Object Extraction Using Probabilistic Maps of Color, Depth, and Near-Infrared Information
DOI:
https://doi.org/10.12962/j25796216.v4.i1.106Abstrak
Object extraction is one of the important and chal-lenging tasks in the computer vision and/or robotics ? elds.This task is to extract the object from the scene using anypossible cues. The scenario discussed in this study was the objectextraction which considering the Space of Interest (SOI), i.e.,the three dimensional area where the object probably existed.To complete such task, the object extraction method based onthe probabilistic maps of multiple cues was proposed. Thanksto the Kinect V2 sensor, multiple cues such as color, depth, andnear-infrared information can be acquired simultaneously. TheSOI was modeled by a simple probabilistic model by consideringthe geometry of the possible objects and the reachability of thesystem acquired from depth information. To model the color andnear-infrared information, a Gaussian mixture models (GMM)was used. All of the models were combined to generate theprobabilistic maps that were used to extract the object fromthe scene. To validate the proposed object extraction, severalexperiments were conducted to investigate the best combinationof the cues used in this study.Keywords: color information, depth information, near-infrared information, object extraction, probabilistic maps.Referensi
Girshick R, Donahue J, Darrell T, Malik J. Rich feature hierarchies for
accurate object detection and semantic segmentation. IEEE Conference
on Computer Vision and Pattern Recognition. Columbus. 2014;580587.
Okuma K, Taleghani A, de Freitas N, Little JJ, Lowe DG. A Boosted
Particle Filter: Multitarget Detection and Tracking. 2004;2839.
Obata M, Nishida T, Miyagawa H, Ohkawa F. Target tracking and
posture estimation of 3d objects by using particle filter and para-
metric eigenspace method. Proc of Comput Vis, & Pattern Recognit.
;6772.
Muhammad Attamimi, Takaya Araki, Tomoaki Nakamura, and
Takayuki Nagai. Visual Recognition System for Cleaning Tasks by
Humanoid Robots. International Journal of Advanced Robotic Systems:
Humanoid, 2013;114.
Attamimi, M., Nagai, T., Purwanto, D. Particle filter with inte-
grated multiple features for object detection and tracking. Telkomnika
(Telecommunication Computing Electronics and Control).
Muhammad Attamimi, Akira Mizutani, Tomoaki Nakamura, Takayuki
Nagai, Kotaro Funakoshi, Mikio Nakano. Real-time 3D visual sensor
for robust object recognition. IROS Taipei. 2010;45604565.
Muhammad Attamimi, Tomoaki Nakamura, and Takayuki Nagai. Hi-
erarchical Multilevel Object Recognition Using Markov Model. Pro-
ceedings of the 21st International Conference on Pattern Recognition.
Kinect for Xbox One: https://en.wikipedia.org/wiki/Kinect.
Salamati, N, Fredembach, C, Ssstrunk, S. Material Classification Using
colour and NIR Images. IST 17th Colour Imaging Conf. 2009;216222.
Mokhtar M. Hasan, Pramod K. Mishra. Superior Skin Color Model
using Multiple of Gaussian Mixture Model. British Journal of Science.
; 6(1): 114.
Stephen J. Krotosky and Mohan M. Trivedi. A Comparison of Color and
Infrared Stereo Approaches to Pedestrian Detection. IEEE Intelligent
Vehicles Symposium, 2007;8186.
Ren S, He K, Girshick R, Sun J. Faster R-CNN: Towards real-time
object detection with region proposal networks. Adv Neural Inf Process
Syst. 2015;9199.
Dong W, Wang Y, Jing W, Peng T. An Improved Gaussian Mix-
ture Model Method for Moving Object Detection. TELKOMNIKA
(Telecommunication Comput Electron Control). 2016;14(3A):115.
Sumardi, Taufiqurrahman M, Riyadi MA. Street mark detection using
raspberry pi for self-driving system. Telkomnika (Telecommunication
Comput Electron Control). 2018;16(2):62934.
N. Salamati and S. Ssstrunk. Material-Based Object Segmentation Using
Near-Infrared Information.
Lai K, Bo L, Ren X, Fox D. A Large-Scale Hierarchical Multi-view
RGB-D Object Dataset. Int. Conf. on ICRA. 2011;18171824.
Kai Welke, Jan Issac, David Schiebener, Tamim Asfour, Rdiger Dill-
mann. Autonomous acquisition of visual multi-view object representa-
tions for object recognition on a humanoid robot. Int. Conf. on ICRA.
;20122019.
Kinect for Xbox 360: https://en.wikipedia.org/wiki/Kinect.
Alex Krizhevsky. ImageNet Classification with Deep Convolutional
Neural Networks. NIPS. 2012.
Mansib Rahman. Beginning Microsoft Kinect for Windows SDK 2.0:
Motion and Depth Sensing for Natural User Interfaces. Apress Berkely,
CA, USA 2017.
Muhammad Attamimi and Takayuki Nagai. A Visual Sensor for Do-
mestic Service Robots. Journal on Advanced Research in Electrical
Engineering. 2018; 2(1):3136.
Conrad Sanderson, Ryan Curtin. An Open Source C++ Implementation
of Multi-Threaded Gaussian Mixture Models, k-Means and Expectation
Maximisation. In Proc. of International Conference on Signal Process-
ing and Communication Systems. 2017.
Everingham M, Gool L, Williams C. K, Winn J, and Zisserman A. The
Pascal Visual Object Classes (VOC) Challenge. Int. J. of Computer
Vision. 2010; 88(2): 303338.
Rasmussen C. E. The Infinite Gaussian Mixture Model. In Advances
in Neural Information Processing Systems. 2000;554560.
