scholarly journals Graph Cut-Based Human Body Segmentation in Color Images Using Skeleton Information from the Depth Sensor

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 393 ◽  
Author(s):  
Jonha Lee ◽  
Dong-Wook Kim ◽  
Chee Won ◽  
Seung-Won Jung
Keyword(s):  
Human Body ◽  
Color Image ◽  
Depth Image ◽  
Body Region ◽  
Reasonable Accuracy ◽  
Depth Sensor ◽  
Complicated Shape ◽  
Depth Sensors ◽  
Body Segmentation ◽  
Human Body Segmentation

Segmentation of human bodies in images is useful for a variety of applications, including background substitution, human activity recognition, security, and video surveillance applications. However, human body segmentation has been a challenging problem, due to the complicated shape and motion of a non-rigid human body. Meanwhile, depth sensors with advanced pattern recognition algorithms provide human body skeletons in real time with reasonable accuracy. In this study, we propose an algorithm that projects the human body skeleton from a depth image to a color image, where the human body region is segmented in the color image by using the projected skeleton as a segmentation cue. Experimental results using the Kinect sensor demonstrate that the proposed method provides high quality segmentation results and outperforms the conventional methods.

scholarly journals Automatic corrections of human body depth maps using deep neural networks

2020 ◽  
Vol 17 (3) ◽  
pp. 285-296
Author(s):  
Gorana Gojic ◽  
Radovan Turovic ◽  
Dinu Dragan ◽  
Dusan Gajic ◽  
Veljko Petrovic
Keyword(s):  
Human Body ◽  
Color Image ◽  
Depth Map ◽  
The Body ◽  
Second Step ◽  
Body Depth ◽  
Depth Maps ◽  
Body Segmentation ◽  
Human Body Segmentation ◽  
Simple Average

This paper presents an approach to correcting misclassified pixels in depth maps representing parts of the human body. A misclassified pixel is a pixel of a depth map which, incorrectly, has the ?background? value and does not accurately reflect the distance from the sensor to the body being scanned. A completely automatic, deep learning based solution for depth map correction is proposed. As an input, the solution requires a color image and a corresponding erroneous depth map. The input color image is segmented using deep neural network for human body segmentation. The extracted segments are further used as guidance to find and amend the misclassified pixels on the depth map using a simple average based filter. Unlike other depth map refinement solutions, this paper designs a method for the improvement of the input depth map in terms of completeness instead of precision. The proposed method does not exclude the application of other refinement methods. Instead, it can be used as the first step in a depth map enhancement pipeline to determine approximate depths for erroneous pixels, while other refinement methods can be applied in a second step to improve the accuracy of the recovered depths.

scholarly journals Approximate Depth Shape Reconstruction for RGB-D Images Captured from HMDs for Mixed Reality Applications

2020 ◽  
Vol 6 (3) ◽  
pp. 11
Author(s):  
Naoyuki Awano
Keyword(s):  
Mixed Reality ◽  
Signal To Noise Ratio ◽  
Low Cost ◽  
Real Space ◽  
Shape Reconstruction ◽  
Human Vision ◽  
Depth Image ◽  
Depth Sensor ◽  
Depth Sensors ◽  
Object Shapes

Depth sensors are important in several fields to recognize real space. However, there are cases where most depth values in a depth image captured by a sensor are constrained because the depths of distal objects are not always captured. This often occurs when a low-cost depth sensor or structured-light depth sensor is used. This also occurs frequently in applications where depth sensors are used to replicate human vision, e.g., when using the sensors in head-mounted displays (HMDs). One ideal inpainting (repair or restoration) approach for depth images with large missing areas, such as partial foreground depths, is to inpaint only the foreground; however, conventional inpainting studies have attempted to inpaint entire images. Thus, under the assumption of an HMD-mounted depth sensor, we propose a method to inpaint partially and reconstruct an RGB-D depth image to preserve foreground shapes. The proposed method is comprised of a smoothing process for noise reduction, filling defects in the foreground area, and refining the filled depths. Experimental results demonstrate that the inpainted results produced using the proposed method preserve object shapes in the foreground area with accurate results of the inpainted area with respect to the real depth with the peak signal-to-noise ratio metric.

Robust human body segmentation based on part appearance and spatial constraint

2013 ◽  
Vol 118 ◽  
pp. 191-202 ◽  
Author(s):  
Lei Huang ◽  
Sheng Tang ◽  
Yongdong Zhang ◽  
Shiguo Lian ◽  
Shouxun Lin
Keyword(s):  
Human Body ◽  
Spatial Constraint ◽  
Body Segmentation ◽  
Human Body Segmentation
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