License plate recognition system based on SIFT features

Zahedi, Morteza; Salehi, Seyed Mahdi

doi:10.1016/j.procs.2010.12.164

Cited by 30 publications

(30 citation statements)

References 6 publications

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“…Various biometric modalities have been explored previously such as iris, fingerprint, face, and hand. Table 1 shows gender recognition based on the face in [7][8][9][10][11]. In [12][13][14][15], a gender is recognized based on human gait, and it can also be recognized based on footwear, color information, and human speech [16][17][18][19].…”

Section: The Need For Gender Recognition Using Biometricsmentioning

confidence: 99%

“…Method [7] • Sift features [8] • Combination of gabor filters and binary features [9] • Using hybrid of gabor filters and binary features [10] • Face_based gender recognition performance with a fuzzy inference system [11] • Using periocular biometric for gender classification in the wild [12] • From gait_based using mixed conditional random field [13] • Extraction of the hip joint data that was computed from Bio-vision hierarchical data [14] • From gait sequences with arbitrary walking directions [15] • Human gait based gender identification using Hidden Markov Model and Support Vector Machines [16] • Using footwear appearance [17] • Color information [18,19] • Speech recognition This research • Fusion of HRV and EMG signals during stepping exercise by stepper…”

Section: Workmentioning

confidence: 99%

See 1 more Smart Citation

Improved Gender Recognition during Stepping Activity for Rehab Application Using the Combinatorial Fusion Approach of EMG and HRV

et al. 2017

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Gender recognition is trivial for a physiotherapist, but it is considered a challenge for computers. The electromyography (EMG) and heart rate variability (HRV) were utilized in this work for gender recognition during exercise using a stepper. The relevant features were extracted and selected. The selected features were then fused to automatically predict gender recognition. However, the feature selection for gender classification became a challenge to ensure better accuracy. Thus, in this paper, a feature selection approach based on both the performance and the diversity between the two features from the rank-score characteristic (RSC) function in a combinatorial fusion approach (CFA) (Hsu et al.) was employed. Then, the features from the selected feature sets were fused using a CFA. The results were then compared with other fusion techniques such as naive bayes (NB), decision tree (J48), k-nearest neighbor (KNN) and support vector machine (SVM). Besides, the results were also compared with previous researches in gender recognition. The experimental results showed that the CFA was efficient and effective for feature selection. The fusion method was also able to improve the accuracy of the gender recognition rate. The CFA provides much better gender classification results which is 94.51% compared to Barani's work (90.34%), Nazarloo's work (92.50%), and other classifiers.

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Section: The Need For Gender Recognition Using Biometricsmentioning

confidence: 99%

Section: Workmentioning

confidence: 99%

Improved Gender Recognition during Stepping Activity for Rehab Application Using the Combinatorial Fusion Approach of EMG and HRV

et al. 2017

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“…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%

“…In [24], a robust and reliable SIFT based method is used to describe local feature of a number plate. As per Morteza Zahedi et al, the set of features extracted from the training images must be robust to changes in image scale, noise and illumination in order to perform reliable recognition.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Automatic Number Plate Recognition System (ANPR): A Survey

Patel¹,

Shah²,

Patel³

2013

IJCA

147

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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.

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“…Scale invariant feature transform (SIFT) is another method that is robust and reliable which is used to extract key points from images, those are invariant to rotation and scaling from which is mostly used in object tracking and person authorization. The SIFT is used by [11] for localizing license plate and yield recognition rate .84. Template matching is a widely approach especially for license plate recognition [12,13].…”

Section: Paper Reviewmentioning

confidence: 99%

A Novel Approach for Vehicle License Plate Localization and Recognition

Dashtban¹,

Dashtban²,

Bevrani³

2011

IJCA

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In this paper, a general approach for international vehicle license plate localization and recognition is proposed. A hybrid solution is presented with combining basic machine vision techniques and neural networks. The proposed model consists of three main parts, including localization, segmentation and recognition. In the license plate localization, after some essential preprocessing and finding edges, the 8-connectivity of image background eliminates which helps more appropriately separating of main image objects from the cluttered backgrounds. Then, it is tried to find connected objects with 8-connectivity of the differentiated binary image. The binarization of license plate is based on local binarizing. The proposed recognizing system utilizes the Hough transform, basic morphological operators and Skeletonizing to provide an appropriate input for artificial neural networks. Segment by segment, the input streams into an intelligent error control unit (IECU) which itself is an already trained multi-layer perceptron (MLP) neural network. IECU investigates empty or non-character-inside segments. In case of no error, each segment streams into two already trained MLPs. Each of them singly recognizes either the alphabets or numbers. We show that this approach achieves accuracy over 91% on localizing vehicle license plate. The image database includes images of various vehicles with different background and slop under varying illumination conditions. The character recognition system correctly recognizes alphabets with probability over 97% and over 94% in case of numbers.

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License plate recognition system based on SIFT features

Cited by 30 publications

References 6 publications

Improved Gender Recognition during Stepping Activity for Rehab Application Using the Combinatorial Fusion Approach of EMG and HRV

Improved Gender Recognition during Stepping Activity for Rehab Application Using the Combinatorial Fusion Approach of EMG and HRV

Automatic Number Plate Recognition System (ANPR): A Survey

A Novel Approach for Vehicle License Plate Localization and Recognition

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