scholarly journals Obstacle detection using Microsoft Kinect

2021 ◽  
Author(s):  
Niclas Zeller
Keyword(s):  
Image Processing ◽  
Obstacle Detection ◽  
Microsoft Kinect ◽  
Depth Image ◽  
Normal Vector ◽  
Blind Person ◽  
Camera System ◽  
Real Time System ◽  
Plane Fitting ◽  
Kinect Camera

This thesis presents the development of image processing algorithms based on a Microsoft Kinect camera system. The algorithms developed during this thesis are applied on the depth image received from Kinect and are supposed to model a three dimensional object based representation of the recorded scene. The motivation behind this thesis is to develop a system which assists visually impaired people by navigating through unknown environments. The developed system is able to detect obstacles in the recorded scene and to warn about these obstacles. Since the goal of this thesis was not to develop a complete real time system but to invent reliable algorithms solving this task, the algorithms were developed in MATLAB. Additionally a control software was developed by which depth as well as color images can be received from Kinect. The developed algorithms are a combination of already known plane fitting algorithms and novel approaches. The algorithms perform a plane segmentation of the 3D point cloud and model objects out of the received segments. Each obstacle is defined by a cuboid box and thus can be illustrated easily to the blind person. For plane segmentation different approaches were compared to each other to find the most suitable approach. The first algorithm analyzed in this thesis is a normal vector based plane fitting algorithm. This algorithm supplies very accurate results but also has a high computation effort. The second approach, which was finally implemented, is a gradient based 2D image segmentation combined with a RANSAC plane segmentation (6) in a 3D points cloud. This approach has the advantage to find very small edges within the scene but also builds planes based on global constrains. Beside the development of the algorithm results of the image processing, which are really promising, are presented. Thus the algorithm is worth to be improved by further development. The developed algorithm is able to detect very small but significant obstacles but on the other hand does not represent the scene too detailed such that the result can be illustrated accurately to a blind person.

scholarly journals Obstacle detection using Microsoft Kinect

2021 ◽  
Author(s):  
Niclas Zeller
Keyword(s):  
Image Processing ◽  
Obstacle Detection ◽  
Microsoft Kinect ◽  
Depth Image ◽  
Normal Vector ◽  
Blind Person ◽  
Camera System ◽  
Real Time System ◽  
Plane Fitting ◽  
Kinect Camera

This thesis presents the development of image processing algorithms based on a Microsoft Kinect camera system. The algorithms developed during this thesis are applied on the depth image received from Kinect and are supposed to model a three dimensional object based representation of the recorded scene. The motivation behind this thesis is to develop a system which assists visually impaired people by navigating through unknown environments. The developed system is able to detect obstacles in the recorded scene and to warn about these obstacles. Since the goal of this thesis was not to develop a complete real time system but to invent reliable algorithms solving this task, the algorithms were developed in MATLAB. Additionally a control software was developed by which depth as well as color images can be received from Kinect. The developed algorithms are a combination of already known plane fitting algorithms and novel approaches. The algorithms perform a plane segmentation of the 3D point cloud and model objects out of the received segments. Each obstacle is defined by a cuboid box and thus can be illustrated easily to the blind person. For plane segmentation different approaches were compared to each other to find the most suitable approach. The first algorithm analyzed in this thesis is a normal vector based plane fitting algorithm. This algorithm supplies very accurate results but also has a high computation effort. The second approach, which was finally implemented, is a gradient based 2D image segmentation combined with a RANSAC plane segmentation (6) in a 3D points cloud. This approach has the advantage to find very small edges within the scene but also builds planes based on global constrains. Beside the development of the algorithm results of the image processing, which are really promising, are presented. Thus the algorithm is worth to be improved by further development. The developed algorithm is able to detect very small but significant obstacles but on the other hand does not represent the scene too detailed such that the result can be illustrated accurately to a blind person.

Single Depth View Based Real-Time Reconstruction of Hand-Object Interactions

2021 ◽  
Vol 40 (3) ◽  
pp. 1-12
Author(s):  
Hao Zhang ◽  
Yuxiao Zhou ◽  
Yifei Tian ◽  
Jun-Hai Yong ◽  
Feng Xu
Keyword(s):  
Real Time ◽  
Synthetic Data ◽  
Real Data ◽  
Depth Image ◽  
Real Time System ◽  
The Real ◽  
Time Performance ◽  
Contact Constraint ◽  
Object Shapes ◽  
Object Interactions

Reconstructing hand-object interactions is a challenging task due to strong occlusions and complex motions. This article proposes a real-time system that uses a single depth stream to simultaneously reconstruct hand poses, object shape, and rigid/non-rigid motions. To achieve this, we first train a joint learning network to segment the hand and object in a depth image, and to predict the 3D keypoints of the hand. With most layers shared by the two tasks, computation cost is saved for the real-time performance. A hybrid dataset is constructed here to train the network with real data (to learn real-world distributions) and synthetic data (to cover variations of objects, motions, and viewpoints). Next, the depth of the two targets and the keypoints are used in a uniform optimization to reconstruct the interacting motions. Benefitting from a novel tangential contact constraint, the system not only solves the remaining ambiguities but also keeps the real-time performance. Experiments show that our system handles different hand and object shapes, various interactive motions, and moving cameras.

scholarly journals Depth image correction for intel realsense depth camera

2020 ◽  
Vol 19 (2) ◽  
pp. 1021
Author(s):  
Hyun Jun Park ◽  
Kwang Baek Kim
Keyword(s):  
Image Processing ◽  
Infrared Radiation ◽  
Infrared Camera ◽  
High Accuracy ◽  
Depth Image ◽  
Depth Information ◽  
Depth Camera ◽  
Image Correction ◽  
Image Processing Techniques ◽  
Processing Techniques

<p><span>Intel RealSense depth camera provides depth image using infrared projector and infrared camera. Using infrared radiation makes it possible to measure the depth with high accuracy, but the shadow of infrared radiation makes depth unmeasured regions. Intel RealSense SDK provides a postprocessing algorithm to correct it. However, this algorithm is not enough to be used and needs to be improved. Therefore, we propose a method to correct the depth image using image processing techniques. The proposed method corrects the depth using the adjacent depth information. Experimental results showed that the proposed method corrects the depth image more accurately than the Intel RealSense SDK.</span></p>

Image Processing To Aid in Graphene Detection

2014 ◽  
Vol 1702 ◽  
Author(s):  
Ryan D. Gorby ◽  
Lihong (Heidi) Jiao
Keyword(s):  
Image Processing ◽  
Single Layer ◽  
Optical Microscope ◽  
Single Layer Graphene ◽  
Camera System ◽  
Layer Graphene ◽  
Sophisticated Equipment ◽  
Processing Software ◽  
Image Processing Software ◽  
Few Layer Graphene

ABSTRACTQualitative techniques for the detection of graphene on a Si/SiO2 substrate, without the use of sophisticated equipment, are presented. Once calibrated, this technique can be used to detect Single Layer Graphene (SLG) and Few Layer Graphene (FLG) with the use of an inexpensive optical microscope (OM), OM camera system, and image processing software. This technique could be transferred to graphene deposited on other substrates or other 2-D materials with minor updates to mathematical theory.

Multi-View RGB-D Synchronized Video Acquisition and Temporally Coherent 3D Animation Reconstruction Using Multiple Kinects

2017 ◽  
pp. 142-163 ◽  
Author(s):  
Naveed Ahmed
Keyword(s):  
Coordinate System ◽  
Cost Effective ◽  
Video Data ◽  
Microsoft Kinect ◽  
Global Coordinate System ◽  
3D Animation ◽  
Dynamic Scene ◽  
Camera System ◽  
Video Acquisition ◽  
3D Geometry

This chapter introduces a system for acquiring synchronized multi-view color and depth (RGB-D) video data using multiple off-the-shelf Microsoft Kinect and methods for reconstructing temporally coherent 3D animation from the multi-view RGB-D video data. The acquisition system is very cost-effective and provides a complete software-based synchronization of the camera system. It is shown that the data acquired by this framework can be registered in a global coordinate system and then can be used to reconstruct the 360-degree 3D animation of a dynamic scene. In addition, a number of algorithms to reconstruct a temporally-coherent representation of a 3D animation without using any template model or a-prior assumption about the underlying surface are also presented. It is shown that despite some limitations imposed by the hardware for the synchronous acquisition of the data, a reasonably accurate reconstruction of the animated 3D geometry can be obtained that can be used in a number of applications.

Vojta-Therapy

2021 ◽  
pp. 383-398
Author(s):  
Muhammad Hassan Khan ◽  
Marcin Grzegorzek
Keyword(s):  
Template Matching ◽  
Movement Pattern ◽  
Microsoft Kinect ◽  
Support Vector ◽  
Therapy Process ◽  
Body Parts ◽  
External Stimulation ◽  
Kinect Camera ◽  
Specific Stimulation ◽  
The Given

This paper proposed a novel computer vision-based framework to recognize the accurate movements of a patient during the Vojta-therapy. Vojta-therapy is a useful technique for the physical and mental impairments in humans. During the therapy, a specific stimulation is given to the patients to cause the patient's body to perform certain reflexive pattern movements. The repetition of this stimulation ultimately makes available the previously blocked connections between the spinal cord and brain, and after a few sessions, patients can perform these movements without any external stimulation. In this paper the authors propose an automatic method for patient detection and recognition of specific movements in his/her various body parts during the therapy process, using Microsoft Kinect camera. The proposed method works in three steps. In the first step, a robust template matching based algorithm is exploited for patient's detection using his/her head location. Second, several features are computed to capture the movements of different body parts during the therapy process. Third, in the classification stage, a multi-class support vector machine (mSVM) is used to classify the accurate movements of patient. The classification results ultimately reveal the correctness of the given treatment. The proposed algorithm is evaluated on the authors' challenging dataset, which was collected in a children hospital. The detection and classification results show that the proposed method is highly useful to recognize the correct movement pattern either in hospital or in-home therapy systems.

Body Recognition Based on Depth Image

2014 ◽  
Vol 631-632 ◽  
pp. 414-417
Author(s):  
Jian Zhang ◽  
Wan Juan Song
Keyword(s):  
Pattern Recognition ◽  
Image Recognition ◽  
The Body ◽  
Depth Image ◽  
Body Parts ◽  
Research Status ◽  
Kinect Camera ◽  
Local Gradient ◽  
Body Recognition

The text introduces the research status of depth image in the pattern recognition and the application in the body recognition. Aiming at the problem that the image recognition shot by common camera has declined performance under the factors of illumination, posture, shielding, and the like, the body parts are distinguished and judged by taking Kinect equipment promoted by Microsoft as the platform, analyzing the features of the depth picture obtained by the Kinect camera and putting forwards to the local gradient features of comprehensive point features and the gradient features; and the elbow is taken as the example to argue simply .

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