Inverse perspective transform using zero-curvature contour points: application to the localization of some generalized cylinders from a single view

1991 ◽  
Vol 13 (2) ◽  
pp. 185-192 ◽  
Author(s):  
M. Richetin ◽  
M. Dhome ◽  
J.T. Lapreste ◽  
G. Rives
Keyword(s):  
Single View ◽  
Zero Curvature ◽  
Generalized Cylinders

Abstraction and Understanding of Indoor Scenes from Single-View RGB-D Scanning Data

2018 ◽  
Vol 30 (6) ◽  
pp. 1046
Author(s):  
Yuliang Sun ◽  
Yongwei Miao ◽  
Lijie Yu ◽  
Pajarola Renato
Keyword(s):  
Single View ◽  
Indoor Scenes

scholarly journals Minimal Solvers for Single-View Lens-Distorted Camera Auto-Calibration

2021 ◽  
Author(s):  
Yaroslava Lochman ◽  
Oles Dobosevych ◽  
Rostyslav Hryniv ◽  
James Pritts
Keyword(s):  
Single View

scholarly journals Human-Mimetic Estimation of Food Volume from a Single-View RGB Image Using an AI System

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1556
Author(s):  
Zhengeng Yang ◽  
Hongshan Yu ◽  
Shunxin Cao ◽  
Qi Xu ◽  
Ding Yuan ◽  
...  
Keyword(s):  
Chronic Diseases ◽  
Volume Estimation ◽  
Portion Size ◽  
Inner Product ◽  
Probability Vector ◽  
Internal Reference ◽  
Single View ◽  
Unhealthy Diet ◽  
Simplified Procedure ◽  
Traditional Image

It is well known that many chronic diseases are associated with unhealthy diet. Although improving diet is critical, adopting a healthy diet is difficult despite its benefits being well understood. Technology is needed to allow an assessment of dietary intake accurately and easily in real-world settings so that effective intervention to manage being overweight, obesity, and related chronic diseases can be developed. In recent years, new wearable imaging and computational technologies have emerged. These technologies are capable of performing objective and passive dietary assessments with a much simplified procedure than traditional questionnaires. However, a critical task is required to estimate the portion size (in this case, the food volume) from a digital image. Currently, this task is very challenging because the volumetric information in the two-dimensional images is incomplete, and the estimation involves a great deal of imagination, beyond the capacity of the traditional image processing algorithms. In this work, we present a novel Artificial Intelligent (AI) system to mimic the thinking of dietitians who use a set of common objects as gauges (e.g., a teaspoon, a golf ball, a cup, and so on) to estimate the portion size. Specifically, our human-mimetic system “mentally” gauges the volume of food using a set of internal reference volumes that have been learned previously. At the output, our system produces a vector of probabilities of the food with respect to the internal reference volumes. The estimation is then completed by an “intelligent guess”, implemented by an inner product between the probability vector and the reference volume vector. Our experiments using both virtual and real food datasets have shown accurate volume estimation results.

scholarly journals Learning pose-invariant 3D object reconstruction from single-view images

2021 ◽  
Vol 423 ◽  
pp. 407-418
Author(s):  
Bo Peng ◽  
Wei Wang ◽  
Jing Dong ◽  
Tieniu Tan
Keyword(s):  
Object Reconstruction ◽  
3D Object ◽  
Single View ◽  
3D Object Reconstruction

scholarly journals Multiview Deep Feature Learning Network for SAR Automatic Target Recognition

2021 ◽  
Vol 13 (8) ◽  
pp. 1455
Author(s):  
Jifang Pei ◽  
Weibo Huo ◽  
Chenwei Wang ◽  
Yulin Huang ◽  
Yin Zhang ◽  
...  
Keyword(s):  
Target Recognition ◽  
Feature Learning ◽  
Automatic Target Recognition ◽  
Operating Conditions ◽  
Sar Images ◽  
Single View ◽  
Learning Network ◽  
Deep Feature ◽  
Deep Feature Learning ◽  
Classification Information

Multiview synthetic aperture radar (SAR) images contain much richer information for automatic target recognition (ATR) than a single-view one. It is desirable to establish a reasonable multiview ATR scheme and design effective ATR algorithm to thoroughly learn and extract that classification information, so that superior SAR ATR performance can be achieved. Hence, a general processing framework applicable for a multiview SAR ATR pattern is first given in this paper, which can provide an effective approach to ATR system design. Then, a new ATR method using a multiview deep feature learning network is designed based on the proposed multiview ATR framework. The proposed neural network is with a multiple input parallel topology and some distinct deep feature learning modules, with which significant classification features, the intra-view and inter-view features existing in the input multiview SAR images, will be learned simultaneously and thoroughly. Therefore, the proposed multiview deep feature learning network can achieve an excellent SAR ATR performance. Experimental results have shown the superiorities of the proposed multiview SAR ATR method under various operating conditions.

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