Object Detection and Comparison of Different Shapes and Materials using Kinect

Nancy Velasco, David Rivas L., Eddie E. Galarza

Abstract


This paper presents an algorithm for object detection and an evaluation of response with different shapes and materials using Kinect sensor. In order to develop this work, a new icon is done using LabVIEW. The depth image of the Kinect is processed by Artificial Vision Toolkit to indicate the distance to each object. Additionally, the application has an audio output in English and Spanish indicating whether an object is in the trajectory. Several tests were done, through which the performance of the proposal was verified.

Keywords


Kinect; Object Detection; LabVIEW Vision Toolkit; Microsoft Kinect SDK;

Full Text:

PDF

References


Uijlings, J. RR, 2013. et al. Selective Search for Object Recognition. International Journal of Computer Vision, 104(2):154-171.

Oji, R. 2012. An Automatic Algorithm for Object Recognition and Detection Based on ASIFT keypoints. Preprint arXiv:1211.5829.

Viola, P., Jones, M.. 2001. Rapid Object Detection using a Boosted Cascade of Simple Features. Proceedings of the 2001 IEEE Computer Society Conference on In Computer Vision and Pattern Recognition CVPR2001. IEEE, 1:I-511-I-518

Papageorgiou, C., Poggio, T. 2000. A trainable system for object detection. International Journal of Computer Vision, 38(1):15-33.

Iralde L. I. 2012. Desarrollo de Aplicaciones Con MicrosoftKinect. Pamplona.

Rakprayoon P., Ruchanurucks M., Coundoul A. 2011, Kinect-based Obstacle Detection for Manipulator, SI International, 68-73,

Xia, L. C., Chia-C. Aggarwal, Jake K. 2011. Human Detection using Depth Information by Kinect. 2011 IEEE Computer Society Conference onComputer Vision and Pattern Recognition Workshops (CVPRW).15-22.

Riyad A. El-I., Jidong Huang, Michael Yeh. 2012. Study on the Use of Microsoft Kinect for Robotics Applications. IEEE/ION Position Location and Navigation Symposium (PLANS). 1280 - 1288

Ekelmann J., Butka B. 2012. Kinect Controlled Electro-Mechanical Skeleton. Mechanical and Electrical Engineering Departments EmbryRiddle Aeronautical University, FL USA.

Ganganath N. and Leung H. 2012. Mobile Robot Localization using Odometry and Kinect Sensor. Department of Electrical and Computer Engineering Schulich School of Engineering. 91-94.

Biswas K. K., Basu, S. K. 2011. Gesture Recognition using Microsoft Kinect®, Conf. on Automation, Robotics and Application. 100-103.

Abramov, A., Pauwels, K., Papon, J., Wörgötter, F., & Dellen, B. 2012. Depth-supported Real-Time Video Segmentation with the Kinect. In IEEE Workshop on Applications of Computer Vision 2012. 457-464.

Lange, B., Koenig, S., McConnell, E., Chang, C. Y., Juang, R., Suma, E., & Rizzo, A. 2012. Interactive Game-Based Rehabilitation using the Microsoft Kinect. In 2012 IEEE Virtual Reality Short Papers and Posters (VRW). 171-172.

Hui-mei Justina Hsu, 2011. The Potential of Kinect in Education. Int. Journal of Information and Education Technology, 365-370.

Microsoft Kinect SDK. [Online] From:

http://msdn.microsoft.com/en-us/library/hh973078.as px#Depth_Ranges. [Acessed on 2 Octubre 2015].

Kinect SDK v1.0. [Online] From:

https://decibel.ni.com/content/servlet/JiveServlet/download/2253-3-47391/Kinect%20SDK%20v1.0.zip. [Acessed on 10 July 2015].

Lee, J.-S.. 1983. Digital Image Smoothing and the Sigma Filter. Computer Vision, Graphics, and Image Processing, 24(2), 255-269.

Text to speech. [Online] From:

https://decibel.ni.com/content/docs/DOC-2263. [Acessed on 10

December 2015].


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

ISSN: 2180-1843

eISSN: 2289-8131