2A2-E07 Configuration and Calibration Method of Color Depth Image Sensor for Humanoids Using Range Sensor and Stereo Cameras

2010 ◽  
Vol 2010 (0) ◽  
pp. _2A2-E07_1-_2A2-E07_4
Author(s):  
Yohei KAKIUCHI ◽  
Kei OKADA ◽  
Masayuki INABA
Keyword(s):  
Calibration Method ◽  
Image Sensor ◽  
Depth Image ◽  
Range Sensor ◽  
Stereo Cameras ◽  
Color Depth

scholarly journals Iranian kinect face database (IKFDB): a color-depth based face database collected by kinect v.2 sensor

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Seyed Muhammad Hossein Mousavi ◽  
S. Younes Mirinezhad
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Depth Image ◽  
Sensor Technology ◽  
Support Vector ◽  
Expression Recognition ◽  
Face Database ◽  
Depth Data ◽  
Color Depth

AbstractThis study presents a new color-depth based face database gathered from different genders and age ranges from Iranian subjects. Using suitable databases, it is possible to validate and assess available methods in different research fields. This database has application in different fields such as face recognition, age estimation and Facial Expression Recognition and Facial Micro Expressions Recognition. Image databases based on their size and resolution are mostly large. Color images usually consist of three channels namely Red, Green and Blue. But in the last decade, another aspect of image type has emerged, named “depth image”. Depth images are used in calculating range and distance between objects and the sensor. Depending on the depth sensor technology, it is possible to acquire range data differently. Kinect sensor version 2 is capable of acquiring color and depth data simultaneously. Facial expression recognition is an important field in image processing, which has multiple uses from animation to psychology. Currently, there is a few numbers of color-depth (RGB-D) facial micro expressions recognition databases existing. With adding depth data to color data, the accuracy of final recognition will be increased. Due to the shortage of color-depth based facial expression databases and some weakness in available ones, a new and almost perfect RGB-D face database is presented in this paper, covering Middle-Eastern face type. In the validation section, the database will be compared with some famous benchmark face databases. For evaluation, Histogram Oriented Gradients features are extracted, and classification algorithms such as Support Vector Machine, Multi-Layer Neural Network and a deep learning method, called Convolutional Neural Network or are employed. The results are so promising.

Integrated real-time polarization image sensor based on UV-NIL and calibration method

2022 ◽  
pp. 1-1
Author(s):  
Chuanlong Guan ◽  
Ran Zhang ◽  
Jinkui Chu ◽  
Ze Liu ◽  
Yuanyi Fan ◽  
...  
Keyword(s):  
Real Time ◽  
Calibration Method ◽  
Image Sensor ◽  
Polarization Image

Design of an Antagonistic Exercise Support System Using a Depth Image Sensor

2016 ◽  
Author(s):  
Toshiya Watanabe ◽  
Kazuki Kamata ◽  
Sheik Hasan ◽  
Susumu Shibusawa ◽  
Masaru Kamada ◽  
...  
Keyword(s):  
Support System ◽  
Image Sensor ◽  
Depth Image

Predicting the Quality of View Synthesis With Color-Depth Image Fusion

2020 ◽  
pp. 1-1
Author(s):  
Leida Li ◽  
Yipo Huang ◽  
Jinjian Wu ◽  
Ke Gu ◽  
Yuming Fang
Keyword(s):  
Image Fusion ◽  
View Synthesis ◽  
Depth Image ◽  
Color Depth

Research on Superimposed Terrain Model for Teleoperation Work Efficiency

2016 ◽  
Vol 28 (2) ◽  
pp. 173-184 ◽  
Author(s):  
Takanobu Tanimoto ◽  
◽  
Ryo Fukano ◽  
Kei Shinohara ◽  
Keita Kurashiki ◽  
...  
Keyword(s):  
Ground Surface ◽  
Digital Terrain Model ◽  
Image Sensor ◽  
Depth Image ◽  
Work Efficiency ◽  
Lower Efficiency ◽  
Terrain Model ◽  
Positioning Errors ◽  
Need To Evaluate ◽  
Binocular Stereo

[abstFig src='/00280002/08.jpg' width=""300"" text='Superimposed terrain model in operator's view image' ]In recent years, unmanned construction based on the teleoperation of construction equipment has increasingly been used in disaster sites or mines. However, operations based on teleoperation are based on 2D images, in which the lack of perspective results in considerably lower efficiency when compared with on-board operations. Previous studies employed multi-viewpoint images or binocular stereo, which resulted in problems, such as lower efficiency, caused by the operator's need to evaluate distances by shifting his or her line of sight, or eye fatigue due to binocular stereo. Thus, the present study aims to improve the work efficiency of teleoperation by superimposing a 3D model of the terrain on the on-board operator's view image. The surrounding terrain is measured by a depth image sensor and represented as a digital terrain model, which is generated and updated in real time. The terrain model is transformed into the on-board operator's view, on which an artificial shadow of the bucket tip and an evenly spaced grid projected to the ground surface are superimposed. This allows the operator to visually evaluate the bucket tip position from the artificial shadow and the distance between the excavation point and bucket tip from the terrain grid. An experiment was conducted investigating the positioning of the bucket tip by teleoperation using a miniature excavator and the terrain model superimposed display. The results showed that the standard deviations of the positioning errors measured with the superimposed display were lower by 30% or more than those obtained without the superimposed display, while they were approximately equal to those acquired using binocular stereo. We thus demonstrated the effectiveness of the superimposed display in improving work efficiency in teleoperation.

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