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研究生:余秋忠
研究生(外文):Yu, Chiu-Chung
論文名稱:以內容為基礎之影像與視訊處理技術於鑑識科學之應用
論文名稱(外文):The Applications of Content-Based Image and Video Processing for Forensic Science
指導教授:溫哲彥溫哲彥引用關係
指導教授(外文):Wen, Che-Yen
學位類別:博士
校院名稱:中央警察大學
系所名稱:鑑識科學研究所
學門:軍警國防安全學門
學類:警政學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:130
中文關鍵詞:以內容為基礎的影像檢索技術移動偵測監視系統鑑識科學多部車輛車牌定位
外文關鍵詞:Content-based image retrieval (CBIR)motion detectionsurveillance systemforensic sciencemultiple license plate location
相關次數:
  • 被引用被引用:1
  • 點閱點閱:473
  • 評分評分:
  • 下載下載:122
  • 收藏至我的研究室書目清單書目收藏:0
指紋、DNA、鞋印、人貌、藥丸等資料庫已經廣泛地應用在刑事鑑識相關工作上,當鑑定上述物證時,須將採取到的物證資料輸入資料庫中,以檢索出相似的資料並進行比對;在犯罪調查過程中,這些資料比對的結果可以關聯不同的刑案。在傳統指紋資料庫,利用“關鍵字”的方式將指紋影像予以標記,當需要指紋比對時,從大量的影像資料庫中查詢相符的關鍵字,並以人工方式找出該指紋影像。此外,當不同指紋專家對同一枚指紋有不同的關鍵字認知時,將會獲得不同的指紋影像檢索結果。
1980年初開始,指紋資料庫已開始數位化,且是第一個應用在網路上的資料庫。隨著影像擷取、儲存設備的普及,及數位內容的應用發展,刑事鑑識單位已建立鞋印、工具痕跡、槍彈痕跡等影像資料庫,為了能快速、準確地檢索影像資料庫的資料,有關“以內容為基礎的影像檢索(content-based image retrieval, CBIR) 技術”的文獻如雨後春筍般的大量出現。CBIR在影像資料庫檢索上是一個很好的工具,它使用“影像特徵”當作檢索的關鍵字,例如,指紋卡通常以影像的方式儲存在「自動化指紋比對系統(Integrated Automated Fingerprint Identification System, IAFIS)」,相似性的判斷是根據指紋特徵空間的距離,越近的代表越相似。時至今日,除了IAFIS外,槍彈比對系統(Integrated Ballistic Identification System, IBIS)、鞋印比對系統(TreadMark™)、筆跡比對系統(Forensic Information System for Handwriting, FISH)等也已經應用在刑事鑑識工作上。在本文中,我們建立了一個可行的「藥丸影像檢索系統」。
除了影像資料庫之外,視訊資料同樣在刑事鑑識及犯罪調查中扮演了重要的角色,最近幾年,監視系統廣泛地應用在公共安全、交通監控和犯罪調查上,因此,非常多的鏡頭被設置在公共空間及室內空間,大量的視訊資料儲存在資料庫中,對管理者而言,這些設備的管理及資料的檢索是不容易的。
「移動偵測」即是要找出偵測出視訊中有移動的區域,而這個技術的另一涵義即是要切割出移動的物件出來。換句話說,即是在偵測視訊中像素(pixel)有變化的地方,如移動的物體和變化的背景,然而,像素的變化極易受光線因素的影響而產生干擾,因此,移動評估的方法可用來估算物體、前景及背景變化的情形。在路口監視系統各項應用功能中,自動化車牌辨識系統(Automatic license plate recognition system, ALPRS)對警政工作有著非常大的幫助,它主要有三項作用:(1)道路交通管理:提升交通安全及流量管制;(2)安全管理:車輛進出管制、停車管制及限制區域管制;(3)犯罪預防:預防犯罪發生及犯罪事後調查。
“車牌的定位”是影響車牌辨識準確率的主要因素,因此,多數的研究均著重在車牌定位的方法上,這些方法大多針對單一車道的單一車輛進行車牌定位,然而,現行單一鏡頭即可紀錄多車道且多部車輛的影像,上述方法已無法準確地定位多部車輛的車牌位置。在本文中,我們成功地利用視覺連貫演算(the optical flow algorithm)及Blob 分析方法定位多部車輛的車牌位置。
本文的主要貢獻為: (1)提出邊界方向特徵(the edge orientation feature)來檢索刑案現場、鞋印及幾何物件等影像; (2)建立一個可實行的藥丸影像資料庫,利用形狀簽名(shape signature)和紋理(texture)等特徵檢索; (3)提出移動評估的方法來偵測造成錯誤警報的移動事件及多重車牌的定位方法。本文實驗的結果,充分地支持我們所提的系統、方法、程序是可行的。
Abstract
Databases have been widely applied to forensic science and crime investigation.
In forensic science, we can identify questioned evidences or find similar evidences
with these database systems, such as fingerprints, DNA, shoe prints, faces, drug
tablets, video data. In crime investigation, we can use these systems to find the
relationship between different cases.
The traditional user interface of image databases uses textbased
retrieval
techniques which use text tags to label images. However, textbased
image retrieval
systems require manual labeling which is a cumbersome and expensive task for large
image databases. Furthermore, the variety of keywords from diversity realization may
cause different retrieval results.
At the beginning of the 1980s, digitized fingerprint databases became the first
forensic databases to be widely used. Other image databases, such as shoe marks, tool
marks and striation marks on cartridge cases and bullets, also became popular. The
improvements of image acquisition and storage facilities make it economically
feasible to build color image databases. With automatic contentbased
comparison
algorithms, we can find similar images from databases. The development of a
retrieval system requires a multidisciplinary approach with knowledge of multimedia
database organization, pattern recognition, image analysis and user interfaces. The
most important knowledge is contentbased
image retrieval (CBIR) techniques that
have been subjected to intensive research efforts.
CBIR provides a good tool to retrieve interested images from image databases. It
use “image features” (instead of “text”) as “searching keywords”. Some commercial CBIR have been available, such as Integrated Automated Fingerprint Identification
System (IAFIS), Integrated Ballistic Identification System (IBIS), TreadMark™ for
shoe prints, and Forensic Information System for Handwriting (FISH).
Video data, especially from surveillance systems, also play an important role in
forensic science and crime investigation. There are a lot of digital video data collected
in database nowadays. Video analyzing technologies are useful for us to quickly
access and get information from those video data. Motion detection is one of useful
video analyzing technologies. Motion detection is used to segment interested image
areas and find possible moving objects in video data. In general, motion detection is a
process of confirming a change between a moving object and its surroundings or the
change in the surroundings relative to an object. However, this process is sensitive to
the light condition. In this thesis, we propose an efficient motion detection method for
false alarms.
Automatic license plate recognition system (ALPRS) is one of the most
important examples of applying computer techniques to intelligent surveillance
systems. ALPRS has been applied in three main categories: (1) Road traffic
management: improving the flow and safety of vehicle traffic controls. (2) Security
management: recognizing and controlling the conditions of entry and exit of vehicles
from parking areas and restrained regions; tracking of vehicles in the restrained region.
(3) Crime prevention: help reducing the criminal intention before crime incidents
happen; help investigating and tracking criminal vehicle(s).
The performance of the license plate localization is crucial to ALPRS, because it
directly influences the accuracy and efficiency of the plate number recognition. A
number of methods have been proposed for license plate location. Most literatures focus on detecting the accurate location of single license plate from a vehicle image or
video. However, there are usually more than one vehicle appear within an image
frame simultaneously in practical cases. That is, we need to locate multiple license
plates before identifying their license plate numbers. In this thesis, we use the optical
flow algorithm and blob analysis to locate multiple license plates in video sequences.
The main contributions of this thesis are as follows: (1) provide the edge
orientation features to retrieve crime scene images, shoe print blocks, and geometric
objects; (2) design a feasible image database of drug tablets based upon shape
signatures and texture features; (3) provide a motion estimation method to detect the
motion incident of false alarms and locate multiple vehicle license plates. The
experimental results show the capability of the proposed systems and methods.
Contents
Abstract .......................... II
摘要 ............................... V
Contents
Contents
1 Introduction ..................... 1
2 Visual Features .................. 8
2.1 Color .......................... 8
2.1.1 Color Spaces ................. 8
2.1.2 Color Descriptors ........... 11
2.1.2.1 Color Histograms........... 12
2.1.2.2 Region Color .............. 13
2.2 Texture ....................... 14
2.2.1 The Co-occurrence Matrices .. 17
2.2.2 Coarseness .................. 18
2.2.3 Contrast..................... 19
2.2.4 Gabor Filters ............... 19
2.3 Shape ......................... 22
2.3.1 Shape Signature ............. 22
2.3.1.1 Shape detection ........... 24
2.3.1.2 Shape features extraction . 26
2.3.2 The Directional Fields ...... 27
2.3.2.1 Color to gray-scale transformation .................... 28
2.3.2.2 Edge detection ............ 30
2.3.2.3 Edge orientation quantization 33
2.3.2.4 Feature computation ....... 33
2.3.2.5 Normalization ............. 35
2.3.3 Shape similarity comparison.. 37
2.4 Motion ........................ 37
2.4.1 Motion Estimation ........... 39
2.4.2 Hierarchical Block Matching Algorithm (HBMA) .................. 42
2.4.3 The Optical Flow Algorithm .. 46
2.4.4 The Horn-Schunck Algorithm .. 47
2.4.5 Blob analysis ............... 49
3 CBIR of Crime Scene Images ...... 52
3.1 Introduction .................. 52
3.2 Methods........................ 55
3.3 Experimental Results .......... 56
3.4 Conclusion .................... 59
4 CBIR of Crime Object Images ..... 60
4.1 Introduction .................. 60
4.2 Methods........................ 63
4.2.1 Image Color Transformation .. 64
4.2.2 Region Size Rescaling ....... 65
4.2.3 Imprint Mark Feature Extraction 65
4.2.4 Imprint Mark Similarity Comparison ........................ 70
4.3 Experimental Results .......... 70
4.5 Conclusions ................... 77
5 Applications of Motion Features in Surveillance Systems .............. 79
5.1 Introduction and Motivation ... 79
5.2 Methods........................ 84
5.2.1 Motion Vectors Classification 84
5.2.2 Elongatedness ............... 86
5.3 Experiments ................... 89
5.4 Conclusions and Discussion ... 100
6 Summary and Discussion ......... 102
7 Reference ...................... 104
2. Z Geradts and J Bijhold, "Content Based Information Retrieval in Forensic
Image Databases," Journal of Forensic Science, vol. 47, no. 2, pp. 285-292,
2002.
3. Z Geradts and J Bijhold, "Data Mining in Forensic Image Databases," in SPIE
Conference on Investigative Image Processing, vol. 4709, Orlando FL ,
Netherlands, 2002, pp. 92-101.
4. Z Geradts, H Hardy, A Poortmann, and J Bijhold, "Evaluation of Contents-Based
Image Retrieval Databases for a Database of Logos of Drugs Tablets," in Proc.
SPIE, vol. 4232, Boston, MA, USA, 2001, pp. 553-562.
5. Y C Wen, C C Yu, C W Yang, and T K Yang, "Content-Based Image Retrieval
(CBIR) and Its Applications to Forensic Sciences," in The 17th Meeting of IAFS,
Hong Kong, 2005.
6. Y C Wen and C C Yu, "Image Retrieval of Digital Crime Scene Images,"
Forensic Science Journal, vol. 4, pp. 37-45, 2005.
7. K A Jain, A Ross, and S Prabhakar, "An introduction to biometric recognition,"
IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1,
pp. 4-20, 2004.
8. C Vittorio and B D Lawrence, Image Database. New York: John Wiley &; Sons,
2002.
9. C R Veltkamp and M Tanase, "Content-Based Image Retrieval Systems: A
Survey," Department of Computing Science, Utrecht University, Technical
Report 2002.
10. H Weiming, X Nianhua, L Li, Z Xianglin, and S Maybank, "A Survey on Visual
Content-Based Video Indexing and Retrieval," IEEE Transactions on Systems,
Man, and Cybernetics, Part C: Applications and Reviews, vol. 6, pp. 797 - 819,
2011.
11. C R Gonzalez and E R Woods, Digital Image Processing. New Jersey:
Prentice-Hall, 2002.
12. J Keith, Video Demystified: a Handbook for the Digital Engineer, 3rd ed.: LLH
Technology Publishing, 2001.
13. J Y Song, B W Park, W D Kim, and H J Ahn, "Content-Based Image Retrieval
Using New Color Histogram," in Int. Signal Processing and Communication
Systems, 2004, pp. 609-611.
14. V Castelli and D L Bergman, Image Database: Search and Retrieval of Digital
Imagery. New York: John Wiley &; Sons, 2002.
15. M Stricker and A Di, "Color Indexing with Weak Spatial Constraints,"
Symposium on Electronic Imaging: Science and Technology - Storage &;
Retrieval for Image and Video Databases IV, pp. 29-41, 1996.
16. R J Smith and F S Chang, "Integrated Spatial and Feature Image Query,"
Multimedia System, vol. 7, no. 2, pp. 129-140, 1999.
17. H Tamura, S Mori, and T Yamawaki, "Texture features Corresponding to Visual
Perception," IEEE Systems, Man, and cybernetics, vol. 8, no. 6, pp. 460-473,
1978.
18. K J Kamarainen, V Kyrki, and H Kalviainen, "Fundamental Frequency Gabor
Filters for Object Recognition," Int. conference on Pattern Recognition, vol. 1,
pp. 628-631, 2002.
19. K J Kamarainen, V Kyrki, and H Kalviainen, "Noise Tolerant Object
Recognition Using Gabor Filtering," Int. Conference on Digital Signal
Processing, vol. 2, pp. 1349-1352, 2002.
20. K J Kamarainen, V Kyrki, and H Kalviainen, "Robustness of Gabor Feature
Parameter Selection," 2002.
21. K J Kamarainen, V Kyrki, H Kalviainen, M Hamouz, and J Kittler, "Invariant
Gabor Features for Face Evidence Extraction," 2002.
22. V Kyrki, K J Kamarainen, and H Kalviainen, "Simple Gabor Feature Space for
Invariant Object Recognition," Pattern Recognition Letters, pp. 311-318, 2004.
23. B J Li, G Huijun, and S J Pan, "Common Vector Analysis of Gabor Features with
Kernel Space Isomorphic Mapping for Face Recognition," Int. Journal of
Innovative Computing Information and Conrol, vol. 6, no. 9, pp. 4055-4064,
2010.
24. K J Kamarainen, V Kyrki, and H Kalviainen, "Invariance Properties of Gabor
Filter-Based Features-Overview and Applications," IEEE Image Processing, vol.
15, no. 5, pp. 1088-1099, 2006.
25. V Kyrki, K J Kamarainen, and H Kalviainen, "Content Based Image Matching
Using Gabor Filtering," Int. Conference on Advanced Concepts for Intelligent
Vision Systems Theory and Applications, pp. 45-49, 2004.
26. Y C Wen and C C Yu, "Fingerprint Pattern Restoration by Digital Image
Processing Techniques," Journal of Forensic Science, vol. 48, no. 5, pp. 973-984,
2003.
27. D Zhang and G Lu, "Review of Shape Representation and Description
Techniques," Pattern Recognition, vol. 37, pp. 1-19, 2004.
28. Y C Wen and Y J Yao, "Pistol Image Retrieval by Shape Representation,"
Forensic Science International, vol. 155, pp. 35-50, 2005.
29. N J Kapur, K P Sahoo, and K C A Wong, "An New Method For Gray-Level
Picture Thresholding Using The Entropy of The Histogram," Computer Vision ,
Graphic, and Image Processing, vol. 29, no. 3, pp. 273-285, 1985.
30. J Shanbehzadeh, F Mahmoudi, A Sarafzadeh, and AM Eftekhari-Moghadam, "A
Shape-Based Method for Content-Based Image Retrieval," Int. Conference on
CSI Computer, pp. 11-16, 2000.
31. F Mahmoudi, J Shanbehadeh, and AM Eftekhari-Moghadam, "Image Retrieval
Based on Shape Similarity by Edge Orientation Autocorrelogram," Pattern
Recognition, vol. 36, no. 8, pp. 1725-1736, 2003.
32. M Kass and A Witkin, "Analyzing Oriented Patterns," Computer Vision,
Graphics, &; Image Processing, vol. 37, no. 3, pp. 362-385, 1987.
33. A B Del and P Pala, "Retrieval by Elastic Matching of User Sketches," Pattern
Analysis and Machine Intelligence, vol. 19, no. 2, pp. 121-132, 1997.
34. J Zhou, G X Lu, D Zhang, and Y C Wu, "Orientation Analysis for Rotated
Human Face Detection," Image and Vision Computing, vol. 20, no. 4, pp.
257-264, 2002.
35. J Zhou, P L Xin, G Rong, and D Zhang, "Algorithm of Automatic Cartridge
Identificaiton," Optical Engineering, vol. 40, no. 12, pp. 2860-2865, 2001.
36. J L Latecki, R Lakaemper, and U Eckhardt, "Shape Descriptors for Non-rigid
Shapes with A Single Closed Contour," in IEEE Computer Society Conference on
Computer Vision and Pattern Recognition, Hamburg , 2000, pp. 13-15.
37. Y C Wen, X Z Li, and C C Yu, "Image Retrieval Based Upon Directional Fields,"
Forensic Science Journal, vol. 7, no. 1, pp. 13-27, 2008.
38. M Sonka, V Hlavac, and R Boyle, Image Processing Analysis and Machine
Vision. Toronto: Thomson, 2008.
39. Y Wang, J Ostermann, and Q Y Zhang, Video Processing and Communications.
NJ: Hall, Upper Saddle River, 2002.
40. M B Wang, C J Yen, and S Chang, "Zero Waiting-Cycle Hierarchical Block
Matching Algorithm and Its Array Architectures," IEEE Transactions on Circuits
and Systems for Video Technology, vol. 40, no. 1, pp. 18-28, 1994.
41. M H Jong, G L Chen, and D T Chiueh, "Parallel Architectures for 3-Step
Hierarchical Search Block-Matching Algorithm," IEEE Transactions on Circuits
and Systems for Video Technology, vol. 4, no. 4, pp. 407-416, 1994.
42. S Indu, M Gupta, and B Asok, "Vehicle Tracking and Speed Estimation Using
Optical Flow Method," Int. Journal of Engineering Science and Technology, vol.
3, no. 1, pp. 429-434, 2011.
43. C C Cheng and T H Li, "Feature-Based Optical Flow Computation," Int. Journal
of Information Technology, vol. 12, no. 7, pp. 82-92, 2006.
44. S Lu, G Tsechpenakis, N D Metaxas, L M Jensen, and J Kruse, "Blob analysis of
The Head and Hands: A Method for Deception Detection," in Proc. 38th Annual
Hawaii Int. Conference on System Sciences, New Jersey, 2005, pp. 1-20.
45. J Michael and D S Ballard, "Color Indexing," Int. Journal of Computer Vision,
vol. 7, no. 1, pp. 11-32, 1991.
46. L C Wilson and S Sirohey, "Human and machine recognition of faces: a survey ,"
Proceedings of the IEEE, vol. 83, no. 5, pp. 705-740, 1995.
47. R C Shyu, E C Brodley, C A Kak, and A Kosaka, "ASSERT: A
Physician-in-the-loop Content-Based Retrieval System for HRCT Image
Databases," Computer Vision and Image Understanding, vol. 75, pp. 111-132,
1999.
48. Y Rui, S T Hang, and F S Chang, "Visual image retrieval by elastic matching of
user sketches ," Pattern Analysis and Machine Intelligence, vol. 19, no. 2, pp.
121-132, 1997.
49. Y Chen, V Roussev, G Richard, and Y Gao, "Content-Based Image Retrieval for
Digital Forensics," In Proc. of the First International Conference on Digital
Forensics (IFIP), vol. 194, pp. 271-282, 2005.
50. M Z Lu, Z S Li, and B Hans, "A Content-Based Image Retrieval Scheme in
JPEG Compressed Domain," Int. Journal of Innovative Computing , Information
and Control, vol. 2, no. 4, pp. 831-839, 2006.
51. M W Zheng, M Z Lu, and H Burkhardt, "Color Image Retrieval Schemes Using
Index Histograms Based on Various Spatial-Domain Vector Quantizers," Int.
Journal of Innovaive Computing, Information and Control, vol. 2, no. 6, pp.
1317-1326, 2006.
52. Drugs.com. Online.. http://www.drugs.com/imprints.php
53. WebMD. Online.. http://www.webmd.com/pill-identification/default.htm
54. C R Chen, T C Pao, H Y Chen, and C J Jian, "Automatic Drug Image
Identification System Based on Multiple Image Features," Lecture Notes in
Computer Science, vol. 6422, pp. 249-257, 0210.
55. D Gabor, "Theory of Communications," Journal of the Institution of Electrical
Engineers - Part III: Radio and Communication Engineering, vol. 93, pp.
429-441, 1946.
56. R Datta, D Joshi, J Li, and Z J Wang, "Image retrieval: Ideas, influences, and
trends of the new age," in ACM Computing Surveys (CSUR), vol. 40(2), 2008.
57. S K Thakre, M A Rajurkar, and Manthalkar R, "An Effective CBVR System
Based on Motion, Quantized Color and Edge Density Features," Proc. of First
International Conference on Intelligent Interactive Technologies and
Multimedia, pp. 145-149, 2010.
58. A Caro, G P Rodriguez, R Morcillo, and M Barrena, "vManager, Developing A
Complete CBV System," Proc. of the 5th Iberian conference on pattern
recognition and image analysis, pp. 604-611, 2011.
59. H S Chiu, Y C Wen, and C W Kao, "An Effective Surveillance Video Retrieval
Method Based Upon Motion Detection," Int. Conference on Intelligence and
Security Informatics, pp. 261-262, 2008.
60. J Fridrich, "Watermarking for Tamper Detection," Proc. of ICIP, pp. 404-408,
1998.
61. D M Swanson and H A Tewfik, "Multimedia Data-Embedding and Watermarking
Technologies," Proc. of IEEE, vol. 86, no. 6, pp. 1064-1087, 1998.
62. K D Roberts, "Security Camera Video Authentication," Digital Signal
Processing Workshop and Signal Processing Education Workshop, pp. 125-130,
2002.
63. R Evan, A Stefan, M Osama, P Nikolaos, and V Richard, "Real-Time Dection of
Camera Tampering," in Int. Conference of IEEE, Sydney, NSW, Australia, 2006.
64. A Aksay, A Temizel, and A C Enis, "Camera Tamper Detection Using Wavelet
Analysis for Video Surveillance," in Pro. of the 2007 IEEE Conference on
Advanced Video and Signal Based Surveillance, METU, Ankara , 2007, pp.
558-562.
65. K Suckchul, N Yunyoung, K Jinhyung, and C We-Duke, "Intelligent Surveillance
System Using Autionomous Multiple Cameras," Ubiquitous Information
Technologies &; Applications, pp. 1-6, 2009.
66. C Alippi, G Boracchi, R Camplani, and M Roveri, "Detecting External
Disturbances on The Camera Lens in Wireless Multimedia Sensor Networks,"
IEEE Tran. Instrumentation and Measurement, pp. 2982-2990, 2010.
67. M R Chong and T Tanaka, "Image Extrema Analysis and Blur Detection with
Identificaiton," Proc. Int. IEEE Con. Signal-Image Technology Internet-Based
System, pp. 320-326, 2008.
68. R Liu, Z Li, and J Jia, "Image Partial Blur Detecion and Classification," in IEEE
Conference on Computer Vision and Pattern Recognition, Hong Kong , 2008, pp.
1-8.
69. L Kovacs et al., "Digital Video Event Detector Framework for Surveillance
Applications," in IEEE Int. Conference on Advanced Video and Signal Based
Surveillance, Budapest, Hungary, 2009, pp. 565-570.
70. C X He et al., "Motion Estimation Method for Blurred Videos and Application of
Deblurring with Spatially Varying Blur Kernels," in 5th Int. Conference on
Computer Sciences and Convergence Information Technology, Hong Kong,
2010, pp. 355-359.
71. B Chen and H H Cheng, "A Review of The Applications of Agent Technology in
Traffic and Transportation Systems," IEEE Trans. on Intelligent Transportation
Systems, vol. 11, no. 2, pp. 485-497, 2010.
72. H C N Yung, H K Au, and H S A Lai, "Recognition of Vehicle Registration Mark
on Moving Vehicles in An Outdoor Environment," IEEE Trans. Intelligent
Transportation Systems, vol. 10, no. 1, pp. 70-82, 1999.
73. S Y Huang, S Y Weng, and C M Zhou, "Critical Scenarios and Their
Identification in Parallel Railroad Level Crossing Traffic Control Systems,"
IEEE Trans. Intelligent Transportation, vol. 11, no. 4, pp. 968-977, 2010.
74. Y Wen et al., "An Algorithm for License Plate Recognition Applied to Intelligent
Transportation System," IEEE Trans. on Intelligent Transportation, vol. 12, no.
3, pp. 830-845, 2011.
75. H Zhang, W Jia, X He, and Q Wu, "A fast algorithm for license plate detection in
various conditions," in IEEE Int. Conference on Systems, Man and Cybernetics,
vol. 3, Sydney, 2006, pp. 2420-2425.
76. V Abolghasemi and A Ahmadyfard, "An edge-based color-aided method," Image
and Vision Computing, vol. 27, no. 8, pp. 1134-1142, 2009.
77. J Jiao, Q Ye, and Q Huang, "A configurable method for multistyle," Pattern
Recognition, vol. 42, no. 3, pp. 358-369, 2009.
78. H Sheng, C Li, Q Wen, and Z Xiong, "Real-Time Anti-Interference Location of
Vehicle License Plates Using High-Definition Video," IEEE in Telligent
Transportation Systems Magazine, vol. 4, pp. 17-23, 2009.
79. L Salgado, M J Menendez, E Rendon, and N Garcia, "Automatic Car Plate
Detection and Recognition Through Intelligent Vision Engineering," in IEEE
33rd Int. Carnahan Conference on Security Technology, Grupo de Tratamiento
de Imagenes, 1999, pp. 71-76.
80. P Y Huang, H C Chen, T Y Chang, and E F Sandnes, "An Intelligent Strategy for
Checking the Annual Inspection Status of Motorcycles Based on License Plate
Recognition," EXPERT SYST. APPL., vol. 36, no. 5, pp. 9260-9267, 2009.
81. R Y Wang, H W Lin, and J S Horng, "A Sliding Window Technique for Efficient
License Plate Localization Based on Discrete Wavelet Transform," Expert
Systems with Applications, vol. 38, pp. 3142-3146, 2011.
82. D Zheng, Y Zhao, and J Wang, "An Efficient Method of License Plate Location,"
Pattern Recognition Letters, vol. 26, no. 15, pp. 2431-2438, 2005.
83. Z Xu and H Zhu, "An Efficient Method of Locating Vehicle License Plate," in
3rd Int. Conference on Natural Computation, vol. 2, Lanzhou, 2007, pp.
180-183.
84. S Zuo and K Z Shi, "A Real-Time Algorithm For License Plate Extraction Based
on Mathematical Morphology," Journal of Image Graphics, vol. 8, no. 3, pp.
281-285, 2003.
85. M J Guo and F Y Liu, "License Plate Localization and Character Segmentation
with Feedback Self-Learning and Hybrid Binarization Techniques," IEEE Trans.
Vehicular Technology, vol. 57, no. 3, pp. 1417-1424, 2008.
86. W Jia, H Zhang, and X He, "Region-Based License Plate Detection," Journal of
Network and Computer Applications, vol. 30, no. 4, pp. 1324-1333, 2007.
87. S M Pan, B J Yan, and H Z Xiao, "Vehicle License Plate Character
Segmentation," Int. Journal of Automation and Computing, vol. 5, no. 4, pp.
425-432, 2008.
88. A Mucherino, J P Papajorgji, and M P Pardalos, Data Mining in Agriculture, CH
4: k-Nearest Neighbor Classification.: Springer Optimization and Its
Applications, 2009.
89. K A Jain and A Vailaya, "Image Retrieval Using Color and Shape," Pattern
Recognition, vol. 29, no. 8, pp. 1233-1244, 1996.
90. K A Jain and A Vailaya, "A Shape-Based Retrieval: A Case Study with
Trademark Image Database," Pattern Recognition, vol. 31, no. 9, pp. 1369-1390,
1998.
91. A Vailaya, A Jain, and J H Zhang, "On Image Classification: City vs.
Landscape," 1998.
92. B T Sebastian, N P Klein, and B B Kimia, "Recognition of Shapes by Editing
Shock Graphs," in IEEE Int. Conference on Computer Vision, 2001, pp. 7-14.
93. T Hideyuki, M Shunji, and Y Takashi, "Texture Features Corresponding to Visual
Perception," Systems, Man and Cybernetics, vol. 8, no. 6, pp. 460-473 , 1978.
94. M C Lee and A Kankanhalli, "Automatic Extraction of Characters in Complex
Scene Images," International journal of pattern recognition and artificial
intelligence, vol. 9, no. 1, pp. 67-82, 1995.
95. L Heuute, T Paquet, V J Moreau, Y Lecourtier, and C Olivier, "A Structural /
Statistical Feature Based Vector for Handwritten Character Recognition," Pattern
Recognition Letters, vol. 19, pp. 629-641, 1998.
96. Z Chi, J Wu, and H Yan, "Handwritten Numeral Recognition Using
Self-Organizing Maps and Fuzzy Rules," Pattern Recognition, vol. 28, no. 1, pp.
59-66, 1995.
97. Y Donggang and Y Hong, "Reconstruction of Broken Handwritten Digits Based
on Structural Morphological Features," Pattern Recognition, vol. 34, pp.
235-354, 2001.
98. H Hishida, "Shape Recognition by Integrating Structural Descriptions and
Geometrical / Statistical Transforms," Computer Vision and Image
Understandimg, vol. 64, no. 2, pp. 248-262, 1996.
99. H Jianming and Y Hong, "Structural Primitive Extraction and Coding for
Handwritten Numeral Recognition," Pattern Recognition, vol. 31, no. 5, pp.
493-509, 1998.
100. S V Chakrvarthy and B Kompella, "The Shape of Handwritten Characters,"
Pattern Recognition Letters, vol. 24, pp. 1901-1913, 2003.
101. R S Kodituwakku, "Comparison of Color Features for Image Retrieval," Indian
Journal of Computer Science and Engineering, vol. 1, no. 3, pp. 207-211, 2004.
102. D Xu, L Huang, and C Liu, "Object Tracking Using Particle Filter Based on
Color Correlogram ," in Second Int. Symposium on Intelligent Information
Technology Application, Beijing, 2008, pp. 608 - 612.
103. A B Del, Visual information retrieval. San Francisco, USA: Morgan Kaufmann,
1999.
104. C Ware, Information Visualization, Perception for Design. San Francisco, USA:
Morgan Kaufmann, 2000.
105. Y C Wen, F L Chang, and H H Li, "Content Based Video Retrieval With Motion
Vectors and the RGB Color Model ," Forensic Science Journal, vol. 6, no. 2, pp.
1-36, 2007.
106. S Calderara, R Cucchiara, and A Rrati, "A Multimedia Surveillance:
Content-Based Retrieval With Multicamera People Tracking," in ACM Int.
Workshop on Video Surveillance and Sensor Networks, 2006.
107. G M C Snoek and M Worring, "Concept-based video retrieval," Foundations and
Trends in Information Retrieval , vol. 2, no. 4, pp. 215–322, 2009.
108. http://biometrics4you.com/shape-of-ear.html.
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