which captured hand gestures with Kinect camera and classified the hand gesture into its correct symbol. Method: We used Kinect camera not the ordinary web camera because the ordinary camera does not capture its 3d orientation or depth of an image from camera however Kinect camera can capture 3d image and this will make classification more accurate. Result: Kinect camera will produce a different image for hand gestures for ‘2’ and ‘V’ and similarly for ‘1’ and ‘I’ however, normal web camera will not be able to distinguish between these two. We used hand gesture for Indian sign language and our dataset had 46339, RGB images and 46339 depth images. 80% of the total images were used for training and the remaining 20% for testing. In total 36 hand gestures were considered to capture alphabets and alphabets from A-Z and 10 for numeric, 26 for digits from 0-9 were considered to capture alphabets and Keywords. Conclusion: Along with real-time implementation, we have also shown the comparison of the performance of the various machine learning models in which we have found out the accuracy of CNN on depth- images has given the most accurate performance than other models. All these resulted were obtained on PYNQ Z2 board.

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