i-manager Publications

Intelligent and Fast Object Detection [FOD] Using Object Properties

D. Sasireka*, D. Jeyabharathi**, D. Kesavaraja***, A. Suthan****

* Assistant Professor, Jayamatha Engineering College, Aralvaimozhi, India.

** Research Scholar, Anna University Regional Centre, Tirunelveli, India.

***_**** Assistant Professor, Dr.Sivanthi Aditanar College of Engineering, Tiruchendur, India.

Periodicity:March - May'2015
DOI : https://doi.org/10.26634/jit.4.2.3389

Abstract

Object identification plays a critical role in security inspection such as luggage, parcel and cargo inspection against object detection under a risky environment. This system automates this process by analyzing scanned the luggage or parcel images. For analyzing the image for object identification it uses edge and structure information of the object. This system uses reference object model which is represented using Prevailing Color Structure Descriptor(PCSD) for identifying object from scanned color images. The Prevailing Color Structure Descriptor(PCSD) is based on RGB-SIFT descriptor which is invariant in various lighting conditions and different orientation of object. This research scheme gives the high end processing in the field of Object Recognition because this technique focuses on RGB SIFT along with an Image Segmentation Scheme. In this method for recognizing the object both color and structure properties of object used, for representing the object geometric transformation as well as color information used. So this object identification robust, accurate and invariant against lighting condition and affine transformation. This would be more helpful for object detection in Airport and Defense Environment. This type recognition will support both Color and Gray Scale in all formats like JPG, BMP and GIF. In this FOD is tested against several images and its results are marked. This gives 91.22% average precision accuracy of detecting objects.

Keywords

Object Identification, PCSD, RGB-SIFT, Color and Structure Properties Object Detection

How to Cite this Article?

Sasireka. D, Jeyabharathi. D, Kesavaraja. D and Suthan. A (2015). Intelligent and Fast Object Detection [FOD] Using Object Properties. i-manager’s Journal on Information Technology, 4(2), 19-26. https://doi.org/10.26634/jit.4.2.3389

References

[1]. KoenE.A.Van de Sande, Theo givers, CeesG.M.Snoek (2010). “Evaluating Color Descriptor for Object and Scene Recognition” IEEE transaction on Pattern Analysis and Machine intelligence, Vol 32, No. 9.

[2]. Sarif Kumar Naik, C.A. Moorthy (2007). “Distinct Multicolored Region Descriptors for Object Recognition”, IEEE transaction on Pattern Analysis and Machine intelligence, Vol 29, No. 7.

[3]. Yining Deng, B.S. Manjunath, Charles Kenney, Michael S. Moore (2001) . “An efficient color representation for image retrival” IEEE transaction on Image Processing, Vol 10, No. 1.

[4]. Ka-Man Wong, Lai Man Po and Kwok – Wai Cheung “A Compact and efficient Color Descriptor for Image Retrival”.

[5]. Moncef Gabbouj, MuratBirinci and SerkanKiranyaz “Perceptual Color Descriptor based on a spatial Distribution Model : PROXIMITY HISTOGRAM”.

[6]. James Annesley, James Orwell, John Paul renno “Evaluation of MPEG -7 Color Descriptors for Visual Survillence Retrival”

[7]. P.F. Forssen (2007). “Maximally Stable Color Region for Recognition and Matching” Proc,IEEE conf. Computer Vision and pattern Recognition.

[8]. R. Datta, D. Joshi, J. Li, and J.Z. Wang, (2008). “Image Retrieval: Ideas, Influences, and Trends of the New Age,” ACM Computing Surveys, Vol. 40, No. 2, pp. 1-60.

[9]. R. Fergus, F.-F. Li, P. Perona, and A. Zisserman, (2005). “Learning Object Categories from Google's Image Search,” Proc. IEEE Int'l Conf. Computer Vision, pp. 1816- 1823.

[10]. S. Lazebnik, C. Schmid, and J. Ponce, (2006). “Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, Vol. 2, pp.2169-2178.

[11]. J. Vogel and B. Schiele, (2007). “Semantic Modeling of Natural Scenes for Content-Based Image Retrieval,” Int'l J. Computer Vision, Vol. 72, No. 2, pp. 133-157.

[12]. J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid, (2007). “local features and kernels for classification of Texture and Object Categories: A Comprehensive Study,” Int'l J. Computer Vision, Vol. 73, No. 2, pp. 213-238.

[13]. S.-F. Chang, D. Ellis, W. Jiang, K. Lee, A. Yanagawa, A.C. Loui,and J. Luo, (2007). “Large-Scale Multimodal Semantic Concept Detection for Consumer Video,” Proc. ACM Int'l Workshop Multimedia Information Retrieval, pp. 255-264.

[14]. A.F. Smeaton, P. Over, and W. Kraaij, (2006). “Evaluation Campaigns and TREC Vid,” Proc. ACM Int'l Workshop Multimedia Information Retrieval, pp. 321-330.

[15]. Y.-G. Jiang, C.-W. Ngo, and J. Yang, “Towards Optimal Bag of-Features for Object Categorization and Semantic Video Retrieval,” Proc. ACM Int'l Conf. Image and Video Retrieval.

[16]. http://en.wikipedia.org/wiki/K-means_clustering

[17]. Chi-Hung Chuang; Hsieh, Jun-Wei; Luo-Wei Tsai; Pei- Shiuan Ju; Kao-Chin Fan, (2008). "object detection using fuzzy-color histogram," Circuits and Systems, 2008. ISCAS 2008. IEEE International Symposium, Vol. 3546, No. 3549, pp. 18-21.

[18]. Chi-Hung Chuang; Hsieh, Jun-Wei; Kao-Chin Fan, (2007). "Object Detection and Robbery Event Analysis," Computer Communications and Networks, ICCCN. Proceedings of 16th International Conference on, Vol. 1189, No. 1192, pp. 13-16.

[19]. Yasushi Makihara,Masao Takizawa,Yoshiaki Shirai,Nobutaka Shimada, (2003). "Object Recognition under Various Lighting Conditions" Image Analysis Lecture Notes in Computer Science Vol. 2749, pp 899-906.

[20]. Che-Ming Hu www.disp.ee.ntu.edu.tw/meeting/ Edge Detection/Edge detection.doc "Edge Detection"

[21]. Soo-Chang Pei, Jian-Jiun Ding, (2005). ” Imporved Harris' Algotithm For Corner And Edge Detections”, Vol 1.

[22]. William K. Pratt, “Digital Image Processing_William K. Pratt 3rd ”, chapter5.

[23]. Jiun-De Huang, “Image Compression by Segmentation and Boundary Description”, chapter 2.

[24]. D. Ziou & S. Tabbone, (1998). “Edge Detection Techniques. An Overview”.

[25]. J. Canny, “A Computational Approach to Edge Detection,” IEEE Trans. Pattern.

[26]. L.N. Kanal, H. Kitano, V. Kumar. (1986), “Parallel Processing for Artificial Intelligence”, Analysis and Machine Intelligence, Vol. 8, No, 6, November 1986, pp.679–698.

[27]. Maar, D., Hildreth E., (1980). “Theory of edge detection”, Proceedings Royal Soc. London, Vol. 207 pp. 187-217.

[28]. Advanced Edge Detection Techniques: Techniques in Computational Vision: http://www.cpsc.ucalgary.ca/ Research/vision/501/edgedetect.pdf

[29]. P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan. (2010). Object detection with discriminatively trained part-based models. TPAMI, Vol. 32, No. 9, pp. 1627–1645.

[30]. P. Felzenszwalb, R. Girshick, and D. McAllester. Discriminatively trained deformable part models, release 4. ”http://people.cs.uchicago.edu/ pff/latent- release4/”.

[31]. R. Girshick, P. Felzenszwalb, and D. McAllester. Discriminatively trained deformable part models, release 5. ”http://people.cs.uchicago.edu/ rbg/latentrelease5/”.

[32]. L. Zhu, Y. Chen, A. Yuille, and W. Freeman. (2010). Latent hierarchical structural learning for object detection. In CVPR.

Intelligent and Fast Object Detection [FOD] Using Object Properties

Abstract

Keywords

How to Cite this Article?

References

If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Options for accessing this content:

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Pdf	35	35	200	20
Online	35	35	200	15
Pdf & Online	35	35	400	25