VEHICLE IDENTIFICATION AND AUTHENTICATION SYSTEM
|
|
- Francis Edwards
- 6 years ago
- Views:
Transcription
1 VEHICLE IDENTIFICATION AND AUTHENTICATION SYSTEM T.Anusha 1, T.Sivakumar 2 1 Assistant Professor, Dept. of Computer Science & Engineering, PSG College of Technology, Tamilnadu, India, anu@cse.psgtech.ac.in 2 Assistant Professor(Sr.Gr), Dept. of Information Technology, PSG College of Technology,Tamilnadu, India, sk@ity.psgtech.ac.in Abstract Vehicle Identification and Authentication System is developed for traffic monitoring. To prevent unauthorized vehicles from entering the private areas, vehicle based authentication technologies are employed. The captured color image of the vehicle is converted to gray scale image. Gray scale image is converted into binary image using Sliding Concentric Windows (SCW) method. Pixels are labelled into components based on pixel connectivity using Connected Component Analysis (CCA) technique. The labelled components are examined and detected license plate is processed to isolate characters. These characters are sent to Probabilistic Neural Network (PNN).PNN uses a supervised training set to develop distributed functions within a pattern layer. Vehicle entering an area is considered as authenticated if it is registered. Registered vehicles number plate indicates the state and district to which particular vehicle belongs to. Vehicles are considered to be authenticated, if it belongs to a particular state and district, after which the vehicles type and other details are extracted from the database. Index Terms: Image Segmentation, Interpolation, Sliding Concentric Window, Probabilistic Neural Network *** INTRODUCTION Vehicle Identification and Authentication system is developed to identify a vehicle and examine the permit details of it. Each vehicle possesses a unique license plate number. A still Red, Green, Blue (RGB) image of a vehicle is given as input. It is then converted into a gray-scale image. License plate is segmented from the image using Sliding Concentric Windows (SCW) method. In this algorithm, two concentric windows were created for the first pixel of the image. Mean of the two windows were calculated. If the ratio exceeds a threshold, then the central pixel of the windows is considered to belong to a Region of Interest (ROI). Threshold is calculated using Otsu s method, mean and standard deviation. Using Connected Component Analysis(CCA) method, various components are labeled. Properties such as area, orientation, and aspect ratios are examined and the license plate is detected. The detected license plate is small in size when compared with the original image and so the image is then resized to 75x228 pixels image using bicubic interpolation method. With this method, the value f(x,y) of a function f at a point (x,y) is computed as a weighted average of the nearest sixteen samples in a rectangular grid(4x4 array). The License plate image is further binarized using SCW method. The segmented characters are then recognized using PNN. PNN is a kind of radial basis network suitable for classification problems. It uses supervised training set clustered into classes for recognizing characters. Each vehicle is registered to a Regional Transport Office (RTO). In old registration series, first three characters identify the RTO district and state of the vehicle belonging to. In new registration series, first two characters identify state, second two characters identify the RTO district of the vehicle 2. REVIEW OF OTHER TECHNIQUES 2.1 License Plate Detection License plate region can be extracted by combining edge statistics and mathematical morphology. In this method, regions with a high edge magnitude and high edge variance are considered as possible license plate regions [5]. The four steps used for license plate location are vertical edge detection, edge statistical analysis, hierarchical based license plate location, and morphology based license plate extraction. Gray scale image f of the input image is smoothed using linear filter and normalized. Then horizontal edge map G H and vertical edge map G V are calculated using equations (1) and (2) G H (i,j)= [f(i-1,j-1)+2(f(i-1,j+1)]-[f(i+1,j- 1)+2(f(i+1,j)+f(i+1,j+1)] (1) Available 222
2 G v (i,j)= [f(i-1,j-1)+2(f(i,j-1)+f(i+1,j-1)]-[f(i- 1,j+1)+2f(i,j+1)+f(i+1,j+1)] (2) Points are combined to lines and lines are combined to rectangles. Rectangles are combined or deleted. The license plate is located through scaling using different thresholds. When low scale is chosen, some fake plates also will get detected. Plate regions tend to have high density of edges. Edge density is calculated by summing all edge pixels using equation (3) in 3x15 block, which is at center G(i,j): d(i,j)=(1/45) g v (i+x,j+y)mask(i+x,j+y) (3) where d(i,j) represents the edge density map. Edge-based methods are too sensitive to unwanted edges and so this method can hardly be applied to complex images. License Plate can be extracted based on the color of the plate and the characters present in it. A neural network is used for more stable color extraction[4]. To find a plate region, fixed region of horizontal and vertical length is used. A color of a pixel is extracted using Hue, Lightness and Saturation (HLS) values of eight neighboring pixels. These surrounding colors influence the perception of a pixel color. Irrelevant pixels are blended into larger color groups for smoothing. A node of maximum value is chosen as the representative color. A standard back-propagation learning algorithm is used for training and testing. Solutions based upon color do not provide a high degree of accuracy in a natural scene as color is not stable when the lighting conditions change. Though color processing shows better performance, it still has difficulties, if the image has many similar parts of color values to a plate region. Fuzzy logic can be used for locating license plates. License plates are describes using fuzzy sets bright, dark, bright and dark sequence, texture, and yellowness to get the horizontal and vertical plate position[3]. Edge of the license plate is detected from an RGB image examining object and background color pairs. It is then followed by fuzzification, generating fuzzy H,S,I maps. Fuzzy maps and edge maps are combined to extract license plates. This process is known as fuzzy aggregation. Local irregularity in the image is described using image statistics using Sliding Concentric Windows (SCW) method. In this algorithm, two concentric windows are created for the first pixel of the image. Statistical measurements in windows were calculated. If the ratio of the statistical measurements exceeds a threshold, then the central pixel of the windows is considered to belong to a Region of Interest(ROI). The size of the windows and threshold accounts for proper segmentation of license plate. 2.2 Character Segmentation Markov Network [11] can be used for segmentation and recognition of license plate characters. Plate recognition algorithm determines the position and size of the license plate in an image. Skew correction algorithm is used to align the characters horizontally in the plate. Each character is extracted using histogram of a character color. CCA technique is applied to binarized plate candidate for eliminating undesired image areas. Components not satisfying the prescribed aspect ratios are deleted. Remaining components are aligned by applying Hough Transform to the centers of gravity of components. Noise components are assumed to be smaller than characters. So, if the number of remaining components is still larger than a prescribed number, connected components are deleted one at a time starting with the smallest one. SCW[1] method is used to segment the characters present in the license plate after resizing the resultant plate image to a standard size of 75x228 pixels using bicubic interpolation method. CCA is a technique in image processing used for scanning the image and labeling its pixels into components based on pixel connectivity. Fuzzy Neural Network[7] can be employed to recognize shifted and distorted training patterns. The input pattern is fuzzified by the network and the similarities of this pattern to the learned pattern are computed. A conclusion is reached by selecting the learned pattern with the highest similarity and a non-fuzzy output is given. Probabilistic Neural Network[2] is a multilayer feed forward network used for character recognition. The PNN is trained with training patterns to derive the activation function of a neuron. 2.3 Probabilistic Neural Network PNN classifier belongs to the class of Radial Basis Function(RBF) classifiers[2]. Members of a set are assumed to belong to different classes. Using Bayes rule, posteriori probability is calculated for observable pattern. Decision is taken by examining the posteriori probability. The pattern belongs to a class for which the posteriori probability is maximum. First layer has one pattern unit for each pattern exemplar. Second layer(hidden) contains one summation unit for each class. Output layer is the decision layer used for implementing the decision rule by selecting the maximum posteriori probability. Wiener take all competitive network is used for implementing decision layer. Available 223
3 PNN has a network structure which has a Gaussian shaped characteristic. The Euclidean distances between the points representing the training patterns in feature space for each class is calculated. The average minimum distance between exemplars in class S i is given in equation (5). d avg (i)=(1/ S i ) d j (i) (i) where S i denotes the number of elements, d j denote the distance between the j th exemplar pattern and the nearest exemplar pattern in class S i. The smoothing parameter s i for class S i is assigned as given in equation (6) s i =gd avg (i) (6) where g is a constant that has been found experimentally to be between 1.1 and Thresholding Using Otsu s Method Otsu s method [10] is a histogram based method. The normalized histogram is treated as a discrete probability density function as in equation(7). p r (r q )=n q /n (7) where n is the total number of pixels in the image, n q is the number of pixels that have the intensity level r q, and L is the total number of possible intensity levels in the image. A threshold K is chosen such that C 0 is the set of pixels with levels [0,1,,k-1] and C 1 is the set of pixels with levels [k,k+1,,l-1]. Otsu s method chooses the threshold value k 2 that maximizes the between-class variance s B which is defined as given in equation (8) s B 2 =ω 0 (μ 0 -μ T ) 2 + ω 1 (μ 1 -μ T ) 2 (8) where, ω 0 = p q (r q ) ω 1 = p q (r q ) (5) 3. NUMBER PLATE IDENTIFICATION AND AUTHENTICATION The following are the various steps involved in this system: 1. RGB image is given as input and converted to Gray scale image. 2. Threshold value is initialized. 3. SCW method is applied. 4. Properties of the plate is examined for locating the plate. 5. If the plate is not located,execute from step 4 after changing the threshold value. 6. Plate is resized using bicubic interpolation. 7. SCW method is applied to the detected plate. 8. Characters are segmented. 9. Segmented characters are recognized using PNN. 10. Registration details are extracted from the database and authenticity of the vehicle is displayed. The Fig-1 shows the various steps used in this work Input RGB Image Gray Scale Image Initialize a threshold value Apply SCW method Examine the properties and locate the plate μ 0 = q p q (r q )/ ω 0 μ 1 = q p q (r q )/ ω 0 μ T = p q (r q ) Located Yes No Change the threshold value Resize the plate using bicubic interpolation A Available 224
4 A Apply SCW to the detected plate Segment the characters Recognize using PNN Extract registration details from the database and display whether the vehicle is authenticated or not Fig-1: Steps involved in authenticating a vehicle The method proposed in this paper involves repeating SCW method with varying thresholds if no license plate is located. Initial threshold is fixed using Otsu s method. After binarizing, large and small objects are neglected. Characters which are splitted are merged and joined characters are splitted by examining the size criteria. Selected characters are resized to 9 x 12 pixels and sent to probabilistic neural network for further recognition. The PNN uses 180 patterns to get trained. Input given into this PNN is a vector having 108 input values representing a character. It is compared with class clusters by examining Euclidean distances and high output is generated for the matched character. Two letter code and the corresponding state are stored in the database. Also the database contains the information regarding old and new registration series for the vehicles. Database is used for getting the registration details of the vehicle. If, additionally, vehicle details are available in the database, it can be displayed. Vehicle is subjected to permit verification and tax collection if it belongs to other states. It is considered as authenticated, if it belongs to Tamilnadu, background color of the license plate is examined. License plate of commercial vehicles has yellow background and that of private vehicles have white background. 4. INDIAN LICENSE PLATES Two types of license plates used in India are taken for experiment. For commercial vehicles, the plate has a yellow background and black numbering. For private vehicles a white background with black numbering is used. The scheme comprises two letter identification for the state in which the vehicle is registered. It is followed by a two number code to identify the district. Last four-digit number is used to uniquely identify the vehicle. When the alphabet reaches Z, the length of the prefix is increased to 2.So after TN ,the next number is TN-01 A 0001 and after TN-01 Z 9999 it is TN-01 AA 0001 and so on. TN 07 BC 1827 is a vehicle registered in Chennai, Tamilnadu. In Tamilnadu, the letter G is reserved for Government vehicles and the letter N is reserved for Government Transport Buses. Old registration series has first three characters identify the RTO district. Eg)TCT EXPERIMENTS AND RESULTS The proposed method is tested with images showing license plate clearly, images with dark license plate, images with license plate with characters of different sizes, images with tilted license plates. Vehicle category is determined according to the background color of the license plate. If the image is clear and characters are visible to our satisfaction, the proposed system works well. For detecting plates in commercial vehicles, plates is detected using Hue, Saturation and Intensity (HSI) model and further SCW and PNN are used for character segmentation and recognition respectively. The proposed system is also tested with images taken with improper illumination. PNN is trained well so that the characters are recognized properly. The presence of multiple lines in the license plate is detected using projection method. The proposed system manages to detect the plate present in the vehicle image with complex background. Vehicle is authenticated if the number detected satisfies either old or new registration series for Tamilnadu. Vehicle is subjected to permit verification and tax collection if it belongs to states, other than Tamilnadu in India. 6. CONCLUSION Sliding Concentric Windows method can be used to detect license plate by repeating with varying threshold values and Probabilistic Neural Network can be used to recognize various characters in it. Further authentication can be done by comparing the license plate number with the database stored. Vehicles belonging to other states are subjected to tax collection and permit verification. 7. FUTURE ENHANCEMENT The proposed work can be enhanced by identifying the vehicle type using image processing techniques. In this case, vehicle Available 225
5 type is detected using image processing techniques and further compared with vehicle details maintained in the database. REFERENCES [1]. C.Anagnostopoulos, I.Anagnostopoulos, E.Kayafas, and V.Loumos, A license plate recognition system for intelligent transportation system applications, IEEE Transaction for Intelligent Transportation Systems, Vol.7, no.3, pp Sep [2]. K.Bose,P.Liang, Neural Network Fundamentals with Graphs, Algorithms, and Applications, Tata McGraw-Hill Edition, [3]. S.L.Chang, L.S.Chen, Y.C.Chung and S.W.Chen, Automatic license plate recognition, IEEE Transaction for Intelliegent Transportation System,Vol.5,No.1,pp Mar [4]. Eun Ryung Lee, PyeoungKee Kim, and Hang Joon Kim, Automatic Recognition of a Car License Plate using Color Image Processing, IEEE ICIP, Vol 2, pp ,Nov [5]. B.Hongliang and L.Changping, A Hybrid license plate extraction metod based on edge statistics and morphology, ICPR,pp , 2004 [6]. S.K.Kim,D.W.Kim, and H.J.Kim, Recognition of vehicle license plate using genetic algorithm based segmentation, International conference on image processing,pp Sep [10]. Rafael Gonzalez,Richard E.Woods,Steven L.Eddins, Digital Image Processing Using MATLAB, Pearson Education,2004. [11]. Xin Fan, Guoliang Fan, Dequn Liang, Joint Segmentation and Recognition of License Plate Characters, Proc.ICIP,pp ,2007. BIOGRAPHIES Prof.T.Anusha completed her Bachelor degree in Computer Science and Engineering from Dr.Sivanthi Aditanar College of Engineering, Tiruchendur and Master degree in Computer Science and Engineering from Manonmaniam Sundaranar University, Tirunelveli. She is currently working as an Assistant Professor in the Department of Computer Science and Engineering in PSG College of Technology, Coimbatore Prof.T.Sivakumar completed his Master degree in Computer Science and Engineering from Anna University of Technology, Coimbatore. He is currently working as an Assistant Professor (Senior Grade) in the Department of Information Technology, PSG College of Technology, Coimbatore. [7]. H.Kwan and Y.Cai, A Fuzzy Neural Network and its application to pattern recognition, IEEE Transaction on Fuzzy Systems. Vol 2,no 3 pp Aug [8]. K.V.Mardia and T.J.Hainsworth, A spatial thresholding method for image segmentation, IEEE Transaction on Pattern Analysis and Machine Intelligence,Vol.10,no.6,pp , Nov [9]. N.Nasrabadi and R.King, Image coding using vector quantization:a review, IEEE Transaction on Communication, Vol 36,no.8,pp , Aug Available 226
VEHICLE LICENSE PLATE DETECTION ALGORITHM BASED ON STATISTICAL CHARACTERISTICS IN HSI COLOR MODEL
VEHICLE LICENSE PLATE DETECTION ALGORITHM BASED ON STATISTICAL CHARACTERISTICS IN HSI COLOR MODEL Instructor : Dr. K. R. Rao Presented by: Prasanna Venkatesh Palani (1000660520) prasannaven.palani@mavs.uta.edu
More informationAn Improved Bernsen Algorithm Approaches For License Plate Recognition
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) ISSN: 78-834, ISBN: 78-8735. Volume 3, Issue 4 (Sep-Oct. 01), PP 01-05 An Improved Bernsen Algorithm Approaches For License Plate Recognition
More informationINDIAN VEHICLE LICENSE PLATE EXTRACTION AND SEGMENTATION
International Journal of Computer Science and Communication Vol. 2, No. 2, July-December 2011, pp. 593-599 INDIAN VEHICLE LICENSE PLATE EXTRACTION AND SEGMENTATION Chetan Sharma 1 and Amandeep Kaur 2 1
More informationAutomatic Licenses Plate Recognition System
Automatic Licenses Plate Recognition System Garima R. Yadav Dept. of Electronics & Comm. Engineering Marathwada Institute of Technology, Aurangabad (Maharashtra), India yadavgarima08@gmail.com Prof. H.K.
More informationKeyword: Morphological operation, template matching, license plate localization, character recognition.
Volume 4, Issue 11, November 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Automatic
More informationPreprocessing and Segregating Offline Gujarati Handwritten Datasheet for Character Recognition
Preprocessing and Segregating Offline Gujarati Handwritten Datasheet for Character Recognition Hetal R. Thaker Atmiya Institute of Technology & science, Kalawad Road, Rajkot Gujarat, India C. K. Kumbharana,
More informationLicense Plate Localisation based on Morphological Operations
License Plate Localisation based on Morphological Operations Xiaojun Zhai, Faycal Benssali and Soodamani Ramalingam School of Engineering & Technology University of Hertfordshire, UH Hatfield, UK Abstract
More informationA Method of Multi-License Plate Location in Road Bayonet Image
A Method of Multi-License Plate Location in Road Bayonet Image Ying Qian The lab of Graphics and Multimedia Chongqing University of Posts and Telecommunications Chongqing, China Zhi Li The lab of Graphics
More informationImplementation of License Plate Recognition System in ARM Cortex A8 Board
www..org 9 Implementation of License Plate Recognition System in ARM Cortex A8 Board S. Uma 1, M.Sharmila 2 1 Assistant Professor, 2 Research Scholar, Department of Electrical and Electronics Engg, College
More informationVehicle License Plate Recognition System Using LoG Operator for Edge Detection and Radon Transform for Slant Correction
Vehicle License Plate Recognition System Using LoG Operator for Edge Detection and Radon Transform for Slant Correction Jaya Gupta, Prof. Supriya Agrawal Computer Engineering Department, SVKM s NMIMS University
More informationDetection of License Plates of Vehicles
13 W. K. I. L Wanniarachchi 1, D. U. J. Sonnadara 2 and M. K. Jayananda 2 1 Faculty of Science and Technology, Uva Wellassa University, Sri Lanka 2 Department of Physics, University of Colombo, Sri Lanka
More information中国科技论文在线. An Efficient Method of License Plate Location in Natural-scene Image. Haiqi Huang 1, Ming Gu 2,Hongyang Chao 2
Fifth International Conference on Fuzzy Systems and Knowledge Discovery n Efficient ethod of License Plate Location in Natural-scene Image Haiqi Huang 1, ing Gu 2,Hongyang Chao 2 1 Department of Computer
More informationAn Evaluation of Automatic License Plate Recognition Vikas Kotagyale, Prof.S.D.Joshi
An Evaluation of Automatic License Plate Recognition Vikas Kotagyale, Prof.S.D.Joshi Department of E&TC Engineering,PVPIT,Bavdhan,Pune ABSTRACT: In the last decades vehicle license plate recognition systems
More informationAutomatic License Plate Recognition System using Histogram Graph Algorithm
Automatic License Plate Recognition System using Histogram Graph Algorithm Divyang Goswami 1, M.Tech Electronics & Communication Engineering Department Marudhar Engineering College, Raisar Bikaner, Rajasthan,
More informationVehicle Number Plate Recognition with Bilinear Interpolation and Plotting Horizontal and Vertical Edge Processing Histogram with Sound Signals
Vehicle Number Plate Recognition with Bilinear Interpolation and Plotting Horizontal and Vertical Edge Processing Histogram with Sound Signals Aarti 1, Dr. Neetu Sharma 2 1 DEPArtment Of Computer Science
More informationVolume 7, Issue 5, May 2017
Volume 7, Issue 5, May 2017 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Localization Techniques
More informationColor Image Segmentation Using K-Means Clustering and Otsu s Adaptive Thresholding
Color Image Segmentation Using K-Means Clustering and Otsu s Adaptive Thresholding Vijay Jumb, Mandar Sohani, Avinash Shrivas Abstract In this paper, an approach for color image segmentation is presented.
More informationStudy and Analysis of various preprocessing approaches to enhance Offline Handwritten Gujarati Numerals for feature extraction
International Journal of Scientific and Research Publications, Volume 4, Issue 7, July 2014 1 Study and Analysis of various preprocessing approaches to enhance Offline Handwritten Gujarati Numerals for
More informationReal Time ALPR for Vehicle Identification Using Neural Network
_ Real Time ALPR for Vehicle Identification Using Neural Network Anushree Deshmukh M.E Student Terna Engineering College,Navi Mumbai Email: anushree_deshmukh@yahoo.co.in Abstract With the rapid growth
More informationEE 5359 MULTIMEDIA PROCESSING. Vehicle License Plate Detection Algorithm Based on Statistical Characteristics in HSI Color Model
EE 5359 MULTIMEDIA PROCESSING Vehicle License Plate Detection Algorithm Based on Statistical Characteristics in HSI Color Model Under the guidance of Dr. K. R. Rao Submitted by: Prasanna Venkatesh Palani
More informationInternational Journal of Advance Engineering and Research Development
Scientific Journal of Impact Factor (SJIF): 4.72 International Journal of Advance Engineering and Research Development Volume 4, Issue 10, October -2017 e-issn (O): 2348-4470 p-issn (P): 2348-6406 REVIEW
More informationFig.1: Sample license plate images[13] A typical LPR system is composed of several hardware and software components as illustrated in Figure 2
International Journals of Advanced Research in Computer Science and Software Engineering Research Article June 2017 License Plate Localization Method Based on VerticalEdge Detection Neha Rana MTech Scholar,
More informationNumber Plate Recognition Using Segmentation
Number Plate Recognition Using Segmentation Rupali Kate M.Tech. Electronics(VLSI) BVCOE. Pune 411043, Maharashtra, India. Dr. Chitode. J. S BVCOE. Pune 411043 Abstract Automatic Number Plate Recognition
More informationA Chinese License Plate Recognition System
A Chinese License Plate Recognition System Bai Yanping, Hu Hongping, Li Fei Key Laboratory of Instrument Science and Dynamic Measurement North University of China, No xueyuan road, TaiYuan, ShanXi 00051,
More informationDigital Image Processing 3/e
Laboratory Projects for Digital Image Processing 3/e by Gonzalez and Woods 2008 Prentice Hall Upper Saddle River, NJ 07458 USA www.imageprocessingplace.com The following sample laboratory projects are
More informationAn Efficient Color Image Segmentation using Edge Detection and Thresholding Methods
19 An Efficient Color Image Segmentation using Edge Detection and Thresholding Methods T.Arunachalam* Post Graduate Student, P.G. Dept. of Computer Science, Govt Arts College, Melur - 625 106 Email-Arunac682@gmail.com
More informationAutomated Number Plate Recognition System Using Machine learning algorithms (Kstar)
Automated Number Plate Recognition System Using Machine learning algorithms (Kstar) Er. Dinesh Bhardwaj 1, Er. Shruti Gujral 2 1, 2 Computer Science and Engineering Department, Chandigarh University, Mohali,
More informationA NOVEL APPROACH FOR CHARACTER RECOGNITION OF VEHICLE NUMBER PLATES USING CLASSIFICATION
A NOVEL APPROACH FOR CHARACTER RECOGNITION OF VEHICLE NUMBER PLATES USING CLASSIFICATION Nora Naik Assistant Professor, Dept. of Computer Engineering, Agnel Institute of Technology & Design, Goa, India
More informationAN EXPANDED-HAAR WAVELET TRANSFORM AND MORPHOLOGICAL DEAL BASED APPROACH FOR VEHICLE LICENSE PLATE LOCALIZATION IN INDIAN CONDITIONS
AN EXPANDED-HAAR WAVELET TRANSFORM AND MORPHOLOGICAL DEAL BASED APPROACH FOR VEHICLE LICENSE PLATE LOCALIZATION IN INDIAN CONDITIONS Mo. Avesh H. Chamadiya 1, Manoj D. Chaudhary 2, T. Venkata Ramana 3
More informationMalaysian Car Number Plate Detection System Based on Template Matching and Colour Information
Malaysian Car Number Plate Detection System Based on Template Matching and Colour Information Mohd Firdaus Zakaria, Shahrel A. Suandi Intelligent Biometric Group, School of Electrical and Electronics Engineering,
More informationComparison of Two Pixel based Segmentation Algorithms of Color Images by Histogram
5 Comparison of Two Pixel based Segmentation Algorithms of Color Images by Histogram Dr. Goutam Chatterjee, Professor, Dept of ECE, KPR Institute of Technology, Ghatkesar, Hyderabad, India ABSTRACT The
More informationNumber Plate Recognition System using OCR for Automatic Toll Collection
IJSTE - International Journal of Science Technology & Engineering Volume 2 Issue 10 April 2016 ISSN (online): 2349-784X Number Plate Recognition System using OCR for Automatic Toll Collection Mohini S.Karande
More informationEnhanced MLP Input-Output Mapping for Degraded Pattern Recognition
Enhanced MLP Input-Output Mapping for Degraded Pattern Recognition Shigueo Nomura and José Ricardo Gonçalves Manzan Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia, MG,
More informationRecognition Of Vehicle Number Plate Using MATLAB
Recognition Of Vehicle Number Plate Using MATLAB Mr. Ami Kumar Parida 1, SH Mayuri 2,Pallabi Nayk 3,Nidhi Bharti 4 1Asst. Professor, Gandhi Institute Of Engineering and Technology, Gunupur 234Under Graduate,
More informationContents 1 Introduction Optical Character Recognition Systems Soft Computing Techniques for Optical Character Recognition Systems
Contents 1 Introduction.... 1 1.1 Organization of the Monograph.... 1 1.2 Notation.... 3 1.3 State of Art.... 4 1.4 Research Issues and Challenges.... 5 1.5 Figures.... 5 1.6 MATLAB OCR Toolbox.... 5 References....
More informationA Simple Skew Correction Method of Sudanese License Plate
A Simple Skew Correction Method of Sudanese License Plate Musab Bagabir 1 and Mohamed Elhafiz 2 1 Faculty of Computer Studies, The National Ribat University, Khartoum, Sudan 2 College of Computer Science
More informationEFFICIENT CONTRAST ENHANCEMENT USING GAMMA CORRECTION WITH MULTILEVEL THRESHOLDING AND PROBABILITY BASED ENTROPY
EFFICIENT CONTRAST ENHANCEMENT USING GAMMA CORRECTION WITH MULTILEVEL THRESHOLDING AND PROBABILITY BASED ENTROPY S.Gayathri 1, N.Mohanapriya 2, B.Kalaavathi 3 1 PG student, Computer Science and Engineering,
More informationAn Approach to Korean License Plate Recognition Based on Vertical Edge Matching
An Approach to Korean License Plate Recognition Based on Vertical Edge Matching Mei Yu and Yong Deak Kim Ajou University Suwon, 442-749, Korea Abstract License plate recognition (LPR) has many applications
More informationFPGA based Real-time Automatic Number Plate Recognition System for Modern License Plates in Sri Lanka
RESEARCH ARTICLE OPEN ACCESS FPGA based Real-time Automatic Number Plate Recognition System for Modern License Plates in Sri Lanka Swapna Premasiri 1, Lahiru Wijesinghe 1, Randika Perera 1 1. Department
More informationInternational Journal of Scientific & Engineering Research, Volume 5, Issue 5, May ISSN
International Journal of Scientific & Engineering Research, Volume 5, Issue 5, May-2014 601 Automatic license plate recognition using Image Enhancement technique With Hidden Markov Model G. Angel, J. Rethna
More informationIraqi Car License Plate Recognition Using OCR
Iraqi Car License Plate Recognition Using OCR Safaa S. Omran Computer Engineering Techniques College of Electrical and Electronic Techniques Baghdad, Iraq omran_safaa@ymail.com Jumana A. Jarallah Computer
More informationAddis Ababa University School of Graduate Studies Addis Ababa Institute of Technology
1 Addis Ababa University School of Graduate Studies Addis Ababa Institute of Technology Design and Implementation of Car Plate Recognition System for Ethiopian Car Plates By: Huda Zuber Ahmed Addis Ababa
More informationChapter 17. Shape-Based Operations
Chapter 17 Shape-Based Operations An shape-based operation identifies or acts on groups of pixels that belong to the same object or image component. We have already seen how components may be identified
More informationAutomatics Vehicle License Plate Recognition using MATLAB
Automatics Vehicle License Plate Recognition using MATLAB Alhamzawi Hussein Ali mezher Faculty of Informatics/University of Debrecen Kassai ut 26, 4028 Debrecen, Hungary. Abstract - The objective of this
More informationNote to Coin Exchanger
Note to Coin Exchanger Pranjali Badhe, Pradnya Jamadhade, Vasanta Kamble, Prof. S. M. Jagdale Abstract The need of coin currency change has been increased with the present scenario. It has become more
More informationAutomated Number Plate Verification System based on Video Analytics
Automated Number Plate Verification System based on Video Analytics Kumar Abhishek Gaurav 1, Viveka 2, Dr. Rajesh T.M 3, Dr. Shaila S.G 4 1,2 M. Tech, Dept. of Computer Science and Engineering, 3 Assistant
More informationUrban Feature Classification Technique from RGB Data using Sequential Methods
Urban Feature Classification Technique from RGB Data using Sequential Methods Hassan Elhifnawy Civil Engineering Department Military Technical College Cairo, Egypt Abstract- This research produces a fully
More informationChapter 6. [6]Preprocessing
Chapter 6 [6]Preprocessing As mentioned in chapter 4, the first stage in the HCR pipeline is preprocessing of the image. We have seen in earlier chapters why this is very important and at the same time
More informationAutomatic Car License Plate Detection System for Odd and Even Series
Automatic Car License Plate Detection System for Odd and Even Series Sapna Gaur Research Scholar Hindustan Institute of Technology Agra APJ Abdul Kalam Technical University, Lucknow Sweta Singh Asst. Professor
More informationBrain Tumor Segmentation of MRI Images Using SVM Classifier Abstract: Keywords: INTRODUCTION RELATED WORK A UGC Recommended Journal
Brain Tumor Segmentation of MRI Images Using SVM Classifier Vidya Kalpavriksha 1, R. H. Goudar 1, V. T. Desai 2, VinayakaMurthy 3 1 Department of CNE, VTU Belagavi 2 Department of CSE, VSMIT, Nippani 3
More informationLine Segmentation and Orientation Algorithm for Automatic Bengali License Plate Localization and Recognition
Line Segmentation and Orientation Algorithm for Automatic Bengali License Plate Localization and Recognition Md. Rokibul Haque B.Sc. Student Sylhet Engineering College Saddam Hossain B.Sc. Student Sylhet
More informationRESEARCH PAPER FOR ARBITRARY ORIENTED TEAM TEXT DETECTION IN VIDEO IMAGES USING CONNECTED COMPONENT ANALYSIS
International Journal of Latest Trends in Engineering and Technology Vol.(7)Issue(4), pp.137-141 DOI: http://dx.doi.org/10.21172/1.74.018 e-issn:2278-621x RESEARCH PAPER FOR ARBITRARY ORIENTED TEAM TEXT
More informationMAV-ID card processing using camera images
EE 5359 MULTIMEDIA PROCESSING SPRING 2013 PROJECT PROPOSAL MAV-ID card processing using camera images Under guidance of DR K R RAO DEPARTMENT OF ELECTRICAL ENGINEERING UNIVERSITY OF TEXAS AT ARLINGTON
More informationLibyan Licenses Plate Recognition Using Template Matching Method
Journal of Computer and Communications, 2016, 4, 62-71 Published Online May 2016 in SciRes. http://www.scirp.org/journal/jcc http://dx.doi.org/10.4236/jcc.2016.47009 Libyan Licenses Plate Recognition Using
More informationNigerian Vehicle License Plate Recognition System using Artificial Neural Network
Nigerian Vehicle License Plate Recognition System using Artificial Neural Network Amusan D.G 1, Arulogun O.T 2 and Falohun A.S 3 Open and Distance Learning Centre, Ladoke Akintola University of Technology,
More informationLocalization of License Plates from Surveillance Camera Images: A Color Feature Based ANN Approach
Localization of License Plates from Surveillance Camera Images: A Color Feature Based ANN Approach Satadal Saha Sr. Lecturer MCKV Institute of Engg. Liluah Subhadip Basu Sr. Lecturer Jadavpur University
More informationExtraction and Recognition of Text From Digital English Comic Image Using Median Filter
Extraction and Recognition of Text From Digital English Comic Image Using Median Filter S.Ranjini 1 Research Scholar,Department of Information technology Bharathiar University Coimbatore,India ranjinisengottaiyan@gmail.com
More informationA Novel Morphological Method for Detection and Recognition of Vehicle License Plates
American Journal of Applied Sciences 6 (12): 2066-2070, 2009 ISSN 1546-9239 2009 Science Publications A Novel Morphological Method for Detection and Recognition of Vehicle License Plates 1 S.H. Mohades
More informationEfficient Car License Plate Detection and Recognition by Using Vertical Edge Based Method
Efficient Car License Plate Detection and Recognition by Using Vertical Edge Based Method M. Veerraju *1, S. Saidarao *2 1 Student, (M.Tech), Department of ECE, NIE, Macherla, Andrapradesh, India. E-Mail:
More informationAdaptive Feature Analysis Based SAR Image Classification
I J C T A, 10(9), 2017, pp. 973-977 International Science Press ISSN: 0974-5572 Adaptive Feature Analysis Based SAR Image Classification Debabrata Samanta*, Abul Hasnat** and Mousumi Paul*** ABSTRACT SAR
More informationNumber Plate Detection with a Multi-Convolutional Neural Network Approach with Optical Character Recognition for Mobile Devices
J Inf Process Syst, Vol.12, No.1, pp.100~108, March 2016 http://dx.doi.org/10.3745/jips.04.0022 ISSN 1976-913X (Print) ISSN 2092-805X (Electronic) Number Plate Detection with a Multi-Convolutional Neural
More informationImage Processing and Artificial Neural Network techniques in Identifying Defects of Textile Products
Image Processing and Artificial Neural Network techniques in Identifying Defects of Textile Products Mrs.P.Banumathi 1, Ms.T.S.Ushanandhini 2 1 Associate Professor, Department of Computer Science and Engineering,
More informationDetection of Compound Structures in Very High Spatial Resolution Images
Detection of Compound Structures in Very High Spatial Resolution Images Selim Aksoy Department of Computer Engineering Bilkent University Bilkent, 06800, Ankara, Turkey saksoy@cs.bilkent.edu.tr Joint work
More informationAn Efficient Method for Vehicle License Plate Detection in Complex Scenes
Circuits and Systems, 011,, 30-35 doi:10.436/cs.011.4044 Published Online October 011 (http://.scirp.org/journal/cs) An Efficient Method for Vehicle License Plate Detection in Complex Scenes Abstract Mahmood
More informationFace Detection System on Ada boost Algorithm Using Haar Classifiers
Vol.2, Issue.6, Nov-Dec. 2012 pp-3996-4000 ISSN: 2249-6645 Face Detection System on Ada boost Algorithm Using Haar Classifiers M. Gopi Krishna, A. Srinivasulu, Prof (Dr.) T.K.Basak 1, 2 Department of Electronics
More informationInternational Conference on Computer, Communication, Control and Information Technology (C 3 IT 2009) Paper Code: DSIP-024
Paper Code: DSIP-024 Oral 270 A NOVEL SCHEME FOR BINARIZATION OF VEHICLE IMAGES USING HIERARCHICAL HISTOGRAM EQUALIZATION TECHNIQUE Satadal Saha 1, Subhadip Basu 2 *, Mita Nasipuri 2, Dipak Kumar Basu
More information[Mohindra, 2(7): July, 2013] ISSN: Impact Factor: 1.852
IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY License Plate Recognition (LPR) system for Indian Vehicle License Plate Extraction and Character Segmentation Surabhi Mohindra
More informationSegmentation using Saturation Thresholding and its Application in Content-Based Retrieval of Images
Segmentation using Saturation Thresholding and its Application in Content-Based Retrieval of Images A. Vadivel 1, M. Mohan 1, Shamik Sural 2 and A.K.Majumdar 1 1 Department of Computer Science and Engineering,
More informationCOLOR LASER PRINTER IDENTIFICATION USING PHOTOGRAPHED HALFTONE IMAGES. Do-Guk Kim, Heung-Kyu Lee
COLOR LASER PRINTER IDENTIFICATION USING PHOTOGRAPHED HALFTONE IMAGES Do-Guk Kim, Heung-Kyu Lee Graduate School of Information Security, KAIST Department of Computer Science, KAIST ABSTRACT Due to the
More informationTHE PROPOSED IRAQI VEHICLE LICENSE PLATE RECOGNITION SYSTEM BY USING PREWITT EDGE DETECTION ALGORITHM
THE PROPOSED IRAQI VEHICLE LICENSE PLATE RECOGNITION SYSTEM BY USING PREWITT EDGE DETECTION ALGORITHM ELAF J. AL TAEE Computer Science, Kufa University, College of Education Kufa, Najaf, IRAQ E-mail: elafj.altaee@uokufa.edu.iq
More informationResearch on Application of Conjoint Neural Networks in Vehicle License Plate Recognition
International Journal of Engineering Research and Technology. ISSN 0974-3154 Volume 11, Number 10 (2018), pp. 1499-1510 International Research Publication House http://www.irphouse.com Research on Application
More informationCOMPARATIVE PERFORMANCE ANALYSIS OF HAND GESTURE RECOGNITION TECHNIQUES
International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 9, Issue 3, May - June 2018, pp. 177 185, Article ID: IJARET_09_03_023 Available online at http://www.iaeme.com/ijaret/issues.asp?jtype=ijaret&vtype=9&itype=3
More informationStudent: Nizar Cherkaoui. Advisor: Dr. Chia-Ling Tsai (Computer Science Dept.) Advisor: Dr. Eric Muller (Biology Dept.)
Student: Nizar Cherkaoui Advisor: Dr. Chia-Ling Tsai (Computer Science Dept.) Advisor: Dr. Eric Muller (Biology Dept.) Outline Introduction Foreground Extraction Blob Segmentation and Labeling Classification
More informationOpen Access An Improved Character Recognition Algorithm for License Plate Based on BP Neural Network
Send Orders for Reprints to reprints@benthamscience.ae 202 The Open Electrical & Electronic Engineering Journal, 2014, 8, 202-207 Open Access An Improved Character Recognition Algorithm for License Plate
More informationExercise questions for Machine vision
Exercise questions for Machine vision This is a collection of exercise questions. These questions are all examination alike which means that similar questions may appear at the written exam. I ve divided
More informationWorld Journal of Engineering Research and Technology WJERT
wjert, 2017, Vol. 3, Issue 3, 357-366 Original Article ISSN 2454-695X Shagun et al. WJERT www.wjert.org SJIF Impact Factor: 4.326 NUMBER PLATE RECOGNITION USING MATLAB 1 *Ms. Shagun Chaudhary and 2 Miss
More informationA Survey on License Plate Recognition Systems
A Survey on License Plate Recognition Systems Divya Gilly Computer Science and Engineering Department Karunya University ABSTRACT License Plate Recognition (LPR) is a well known image processing technology.
More informationKeywords Fuzzy Logic, ANN, Histogram Equalization, Spatial Averaging, High Boost filtering, MSE, RMSE, SNR, PSNR.
Volume 4, Issue 1, January 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com An Image Enhancement
More informationSmart Number Plate Identification Using Back Propagation Neural Network
Smart Number Plate Identification Using Back Propagation Neural Network Prof. Pankaj Salunkhe 1, Mr. Akshay Dhawale 2 1 Head of Department (Electronics & Telecommunication Engineering), YTIET, Bhivpuri
More informationAutomatic Vehicle Number Plate Recognition for Vehicle Parking Management System
Automatic Vehicle Number Plate Recognition for Vehicle Parking Management System Ganesh R. Jadhav, Electronics and Telecommunication Engineering Department, SKN Sinhgad college of engineering, Pandharpur,
More informationCOLOR IMAGE SEGMENTATION USING K-MEANS CLASSIFICATION ON RGB HISTOGRAM SADIA BASAR, AWAIS ADNAN, NAILA HABIB KHAN, SHAHAB HAIDER
COLOR IMAGE SEGMENTATION USING K-MEANS CLASSIFICATION ON RGB HISTOGRAM SADIA BASAR, AWAIS ADNAN, NAILA HABIB KHAN, SHAHAB HAIDER Department of Computer Science, Institute of Management Sciences, 1-A, Sector
More informationAn Efficient Method for Landscape Image Classification and Matching Based on MPEG-7 Descriptors
An Efficient Method for Landscape Image Classification and Matching Based on MPEG-7 Descriptors Pharindra Kumar Sharma Nishchol Mishra M.Tech(CTA), SOIT Asst. Professor SOIT, RajivGandhi Technical University,
More informationAnna University, Chennai B.E./B.TECH DEGREE EXAMINATION, MAY/JUNE 2013 Seventh Semester
www.vidyarthiplus.com Anna University, Chennai B.E./B.TECH DEGREE EXAMINATION, MAY/JUNE 2013 Seventh Semester Electronics and Communication Engineering EC 2029 / EC 708 DIGITAL IMAGE PROCESSING (Regulation
More informationReal Time Word to Picture Translation for Chinese Restaurant Menus
Real Time Word to Picture Translation for Chinese Restaurant Menus Michelle Jin, Ling Xiao Wang, Boyang Zhang Email: mzjin12, lx2wang, boyangz @stanford.edu EE268 Project Report, Spring 2014 Abstract--We
More informationInstitute of Technology, Carlow CW228. Project Report. Project Title: Number Plate f Recognition. Name: Dongfan Kuang f. Login ID: C f
Institute of Technology, Carlow B.Sc. Hons. in Software Engineering CW228 Project Report Project Title: Number Plate f Recognition f Name: Dongfan Kuang f Login ID: C00131031 f Supervisor: Nigel Whyte
More informationECC419 IMAGE PROCESSING
ECC419 IMAGE PROCESSING INTRODUCTION Image Processing Image processing is a subclass of signal processing concerned specifically with pictures. Digital Image Processing, process digital images by means
More informationPERFORMANCE EVALUATION OFADVANCED LOSSLESS IMAGE COMPRESSION TECHNIQUES
PERFORMANCE EVALUATION OFADVANCED LOSSLESS IMAGE COMPRESSION TECHNIQUES M.Amarnath T.IlamParithi Dr.R.Balasubramanian M.E Scholar Research Scholar Professor & Head Department of Computer Science & Engineering
More informationCorrection of Clipped Pixels in Color Images
Correction of Clipped Pixels in Color Images IEEE Transaction on Visualization and Computer Graphics, Vol. 17, No. 3, 2011 Di Xu, Colin Doutre, and Panos Nasiopoulos Presented by In-Yong Song School of
More informationAutomatic Electricity Meter Reading Based on Image Processing
Automatic Electricity Meter Reading Based on Image Processing Lamiaa A. Elrefaei *,+,1, Asrar Bajaber *,2, Sumayyah Natheir *,3, Nada AbuSanab *,4, Marwa Bazi *,5 * Computer Science Department Faculty
More informationA comparative study of different feature sets for recognition of handwritten Arabic numerals using a Multi Layer Perceptron
Proc. National Conference on Recent Trends in Intelligent Computing (2006) 86-92 A comparative study of different feature sets for recognition of handwritten Arabic numerals using a Multi Layer Perceptron
More informationIJRASET 2015: All Rights are Reserved
A Novel Approach For Indian Currency Denomination Identification Abhijit Shinde 1, Priyanka Palande 2, Swati Kamble 3, Prashant Dhotre 4 1,2,3,4 Sinhgad Institute of Technology and Science, Narhe, Pune,
More informationA Review of Optical Character Recognition System for Recognition of Printed Text
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 3, Ver. II (May Jun. 2015), PP 28-33 www.iosrjournals.org A Review of Optical Character Recognition
More informationMatlab Based Vehicle Number Plate Recognition
International Journal of Computational Intelligence Research ISSN 0973-1873 Volume 13, Number 9 (2017), pp. 2283-2288 Research India Publications http://www.ripublication.com Matlab Based Vehicle Number
More informationENHANCHED PALM PRINT IMAGES FOR PERSONAL ACCURATE IDENTIFICATION
ENHANCHED PALM PRINT IMAGES FOR PERSONAL ACCURATE IDENTIFICATION Prof. Rahul Sathawane 1, Aishwarya Shende 2, Pooja Tete 3, Naina Chandravanshi 4, Nisha Surjuse 5 1 Prof. Rahul Sathawane, Information Technology,
More informationIntegrated Digital System for Yarn Surface Quality Evaluation using Computer Vision and Artificial Intelligence
Integrated Digital System for Yarn Surface Quality Evaluation using Computer Vision and Artificial Intelligence Sheng Yan LI, Jie FENG, Bin Gang XU, and Xiao Ming TAO Institute of Textiles and Clothing,
More informationInternational Journal of Innovative Research in Engineering Science and Technology APRIL 2018 ISSN X
HIGH DYNAMIC RANGE OF MULTISPECTRAL ACQUISITION USING SPATIAL IMAGES 1 M.Kavitha, M.Tech., 2 N.Kannan, M.E., and 3 S.Dharanya, M.E., 1 Assistant Professor/ CSE, Dhirajlal Gandhi College of Technology,
More informationReceived on: Accepted on:
ISSN: 0975-766X CODEN: IJPTFI Available Online through Research Article www.ijptonline.com AUTOMATIC FLUOROGRAPHY SEGMENTATION METHOD BASED ON HISTOGRAM OF BRIGHTNESS SUBMISSION IN SLIDING WINDOW Rimma
More informationDESIGN & DEVELOPMENT OF COLOR MATCHING ALGORITHM FOR IMAGE RETRIEVAL USING HISTOGRAM AND SEGMENTATION TECHNIQUES
International Journal of Information Technology and Knowledge Management July-December 2011, Volume 4, No. 2, pp. 585-589 DESIGN & DEVELOPMENT OF COLOR MATCHING ALGORITHM FOR IMAGE RETRIEVAL USING HISTOGRAM
More informationImproved color image segmentation based on RGB and HSI
Improved color image segmentation based on RGB and HSI 1 Amit Kumar, 2 Vandana Thakur, Puneet Ranout 1 PG Student, 2 Astt. Professor 1 Department of Computer Science, 1 Career Point University Hamirpur,
More informationAn Enhanced Biometric System for Personal Authentication
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-issn: 2278-2834,p- ISSN: 2278-8735. Volume 6, Issue 3 (May. - Jun. 2013), PP 63-69 An Enhanced Biometric System for Personal Authentication
More information