A vision-based machine learning method for barrier access control using vehicle license plate authentication

Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, the image is processed so that a region of interest is identified. In the recognition stage, features are extracted from the region of interest using the histogram of oriented gradients method. These features are then used to train an artificial neural network to identify characters in the license plate. Experimental results show that the proposed method achieves a high level of accuracy as well as low processing time when compared to existing methods, indicating that it is suitable for real-time applications

Automatic Identification of Personal Automobiles Plates of Iran Using Genetic Algorithm

In this study, a new method for using LPR systems for Iranian plates number has been presented. Increasing the precision of the letter recognition process and reducing the amount of training are in fact the main advantages of the new hybrid model. The K-NN has been implemented as the first classification method, because it was simple, and it was resistant to the noisy data, and for large datasets it is also effective at zero cost. The confusion problem related to the similarity of letters in plate numbers has also been resolved by using the classification model of the multi-class genetic algorithm. The genetic algorithm improves K-NN performance in the recognition of similar letters. Vehicle license plate recognition (LPR) plays an important role in ITS and is mainly used in access control systems.The purpose of this research is to determine the Iranian plate automobiles that are specifically owned by the automobile. The confusion caused by the similarity between the letters of the alphabet and numeric characters is one of the problems of the Persian LPR systems at the diagnostic stage. In this regard, a method using the KNN-based advantages of genetic algorithm as a hybrid model is presented in this study to overcome the above problem. The genetic algorithm has been trained and tested only with the same letters, thus the cost of training for the genetic algorithm has significantly decreased. Comparison of the results obtained from the experiments carried out in this study with the results of a similar study shows that the combined KNN-genetic algorithm model significantly improved the detection rate of the letters for all tested cases from 94% to 97.03% . Keywords: Coding, plate recognition, genetics, Iran automobile, Genetic Algorithm DOI: 10.7176/CEIS/10-6-04 Publication date:July 31st 2019

Robust search-free car number plate localization incorporating hierarchical saliency

There are two major shortcomings associated with presently implemented automatic license plate recognition (ALPR) systems: first, processing images with complex background is time-consuming and second, the results are not sufficiently accurate. To overcome these problems and also to achieve a robust recognition of multiple car number plates, saliency detection based on the ALPR system is used in this paper and also an improved and more effective definition of saliency is presented. In this new approach, the notion of the directionality of the edges using Gabor filtering and the detection of the patterns of numbers using L1 -norm have been added to the traditional saliency detection method. The proposed algorithm was tested on 660 images; some consisting of two or more cars. A detection accuracy of 94.77% and an average execution time of 40 ms for 600 × 800 images are the marked outcomes. The proposed SB-ALPR method outperforms most of the state of the art techniques in terms of execution time and accuracy, and can be used in real-time applications. Also, unlike some recently introduced saliency-based ALPR methods, our two-stage saliency detection approach exploits smaller numbers of sample sizes to reduce the computation cost

License plate localization based on statistical measures of license plate features

— License plate localization is considered as the most important part of license plate recognition system. The high accuracy rate of license plate recognition is depended on the ability of license plate detection. This paper presents a novel method for license plate localization bases on license plate features. This proposed method consists of two main processes. First, candidate regions extraction step, Sobel operator is applied to obtain vertical edges and then potential candidate regions are extracted by deploying mathematical morphology operations [5]. Last, license plate verification step, this step employs the standard deviation of license plate features to confirm license plate position. The experimental results show that the proposed method can achieve high quality license plate localization results with high accuracy rate of 98.26 %

High dimensionality carrierless amplitude phase modulation technique for radio over fiber system

Advanced modulation formats such as carrierless amplitude phase (CAP) modulation technique is one of the solutions to increase flexibility and high bit rates to support multi-level and multi-dimensional modulations with the absence of sinusoidal carrier. Recent work are focussing on the 2D CAP-64 QAM Radio-over-Fiber (RoF) system but no extension of higher dimensions is reported. This thesis expands the area of CAP modulation technique and RoF system. The work described in this thesis is devoted to the investigation of 1.25 GSa/s sampling rate for multi-level and multi-dimensional CAP in point-to-point (P2P) and RoF system at 3 km single-mode fiber (SMF). Another advanced modulation format which is known as discrete multitone (DMT) is compared with CAP modulation in order to observe the performance in different modulation schemes. The 4QAM-DMT and 16QAM-DMT at different number of subcarriers are carried out in this propagation. Based on the results, the transmission performance in terms of BER and received optical power for RoF transmission are degraded to almost 3 dB when comparing to 3 km SMF transmission. These are caused by the wireless power loss and impairment effects. The bit rate and spectral efficiency can be increased with the increasing number of levels, and may decreased once the number of dimensions is increased due to the higher up-sampling factor. However, the additional dimensions can be used to support multiple service applications. Therefore, it can be concluded that CAP has better performance as compared to DMT in terms of higher spectral efficiency and data rate. To conclude, the results presented in this thesis exhibit high feasibility of CAP modulation in the increasing number of dimensions and levels. Thus, CAP has the potential to be utilized in multiple service allocations for different number of users

Shape descriptors for mode-shape recognition and model updating

The most widely used method for comparing mode shapes from finite elements and experimental measurements is the Modal Assurance Criterion (MAC), which returns a single numerical value and carries no explicit information on shape features. New techniques, based on image processing (IP) and pattern recognition (PR) are described in this paper. The Zernike moment descriptor (ZMD), Fourier descriptor (FD), and wavelet descriptor (WD), presented in this article, are the most popular shape descriptors having properties that include efficiency of expression, robustness to noise, invariance to geometric transformation and rotation, separation of local and global shape features and computational efficiency. The comparison of mode shapes is readily achieved by assembling the shape features of each mode shape into multi-dimensional shape feature vectors (SFVs) and determining the distances separating them. © 2009 IOP Publishing Ltd

Optimisation of the enzyme-linked lectin assay for enhanced glycoprotein and glycoconjugate analysis

Lectin’s are proteins capable of recognising and binding to specific oligosaccharide tructures found on glycoproteins and other biomoloecules. As such they have found tility for glycoanalytical applications. One common difficulty encountered in the pplication of these proteins, particularly in multi-well plate assay formats known as Enzyme Linked Lectin Assays (ELLA’s), is in finding appropriate blocking solutions to prevent non-specific binding with plate surfaces. Many commonly used blocking agents contain carbohydrates and generate significant background signals in ELLA’s, limiting the utility of the assay. In this study we examined the suitability of a range of blocking reagents, including rotein based, synthetic and commercially available carbohydrate free blocking eagents, for ELLA applications. Each blocking reagent was assessed against a panel f 19 commercially available biotinylated lectins exhibiting diverse structures and arbohydrate specificities. We identified the synthetic polymer Polyvinyl Alcohol PVA) as the best global blocking agent for performing ELLA’s. We ultimately present n ELLA methodology facilitating broad spectrum lectin analysis of glycoconjugates nd extending the utility of the ELLA

Modeshapes recognition using Fourier descriptors: a simple SHM example

The main objective of this study is to develop an alternative criterion for modeshape classification, as the currently available one, MAC (Modal Assurance Criteria), is only a vector correlation representing modeshape similarities. This new method is developed to provide a set of features (Fourier Descriptors) for comparing modeshapes with “local” similarities of higher interest than “global” similarities using nodal lines. These lines are able to characterize modeshapes very easily. So when damage occurs, we are able to track the few descriptors changes to localise the damage. We validated our method on a CFCF plate demonstrating the quality of the damage localisation and possible use in a “mode tracking” application (space structure)