A learning-based variable size part extraction architecture for 6D object pose recovery in depth images

2017 ◽  
Vol 63 ◽  
pp. 38-50 ◽  
Author(s):  
Caner Sahin ◽  
Rigas Kouskouridas ◽  
Tae-Kyun Kim
Author(s):  
Meysam Madadi ◽  
Sergio Escalera ◽  
Alex Carruesco ◽  
Carlos Andujar ◽  
Xavier Baro ◽  
...  
Keyword(s):  

2018 ◽  
Vol 79 ◽  
pp. 63-75 ◽  
Author(s):  
Meysam Madadi ◽  
Sergio Escalera ◽  
Alex Carruesco ◽  
Carlos Andujar ◽  
Xavier Baró ◽  
...  

Author(s):  
Sukhendra Singh ◽  
G. N. Rathna ◽  
Vivek Singhal

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.


2021 ◽  
Vol 13 (5) ◽  
pp. 935
Author(s):  
Matthew Varnam ◽  
Mike Burton ◽  
Ben Esse ◽  
Giuseppe Salerno ◽  
Ryunosuke Kazahaya ◽  
...  

SO2 cameras are able to measure rapid changes in volcanic emission rate but require accurate calibrations and corrections to convert optical depth images into slant column densities. We conducted a test at Masaya volcano of two SO2 camera calibration approaches, calibration cells and co-located spectrometer, and corrected both calibrations for light dilution, a process caused by light scattering between the plume and camera. We demonstrate an advancement on the image-based correction that allows the retrieval of the scattering efficiency across a 2D area of an SO2 camera image. When appropriately corrected for the dilution, we show that our two calibration approaches produce final calculated emission rates that agree with simultaneously measured traverse flux data and each other but highlight that the observed distribution of gas within the image is different. We demonstrate that traverses and SO2 camera techniques, when used together, generate better plume speed estimates for traverses and improved knowledge of wind direction for the camera, producing more reliable emission rates. We suggest combining traverses and the SO2 camera should be adopted where possible.


2021 ◽  
Vol 13 (1) ◽  
Author(s):  
L. C. Schenkel ◽  
E. Aref-Eshghi ◽  
K. Rooney ◽  
J. Kerkhof ◽  
M. A. Levy ◽  
...  

Abstract Background Phelan-McDermid syndrome is characterized by a range of neurodevelopmental phenotypes with incomplete penetrance and variable expressivity. It is caused by a variable size and breakpoint microdeletions in the distal long arm of chromosome 22, referred to as 22q13.3 deletion syndrome, including the SHANK3 gene. Genetic defects in a growing number of neurodevelopmental genes have been shown to cause genome-wide disruptions in epigenomic profiles referred to as epi-signatures in affected individuals. Results In this study we assessed genome-wide DNA methylation profiles in a cohort of 22 individuals with Phelan-McDermid syndrome, including 11 individuals with large (2 to 5.8 Mb) 22q13.3 deletions, 10 with small deletions (< 1 Mb) or intragenic variants in SHANK3 and one mosaic case. We describe a novel genome-wide DNA methylation epi-signature in a subset of individuals with Phelan-McDermid syndrome. Conclusion We identified the critical region including the BRD1 gene as responsible for the Phelan-McDermid syndrome epi-signature. Metabolomic profiles of individuals with the DNA methylation epi-signature showed significantly different metabolomic profiles indicating evidence of two molecularly and phenotypically distinct clinical subtypes of Phelan-McDermid syndrome.


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