A novel edge-enabled SLAM solution using projected depth image information

2019 ◽  
Vol 32 (19) ◽  
pp. 15369-15381 ◽  
Author(s):  
Jian-qiang Li ◽  
Yi-fan Zhang ◽  
Zhuang-zhuang Chen ◽  
Jia Wang ◽  
Min Fang ◽  
...  
Keyword(s):  
Depth Image ◽  
Image Information

A Track-Based Gesture Recognition Algorithm for Kinect

2015 ◽  
Vol 738-739 ◽  
pp. 334-338 ◽  
Author(s):  
Ying Wang ◽  
Ling Zhang
Keyword(s):  
Gesture Recognition ◽  
Golden Section ◽  
Recognition Algorithm ◽  
Depth Image ◽  
Kinect Sensor ◽  
Recognition Method ◽  
Image Information ◽  
Golden Section Search ◽  
Highly Effective

This paper presents a new gesture track recognition method based on the depth image information received from the Kinect sensor. First, a Kinect sensor is used to obtain the coordinates of a moving arm. Then, the gesture tracks corresponding to these coordinates are analyzed. Matching and recognition of gesture tracks are implemented by performing golden section search. The results show that this track-based method is highly effective in gesture recognition.

scholarly journals Edge Detection from RGB-D Image Based on Structured Forests

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Heng Zhang ◽  
Zhenqiang Wen ◽  
Yanli Liu ◽  
Gang Xu
Keyword(s):  
Random Forest ◽  
Edge Detection ◽  
Random Forests ◽  
Information Gain ◽  
Fundamental Problem ◽  
Principal Component ◽  
Bilateral Filter ◽  
Depth Image ◽  
Image Information ◽  
Pca Method

This paper looks into the fundamental problem in computer vision: edge detection. We propose a new edge detector using structured random forests as the classifier, which can make full use of RGB-D image information from Kinect. Before classification, the adaptive bilateral filter is used for the denoising processing of the depth image. As data sources, information of 13 channels from RGB-D image is computed. In order to train the random forest classifier, the approximation measurement of the information gain is used. All the structured labels at a given node are mapped to a discrete set of labels using the Principal Component Analysis (PCA) method. NYUD2 dataset is used to train our structured random forests. The random forest algorithm is used to classify the RGB-D image information for extracting the edge of the image. In addition to the proposed methodology, the quantitative comparisons of different algorithms are presented. The results of the experiments demonstrate the significant improvements of our algorithm over the state of the art.

The Depth Image Restoring Based on Harris Corner Detection

2013 ◽  
Vol 791-793 ◽  
pp. 852-856
Author(s):  
Ao Shuang Dong ◽  
Xiao Liu
Keyword(s):  
The Body ◽  
Corner Detection ◽  
Depth Image ◽  
Image Information ◽  
Depth Data ◽  
Harris Corner ◽  
Harris Corner Detection

Obtaining the depth image is the foundation of the research on other direction of Kinect. When to obtain the depth image information in a Kinect, it can t obtain the depth image accurately if there is a shelter in front of the body, and then the recognition and the restoration of the skeleton will become very difficult. This paper uses the method of Harris corner detection to detect shelter corner which is used to detect the range of shelter and restore the depth image information. But different from the traditional way, this paper uses the depth data values in the corner detection instead of the gray date values. After detecting corner, a series of actions of depth value substitution and smoothing will be done to repair occluded depth image.

scholarly journals Persian sign language detection based on normalized depth image information

2019 ◽  
Vol 13 (3) ◽  
pp. 29-39
Author(s):  
Shahab Rajabi ◽  
Amir Mousavinia ◽  
◽  
Keyword(s):  
Sign Language ◽  
Depth Image ◽  
Image Information ◽  
Language Detection

Formation geological depth image according to refraction and reflection marine seismic data

2017 ◽  
Vol 39 (6) ◽  
pp. 106-121
Author(s):  
A. O. Verpahovskaya ◽  
V. N. Pilipenko ◽  
Е. V. Pylypenko
Keyword(s):  
Seismic Data ◽  
Depth Image ◽  
Marine Seismic
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