scholarly journals Virtual DressUp system by using image deformation method

2009 ◽  
Vol 15 (2) ◽  
pp. 1-8
Author(s):  
김나리 ◽  
In-Kwon Lee ◽  
Jong-Chul Yoon
Keyword(s):  
Deformation Method ◽  
Image Deformation

Modifying Perceived Size of a Handled Object through Hand Image Deformation

2013 ◽  
Vol 22 (3) ◽  
pp. 255-270 ◽  
Author(s):  
Yuki Ban ◽  
Takuji Narumi ◽  
Tomohiro Tanikawa ◽  
Michitaka Hirose
Keyword(s):  
Visual Representation ◽  
Shape Perception ◽  
Size Perception ◽  
Visual Object ◽  
Display System ◽  
Haptic Interaction ◽  
Image Deformation ◽  
Virtual Objects ◽  
The Difference ◽  
Hand Image

In this study, we aim to construct a perception-based shape display system to provide users with the sensation of touching virtual objects of varying shapes using only a simple mechanism. Thus far, we have proved that identified curved surface shapes or edge angles can be modified by displacing the visual representation of the user's hand. However, using this method, we cannot emulate multifinger touch, because of spatial unconformity. To solve this problem, we focus on modifying the identification of shapes using two fingers by deforming the visual representation of the user's hand. We devised a video see-through system that enables us to change the perceived shape of an object that a user is touching visually. The visual representation of the user's hand is deformed as if the user were handling a visual object; however, the user is actually handling an object of a different shape. Using this system, we conducted two experiments to investigate the effects of visuo-haptic interaction and evaluate its effectiveness. One is an investigation on the modification of size perception to confirm that the fingers did not stroke the shape but only touched it statically. The other is an investigation on the modification of shape perception for confirming that the fingers dynamically stroked the surface of the shape. The results of these experiments show that the perceived sizes of objects handled using a thumb and other finger(s) could be modified if the difference between the size of physical and visual stimuli was in the −40% to 35% range. In addition, we found that the algorithm can create an effect of shape perception modification when users stroke the shape with multiple fingers.

scholarly journals LARGE SCALE CITY MAPPING USING SATELLITE IMAGERY / KOSMINIŲ NUOTRAUKŲ IŠ GOOGLE EARTH, TAIKOMŲ MIESTAMS KARTOGRAFUOTI STAMBIUOJU MASTELIU, REKTIFIKAVIMAS / РЕКТИФИКАЦИЯ КОСМИЧЕСКИХ СНИМКОВ ИЗ GOOGLE EARTH ДЛЯ КРУПНОМАСШТАБНОГО КАРТОГРАФИРОВАНИЯ ГОРОДОВ

2012 ◽  
Vol 37 (4) ◽  
pp. 168-171 ◽  
Author(s):  
Birutė Ruzgienė ◽  
Qian Yi Xiang ◽  
Silvija Gečytė
Keyword(s):  
Satellite Imagery ◽  
Large Scale ◽  
Modern Technology ◽  
Google Earth ◽  
Aerial Images ◽  
Control Points ◽  
Image Rectification ◽  
Image Deformation ◽  
Map Construction ◽  
Short Period

The rectification of high resolution digital aerial images or satellite imagery employed for large scale city mapping is modern technology that needs well distributed and accurately defined control points. Digital satellite imagery, obtained using widely known software Google Earth, can be applied for accurate city map construction. The method of five control points is suggested for imagery rectification introducing the algorithm offered by Prof. Ruan Wei (tong ji University, Shanghai). Image rectification software created on the basis of the above suggested algorithm can correct image deformation with required accuracy, is reliable and keeps advantages in flexibility. Experimental research on testing the applied technology has been executed using GeoEye imagery with Google Earth builder over the city of Vilnius. Orthophoto maps at the scales of 1:1000 and 1:500 are generated referring to the methodology of five control points. Reference data and rectification results are checked comparing with those received from processing digital aerial images using a digital photogrammetry approach. The image rectification process applying the investigated method takes a short period of time (about 4-5 minutes) and uses only five control points. The accuracy of the created models satisfies requirements for large scale mapping. Santrauka Didelės skiriamosios gebos skaitmeninių nuotraukų ir kosminių nuotraukų rektifikavimas miestams kartografuoti stambiuoju masteliu yra nauja technologija. Tai atliekant būtini tikslūs ir aiškiai matomi kontroliniai taškai. Skaitmeninės kosminės nuotraukos, gautos taikant plačiai žinomą programinį paketą Google Earth, gali būti naudojamos miestams kartografuoti dideliu tikslumu. Siūloma nuotraukas rektifikuoti Penkių kontrolinių taskų metodu pagal prof. Ruan Wei (Tong Ji universitetas, Šanchajus) algoritmą. Moksliniam eksperimentui pasirinkta Vilniaus GeoEye nuotrauka iš Google Earth. 1:1000 ir 1:500 mastelio ortofotografiniai žemėlapiai sudaromi Penkių kontrolinių taškų metodu. Rektifikavimo duomenys lyginami su skaitmeninių nuotraukų apdorojimo rezultatais, gautais skaitmeninės fotogrametrijos metodu. Nuotraukų rektifikavimas Penkių kontrolinių taskų metodu atitinka kartografavimo stambiuoju masteliu reikalavimus, sumažėja laiko sąnaudos. Резюме Ректификация цифровых и космических снимков высокой резолюции для крупномасштабного картографирования является новой технологией, требующей точных и четких контрольных точек. Цифровые космические снимки, полученные с использованием широкоизвестного программного пакета Google Earth, могут применяться для точного картографирования городов. Для ректификации снимков предложен метод пяти контрольных точек с применением алгоритма проф. Ruan Wei (Университет Tong Ji, Шанхай). Для научного эксперимента использован снимок города Вильнюса GeoEye из Google Earth. Ортофотографические карты в масштабе 1:1000 и 1:500 генерируются с применением метода пяти контрольных точек. Полученные результаты и данные ректификации сравниваются с результатами цифровых снимков, полученных с применением метода цифровой фотограмметрии. Ректификация снимков с применением метода пяти контрольных точек уменьшает временные расходы и удовлетворяет требования, предъявляемые к крупномасштабному картографированию.

scholarly journals Modeling of Personalized Anatomy Using Plastic Strains

2021 ◽  
Vol 40 (2) ◽  
pp. 1-21
Author(s):  
Bohan Wang ◽  
George Matcuk ◽  
Jernej Barbič
Keyword(s):  
Magnetic Resonance Imaging ◽  
Computed Tomography ◽  
Magnetic Resonance ◽  
Large Strain ◽  
Human Anatomy ◽  
Shape Deformation ◽  
Human Hand ◽  
Plastic Strains ◽  
Resonance Imaging ◽  
Deformation Method

We present a method for modeling solid objects undergoing large spatially varying and/or anisotropic strains, and use it to reconstruct human anatomy from medical images. Our novel shape deformation method uses plastic strains and the finite element method to successfully model shapes undergoing large and/or anisotropic strains, specified by sparse point constraints on the boundary of the object. We extensively compare our method to standard second-order shape deformation methods, variational methods, and surface-based methods, and demonstrate that our method avoids the spikiness, wiggliness, and other artifacts of previous methods. We demonstrate how to perform such shape deformation both for attached and un-attached (“free flying”) objects, using a novel method to solve linear systems with singular matrices with a known nullspace. Although our method is applicable to general large-strain shape deformation modeling, we use it to create personalized 3D triangle and volumetric meshes of human organs, based on magnetic resonance imaging or computed tomography scans. Given a medically accurate anatomy template of a generic individual, we optimize the geometry of the organ to match the magnetic resonance imaging or computed tomography scan of a specific individual. Our examples include human hand muscles, a liver, a hip bone, and a gluteus medius muscle (“hip abductor”).

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