Study the Method of Vehicle License Locating Based on Color Segmentation

Yang, Yang; Gao, Xuhui; Yang, Guowei

doi:10.1016/j.proeng.2011.08.245

Cited by 21 publications

(15 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The gray image shows the intensity value of the grayscale image, while R, G, and B represent the values of red, green, and blue pixels, respectively [14]. Given that the illumination condition of the images is vital for recognition systems, the heterogeneous distribution of the gray levels of the converted image as a result of the differentiation of the illumination conditions while capturing the image are enhanced.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, numerous scholars have attempted to improve LPL systems, leading to development of many methods such as support vector machine (SVM) [6], wavelet transform [7], [8], [9], Fourier transform [9], edge detection [10], [11], [12], color segmentation [14], and Hough transform [15]. Methods based on color image are sensitive to the features of the input image, indicating that illumination conditions affect the recognition process.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A robust and highly efficient localization method for irregular license plates

Ghaffari

Kitajima

Abdullah

et al. 2014

2014 Ieee Region 10 Symposium

View full text Add to dashboard Cite

This paper presents a robust method for license plate localization (LPL). Basically, the performance of LPL directly affects the accuracy of License Plate Recognition system. Although many studies had been conducted on LPL systems, many of them only discuss on license plate (LP) from countries that have restricted and distinguished LP types in terms of shapes and fonts. In this paper, a novel LPL system is proposed to address difficulties in recognizing extraordinary LP types with respect to shapes and fonts. The system has been implemented using MATLAB in eight different conditions of illumination in an actual environment to illustrate the efficiency of the proposed method. The total accuracy of this system for all conditions is 96% to 97%.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A robust and highly efficient localization method for irregular license plates

Ghaffari

Kitajima

Abdullah

et al. 2014

2014 Ieee Region 10 Symposium

View full text Add to dashboard Cite

show abstract

“…These systems follow different approaches to locate vehicle number plate from vehicle and then to extract vehicle number from that image. Most of the ANPR systems are based on common approaches like artificial neural network (ANN) [5], [1], [6], [7][8], [9], [10], Probabilistic neural network (PNN) [11], Optical Character Recognition (OCR) [5], [12], [2], [13], [7], [14], Feature salient [15], MATLAB [16], Configurable method [17], Sliding concentrating window (SCW) [14], [8], BP neural network [18], support vector machine(SVM) [19], inductive learning [20], region based [21], color segmentation [22], fuzzy based algorithm [23], scale invariant feature transform (SIFT) [24], trichromatic imaging, Least Square Method(LSM) [25], [26], online license plate matching based on weighted edit distance [27] and color-discrete characteristics [28]. A case study of license plate reader (LPR) is well explained in [29].…”

Section: Automatic Number Plate Recognition (Anpr)mentioning

confidence: 99%

“…Some plate segmentation algorithms are based on color segmentation. A study of license plate location based on color segmentation is discussed in [22]. In the following sections common number plate extraction methods are explained, which is followed by detailed discussion of image segmentation techniques adopted in various literature of ANPR or LPR.…”

Section: Number Plate Detectionmentioning

confidence: 99%

Automatic Number Plate Recognition System (ANPR): A Survey

Patel¹,

Shah²,

Patel³

2013

IJCA

134

View full text Add to dashboard Cite

Traffic control and vehicle owner identification has become major problem in every country. Sometimes it becomes difficult to identify vehicle owner who violates traffic rules and drives too fast. Therefore, it is not possible to catch and punish those kinds of people because the traffic personal might not be able to retrieve vehicle number from the moving vehicle because of the speed of the vehicle. Therefore, there is a need to develop Automatic Number Plate Recognition (ANPR) system as a one of the solutions to this problem. There are numerous ANPR systems available today. These systems are based on different methodologies but still it is really challenging task as some of the factors like high speed of vehicle, non-uniform vehicle number plate, language of vehicle number and different lighting conditions can affect a lot in the overall recognition rate. Most of the systems work under these limitations. In this paper, different approaches of ANPR are discussed by considering image size, success rate and processing time as parameters. Towards the end of this paper, an extension to ANPR is suggested.

show abstract

“…The colorful image represented by 3 coefficients red, green and blue from the acquisition unit must be converted to the images with 256 levels of gray scale [1]. Then select an appropriate threshold to achieve the image binarization [2]. Following by converting the grayscale image into binary image which consists of only 0 and 1 [3].…”

Section: Introductionmentioning

confidence: 99%

Identifying the Character by Applying PCA Method using Matlab

Subbuthai¹,

Periasamy²,

Muruganand³

2012

IJCA

View full text Add to dashboard Cite

Optical character recognition is getting more and more useful in daily life for various purposes. The aim of the paper is to find the number and English alphabets in the symbol of times new roman, arial, arial block size of 72, 48.Many researches have been done on many types of characters by using different approaches. In this recognition system was implemented by using of principal component analysis (PCA) algorithm. This algorithm is based on an Eigen value and Euclidean distance. PCA is practical and standard statistical tool in modern data analysis that has found application in different areas such as face recognition, image compression, and neuroscience. General TermsBinary. Edge, filling image and PCA Keywords PCA, Eigen value, Euclidean distance PCA is mathematically defined as an orthogonal linear transformation that transforms the data to a new coordinate system such that the greatest variance by any projection of the data comes to lie on the first coordinate, the second greatest

show abstract

Study the Method of Vehicle License Locating Based on Color Segmentation

Cited by 21 publications

References 2 publications

A robust and highly efficient localization method for irregular license plates

A robust and highly efficient localization method for irregular license plates

Automatic Number Plate Recognition System (ANPR): A Survey

Identifying the Character by Applying PCA Method using Matlab

Contact Info

Product

Resources

About