Total views : 276

Extracting Objects of Interest based on Three Dimensional Feature


  • Department of Digital Media, Anyang University, 22, 37-Beongil, Samdeok-Ro, Manan-Gu, Anyang 430-714, Korea, Republic of


Objectives: This paper proposes a technique of segmenting a target object more robustly by combining and clustering 2-dimensional and 3-dimensional features of 3D stereoscopic images coming sequentially. Methods/Statistical analysis: The proposed object detection technique first applies a disparity computation algorithm to the left and right stereo input images that are captured to extract the depth values representing the distance between a camera and a target object by each image pixel. The technique then effectively clusters color and depth features using feature similarity. Subsequently, the method excludes the background area from the image and finally detects an object of interest. Findings: The experimental results of this paper show that the proposed 3-dimensional feature-based target detection technique extracted objects more robustly than other existing object detection methods. To evaluate the performance of the proposed algorithm of detecting an object of interest, this paper uses various types of indoor and outdoor input images without any particular constraints. To compare the performance of the suggested object segmentation method, this paper defined root mean square error measure. The measure which is the scale to deal with general and particular viewpoints of image quality is often used to measure the difference between an actual value and a measured value. It is known as a good scale tool for accuracy measurement. The two existing methods use 2D features only to segment an object of interest, and thereby include many false positive errors, whereas the proposed method effectively clusters the 3-dimensional distance feature as well as 2-dimensional feature so that it segments an object of interest more accurately than the other two methods in terms of quantitative aspect. Improvements/Applications: It is expected that the proposed technique of detecting an object of interest will be used in various types of actual application areas related to multimedia contents.


Clustering, Color model, Feature, Multimedia content, Objects of Interest, Segmentation.

Full Text:

 |  (PDF views: 212)


  • Kang S, Lee K, Lee K. Context-aware abnormality monitoring service for care-needing persons using a probabilistic model. Indian Journal of Science and Technology. 2016 Jun; 9(24):1–8.
  • Sundaravadivu K, Sadeesh kumar A, Devi M. Segmentation of noise stained gray scale images with Otsu and firefly algorithm. Indian Journal of Science and Technology. 2016 Jun; 9(22):1–6.
  • Wang H, Wang T. Primary object discovery and segmentation in videos via graph-based transductive inference. Computer Vision and Image Understanding. 2016 Feb; 143:159–72.
  • Li Z, Liu G, Zhang D, Xu Y. Robust single-object image segmentation based on salient transition region. Pattern Recognition. 2016 Apr; 52:317–31.
  • Lee S, Kim N, Jeongn K, Paek I, Hong H, Paik J. Multiple moving object segmentation using motion orientation histogram in adaptively partitioned blocks for high-resolution video surveillance systems. Optik - International Journal for Light and Electron Optics. 2015 Oct; 126(19):2063–69.
  • Yang J, He Y, Caspersen J, Jones T. A discrepancy measure for segmentation evaluation from the perspective of object recognition. ISPRS Journal of Photogrammetry and Remote Sensing. 2015 Mar; 101:186–192.
  • Zhao X, Satoh Y, Takauji H, Kaneko S, Iwata K, Ozaki R. Object detection based on a robust and accurate statistical multi-point-pair model. Pattern Recognition. 2011 Jun; 44(6):1296–311.
  • Yang X, Liu H, Latecki LJ. Contour-based object detection as dominant set computation. Pattern Recognition. 2012 Mar; 45(5):1927–36.
  • Kim SY. Dynamic modeling of eigen background for object tracking. Journal of the Korea Society of Computer and Information. 2012; 17(4):67–74.
  • Huang K, Wang L, Tan T, Maybank S. A real-time object detecting and tracking system for outdoor night surveillance. Pattern Recognition. 2008 Jan; 41(1):432–44.
  • Lee WS, Kim HH, Cho YG. Passenger monitoring method using optical flow and difference image. Proceedings of the Fall Conference of the Korean Society for Railway, Korea. 2010. p. 1966–72.
  • Singh SPN, Csonka PJ, Waldron KJ. Optical flow aided motion estimation for legged locomotion. Proceedings of IEEE International Conference on Intelligent Robots and Systems, Korea. 2006. p. 1738–43.
  • Moeini A, Faez K, Sadeghi H, Moeini H. 2D facial expression recognition via 3D reconstruction and feature fusion. Journal of Visual Communication and Image Representation. 2016 Feb; 35:1–14.
  • Xiao J, Feng Y, Ji M, Zhuang Y. Fast view-based 3D model retrieval via unsupervised multiple feature fusion and online projection learning. Signal Processing. 2016 Mar; 120:702–13.
  • Weinmann M, Urban S, Hinz S, Jutzi B, Mallet C. Distinctive 2D and 3D features for automated large-scale scene analysis in urban areas. Computers and Graphics. 2015 Jun; 49:47–57.
  • Baha N, Larabi S. Accurate real-time neural disparity MAP estimation with FPGA, Pattern Recognition. 2012 Mar; 45(3):1195–204.
  • Jung C, Chen X, Cai J, Lei H, Yun I, Kim J. Boundary-preserving stereo matching with certain region detection and adaptive disparity adjustment. Journal of Visual Communication and Image Representation. 2015 Nov; 33:1–9.
  • Ploumpis S, Amanatiadis A, Gasteratos A. A stereo matching approach based on particle filters and scattered control landmarks. Image and Vision Computing. 2015 Jun; 38:13–23.
  • Jama A, Rakshit S. Augmenting graph cut with TV-L approach for robust stereo matching. Proceedings of International Conference on Image Information Processing. 2011. p. 1–6.
  • Zagrouba E, Gamra S B, Najjar A. Model-based graph-cut method for automatic flower segmentation with spatial constraints. Image and Vision Computing. 2014 Dec; 32(12):1007–20.
  • Dong E, Zheng Q, Sun W, Li Z, Li L. Constrained multiplicative graph cuts based active contour model for magnetic resonance brain image series segmentation. Signal Processing. 2014 Nov; 104: 59–69.
  • Jang SW, Park YJ, Kim GY, Choi HI, Hong MC. An adult image identification system based on robust skin segmentation. Journal of Imaging Science and Technology. 2011 Mar; 55(2):20508-1- 020508-10.
  • Chen YK, Chen KJ. Pavement detection in YCbCr color space and its application. Proceedings of IEEE International Conference on Information and Automation, Honlulu. 2015. p. 663–67.
  • Jose-Garcia A, Gomez-Flores W. Automatic clustering using nature-inspired met heuristics: a survey. Applied Soft Computing. 2016 Apr; 41:192–213.
  • Yang MS, Chang-Chien SJ, Hung WL. An unsupervised clustering algorithm for data on the unit hyper sphere. Applied Soft Computing. 2016 May; 42:290–313.
  • Chen JY, He HH. A fast density-based data stream clustering algorithm with cluster centers self-determined for mixed data. Information Science. 2016 Jun; 345:271–93.
  • Ravi TV, Gowda KC. An ISODATA clustering procedure for symbolic objects using a distributed genetic algorithm. Pattern Recognition Letters. 1999 Jul; 20(7):659–66.
  • Ma Y, Tan Z, Chang G, Wang X. New P2P network routing algorithm based on ISODATA clustering topology. Procedia Engineering. 2011; 15:2966–70.
  • Sugano H, Miyamoto R. Highly optimized implementation of Open CV for the cell broadband engine. Computer Vision and Image Understanding. 2010 Nov; 114(11):1273–81.
  • Ibrahim MZ, Mulvaney DJ. Geometrical-based lip-reading using template probabilistic multi-dimension dynamic time warping. Journal of Visual Communication and Image Representation. 2015 Jul; 30:219–33.
  • Hashmi MF, Shukla RJ, Keskar AG. Real time copyright protection and implementation of image and video processing on Android and embedded platforms. Procedia Computer Science. 2015; 46: 1626–34.
  • Liu GJ, Tang XL, Cheng HD, Huang JH, Liu JF. A novel approach for tracking high speed skaters in sports using a panning camera. Pattern Recognition. 2009 Nov; 42(11):2922–35.
  • Zollanvari A, Dougherty ER. Moments and root-mean-square error of the Bayesian MMSE estimator of classification error in the Gaussian model. Pattern Recognition. 2014 Jun; 47(6):2178–92.


  • There are currently no refbacks.

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