A novel license plate character segmentation method for different types of vehicle license plates.

License plate character segmentation (LPCS) is a very important part of vehicle license plate recognition (LPR) system. The accuracy of LPR system widely depends on two parts; namely license plate detection (LPD) and LPCS. Different country has different types and shapes of LPs are available. Based on character position on LP, we can find two types of LPs over the world, single row (SR) and double rows (DR) LP. Most of the LPCS methods are generally used for SRLP. This paper proposed a novel LPCS method for SR and DR types of LPs. Experimental results shows the real-time effectiveness of our proposed method. The accuracy of our proposed LPCS method is 99.05% and the average computational time is 27ms which is higher than other existing methods

Segmentation and recognition of Korean vehicle license plate characters based on the global threshold method and the cross-correlation matching algorithm.

The vehicle license plate recognition (VLPR) system analyzes and monitors the speed of vehicles, theft of vehicles, the violation of traffic rules, illegal parking, etc., on the motorway. The VLPR consists of three major parts: license plate detection (LPD), license plate character segmentation (LPCS), and license plate character recognition (LPCR). This paper presents an efficient method for the LPCS and LPCR of Korean vehicle license plates (LPs). LP tilt adjustment is a very important process in LPCS. Radon transformation is used to correct the tilt adjustment of LP. The global threshold segmentation method is used for segmented LP characters from two different types of Korean LPs, which are a single row LP (SRLP) and double row LP (DRLP). The cross-correlation matching method is used for LPCR. Our experimental results show that the proposed methods for LPCS and LPCR can be easily implemented, and they achieved 99.35% and 99.85% segmentation and recognition accuracy rates, respectively for Korean LPs

Detection and recognition of illegally parked vehicles based on an adaptive gaussian mixture model and a seed fill algorithm.

In this paper, we present an algorithm for the detection of illegally parked vehicles based on a combination of some image processing algorithms. A digital camera is fixed in the illegal parking region to capture the video frames. An adaptive Gaussian mixture model (GMM) is used for background subtraction in a complex environment to identify the regions of moving objects in our test video. Stationary objects are detected by using the pixel-level features in time sequences. A stationary vehicle is detected by using the local features of the object, and thus, information about illegally parked vehicles is successfully obtained. An automatic alarm system can be utilized according to the different regulations of different illegal parking regions. The results of this study obtained using a test video sequence of a real-time traffic scene show that the proposed method is effective

Modeling and Implementing Two-Stage AdaBoost for Real-Time Vehicle License Plate Detection

License plate (LP) detection is the most imperative part of the automatic LP recognition system. In previous years, different methods, techniques, and algorithms have been developed for LP detection (LPD) systems. This paper proposes to automatical detection of car LPs via image processing techniques based on classifier or machine learning algorithms. In this paper, we propose a real-time and robust method for LPD systems using the two-stage adaptive boosting (AdaBoost) algorithm combined with different image preprocessing techniques. Haar-like features are used to compute and select features from LP images. The AdaBoost algorithm is used to classify parts of an image within a search window by a trained strong classifier as either LP or non-LP. Adaptive thresholding is used for the image preprocessing method applied to those images that are of insufficient quality for LPD. This method is of a faster speed and higher accuracy than most of the existing methods used in LPD. Experimental results demonstrate that the average LPD rate is 98.38% and the computational time is approximately 49 ms

Climatic yield potential of Japonica???type rice in the Korean Peninsula under RCP scenarios using the ensemble of multi???GCM and multi???RCM chains

Rice production in the Korean Peninsula (KP) in the near future (2021-2050) is analysed in terms of the climatic yield potential (CYP) index for Japonica-type rice. Data obtained from the dynamically downscaled daily temperature and sunshine duration for the Historical period (1981-2010) and near future under two Representative Concentration Pathway (RCP4.5 and RCP8.5) scenarios are utilized. To reduce uncertainties that might be induced by using a Coupled General Circulation Model (CGCM)-a Regional Climate Model (RCM) chain in dynamical downscaling, two CGCM-three RCM chains are used to estimate the CYP index. The results show that the mean rice production decreases, mainly due to the increase of the temperature during the grain-filling period (40 days after the heading date). According to multi model ensemble, the optimum heading date in the near future will be approximately 12 days later and the maximum CYP will be even higher than in the Historical. This implies that the rice production is projected to decrease if the heading date is selected based on the optimum heading date of Historical, but to increase if based on that of near future. The mean rice production during the period of ripening is projected to decrease (to about 95% (RCP4.5) and 93% (RCP8.5) of the Historical) in the western and southern regions of the KP, but to increase (to about 104% (RCP4.5) and 106% (RCP8.5) of the Historical) in the northeastern coastal regions of the KP. However, if the optimum heading date is selected in the near future climate, the peak rice production is projected to increase (to about 105% (RCP4.5) and 104% (RCP8.5) of the Historical) in the western, southern and northeastern coastal regions of the KP, but to decrease (to about 98% (RCP4.5) and 96% (RCP8.5) of the Historical) in the southeastern coastal regions of the KP

Real-time vehicle license plate detection based on background subtraction and cascade of boosted classifiers

License plate (LP) detection is the most imperative part of an automatic LP recognition (LPR) system. Typical LPR contains two steps, namely LP detection (LPD) and character recognition. In this paper, we propose an efficient Vehicle-to-LP detection framework which combines with an adaptive GMM (Gaussian Mixture Model) and a cascade of boosted classifiers to make a faster vehicle LP detector. To develop a background model by using a GMM is possible in the circumstance of a fixed camera and extracts the motions using background subtraction. Firstly, an adaptive GMM is used to find the region of interest (ROI) on which motion detectors are running to detect the vehicle area as blobs ROIs. Secondly, a cascade of boosted classifiers is executed on the blobs ROIs to detect a LP. The experimental results on our test video with the resolution of 720×576 show that the LPD rate of the proposed system is 99.14% and the average computational time is approximately 42ms

Receding Horizon Chaos Synchronization Method

This article proposes a new synchronization method, called a receding horizon synchronization (RHS) method, for a general class of chaotic systems. A new linear matrix inequality (LMI) condition on the finite terminal weighting matrix is proposed for chaotic systems under which non-increasing monotonicity of the optimal cost is guaranteed. It is shown that the proposed terminal inequality condition guarantees the closed-loop stability of the RHS method for chaotic systems. As an application of the proposed method, the RHS problem for Chua’s chaotic system is investigated

Evaluation of climatological tropical cyclone activity over the western North Pacific in the CORDEX-East Asia multi-RCM simulations

The ability of five regional climate models (RCMs), within the Coordinated Regional Climate Downscaling Experiment (CORDEX) for East Asia, to simulate tropical cyclone (TC) activity over the western North Pacific is evaluated. All RCMs are performed at ~50 km resolution over the CORDEX-East Asia domain, and are driven by the ECMWF Interim Re-Analysis (ERA-Interim) for the period 1989-2008. ERA-Interim sea surface temperature is prescribed as the lower boundary. Performances of the individual RCMs and multi-RCM ensemble mean are investigated in detail for 20-year climatology, intensity, and interannual variability of TC activity compared to observational datasets. Although most of the individual RCMs show significant biases and underestimate TC intensity due to horizontal resolutions still too low to resolve the most intense observed TCs, they reasonably capture the observed climatological spatial distribution and interannual variability of TC activity. The multi-RCM ensemble mean based on the model performance generally outperforms most of the individual models with smaller biases and higher correlation on the spatial and temporal variation of TC activity. This ensemble mean reduces the uncertainty in the simulated TC activity by a single RCM. These analyses suggest that the multi-RCM ensemble within CORDEX-East Asia can be applied to provide more reliable and credible estimation of future TC activity over the western North Pacific due to climate change. © 2015 Springer-Verlag Berlin Heidelberclose