iosr-JECE   Volume-8 ~ Issue-6

Abstract: Nondestructive quality evaluation of fruits is important and very vital for the food and agricultural industry. The fruits in the market should satisfy the consumer preferences. Traditionally grading of fruits is performed primarily by visual inspection using size as a particular quality attribute. Image processing offers solution for automated fruit size grading to provide accurate, reliable, consistent and quantitative information apart from handling large volumes, which may not be achieved by employing human graders. This paper presents a fruit size detecting and grading system based on image processing. The early assessment of fruit quality requires new tools for size and color measurement. After capturing the fruit side view image, some fruit characters is extracted by using detecting algorithms. According to these characters, grading is realized. Experiments show that this embedded grading system has the advantage of high accuracy of grading, high speed and low cost. It will have a good prospect of application in fruit quality detecting and grading areas.
Keywords: embedded system, size detecting, fruit grading, image processing.

[1] Hongshe Dang, Jinguo Song, Qin Guo, "A Fruit Size Detecting and Grading System Based on Image Processing," 2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics,pp83-86.

[2] Harshavardhan G. Naganur, Sanjeev S. Sannakki, Vijay SRajpurohit, Arunkumar R, "Fruits Sorting and Grading usingFuzzy Logic," International Journal of Advanced Research inComputer Engineering & Technology (IJARCET) Volume 1,Issue 6, August 2012,pp 117-122.

[3] John B. Njoroge. Kazunori Ninomiya. Naoshi Kondo andHideki Toita, "Automated Fruit Grading System using ImageProcessing," The Society of Instrument and Control Engineers(SICE2002), Osaka, Japan, August 2002, pp 1346-1351.

[4] J. V. Frances, J. Calpe, E. Soria, M. Martinez, A. Rosado, A.J.Serrano, J. Calleja, M. Diaz, "Application of ARMA modeling to the improvement of weight estimations in fruit sorting and grading machinery," IEEE 2000, pp 3666-3669.

[5] Wong Bing Yit, Nur Badariah Ahmad Mustafa, ZaipatimahAli, Syed Khaleel Ahmed, Zainul Abidin Md Sharrif, "Design and Development of a Fully Automated Consumer-based Wireless Communication System For Fruit Grading", ISCIT 2009 , pp 364-369. [6] Naoshi Kondo, "Fruit Grading Robot", Proceedings of the2003 IEEE/ASME International Conference on AdvancedIntelligent Mechatronics (AIM 2003), pp 1366-1371.

[7] R. C. Gonzalez, R. E. Woods," Digital Image Processing", Pearson Education.IIEd., 2002.

Abstract: A novel algorithm for detecting and tracing extended target using projection curves analysis and correlation tracking based on the maximum matching pixel count (MPC) criterion is presented. First, the projection curves of the difference image of two consecutive frames are analyzed to find the approximate areas of moving target on the entire scenes. Then correlation tracking based on the improved MPC criterion is used for target tracking against the cluttered background. Experimental results show, as compared to the conventional approaches, the proposed algorithm is more robust, has higher precision, and has simplified computational complexity for tracking an extended target against a cluttered background.
Keywords: MAD, MPC, NCC, MSE, CTA

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Abstract: This paper focuses on the early detection problem of multiple moving targets in statistical MIMO radar systems using TBD techniques. At first, assuming prior knowledge of the number of targets, a binary generalized likelihood ratio test (GLRT) is derived, which shows that the optimal implementation of the GLRT requires multidimensional joint search. To reduce the implementation complexity, a suboptimum multitarget TBD algorithm using successive-target cancellation and polar Hough transform (STC-PHT) is proposed. In addition to low complexity, the new proposed algorithm doesn't need the prior information of the number of targets, and can avoid the implementation of multi-hypothesis test when the number of targets is unknown.
Keywords: Antenna, Detect, Processor, Noise, Target, Threshold, Track, Velocity.

[1] LuzhouXu, PetreStoice, Jain Li "Optimal joint target detection and parameter estimation by MIMO radar," IEEE Transaction on Signal Processing, vol.23, pp. 143-1-4244-4567, May 2011.

[2] Huang Yong, Guan Jian "A Track-Before-Detect Algorithm for Statistical MIMO Radar Multitarget Detection," IEEE Transaction on Signal Processing, vol.15, pp. 978-1-4244-5812, October 2010.

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[4] C. Kabakchiev, I. Garvanov, L. Doukovska, V. Kyovtorov, and H. Rohling, "Data association algorithm in multiradar system," In Proc.2008 IEEE International Radar Conference, Rome, IEEE AESS, pp. 1- 4, 2008.

[5] C. Kabakchiev, I. Garvanov, L. Doukovska, V. Kyovtorov, and H Rohling, "Data association algorithm in TBD multiradar system," In Proc. 2007 International Radar Symposium, Cologne, GIN, pp. 521- 525, 2007.

[6] A. K. Shackelford, K. Gerlach, S. D. Blunt, "Partially adaptive STAP using the FRACTA algorithm," IEEE Transactions on Aerospace and Electronic Systems, vol. 45, no. 1, pp.58-69, 2009.

[7] J. K. Wolf, A. M. Viterbi, G. S. Dixon, "Finding the best set of K paths through a trellis with application to multitarget tracking," IEEE Transactions on Aerospace and Electronic Systems, vol. 25, no. 2, pp. 287-295, 1989.

[8] Huang Yong, Jiang Guo-feng, Qiukai-lan, Qu Chang-wen. "Radar Track-Before-Detect Algorithm of Multitarget Based on the Dynamic Programming", In Proc. 2006 CIE International Conference on Radar, Shanghai, IEEE PRESS, pp. 212-216, 2006.