3D Object Detection Using a New Descriptor with RGB-D

Öz

Object detection is a very important study area in computer vision. Many research use only RGB images to find objects. In our work, we present new descriptor for object detection using RGB-D’s Depth image data. We combine RGB image with depth image to create new feature vector. The introduced features feeds Bag of Visual Words algorithm to classify images of the objects. Result shows us to RGB-D images are given better accuracy results to comparing with RGB image. 

Anahtar Kelimeler

• RGB-D,Depth Image,Machine Learning,K-Means,Bag of Visual Words

Kaynakça

1. Arıcan E, Aydın T (2017) Object Detection With RGB-D Data Using Depth Oriented Gradients. In: Book of Proceedings - International Conference on Engineering and Natural Sciences
2. Bay H, Tuytelaars T, Van Gool L (2006) SURF: Speeded up robust features. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp 404–417
3. Calonder M, Lepetit V, Strecha C, Fua P (2010) BRIEF: Binary robust independent elementary features. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 6314 LNCS:778–792. doi: 10.1007/978-3-642-15561-1_56
4. Csurka G, Dance C, Fan L, et al (2004) Visual categorization with bag of keypoints. Int Work Stat Learn Comput Vis. doi: 10.1234/12345678
5. Huang J, You S (2012) Point cloud matching based on 3D self-similarity. In: Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on. pp 41–48
6. Intel Intel RealSense. https://www.intel.com
7. Janoch A, Karayev S, Jia Y, et al (2011) A category-level 3-D object dataset: Putting the Kinect to work. Proc IEEE Int Conf Comput Vis 1168–1174. doi: 10.1109/ICCVW.2011.6130382
8. Lai K, Bo L, Ren X, Fox D (2011) A large-scale hierarchical multi-view RGB-D object dataset. In: Proceedings - IEEE International Conference on Robotics and Automation. pp 1817–1824

9. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110. doi: 10.1023/B:VISI.0000029664.99615.94
10. MATLAB MATLAB. https://www.mathworks.com/products/matlab.html
11. Microsoft Kinect. https://dev.windows.com/en-us/kinect
12. Rublee E, Rabaud V, Konolige K, Bradski G (2011) ORB: An efficient alternative to SIFT or SURF. Proc IEEE Int Conf Comput Vis 2564–2571. doi: 10.1109/ICCV.2011.6126544
13. Shechtman E, Irani M (2007) Matching local self-similarities across images and videos. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, pp 1–8
14. Silberman N, Hoiem D, Kohli P, Fergus R (2012) Indoor segmentation and support inference from RGBD images. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 7576 LNCS:746–760. doi: 10.1007/978-3-642-33715-4_54
15. Xiao J, Owens A, Torralba A (2013) SUN3D: A database of big spaces reconstructed using SfM and object labels. Proc IEEE Int Conf Comput Vis 1625–1632. doi: 10.1109/ICCV.2013.458