A Fast Vertical Edge Detection Algorithm for Car License Plate Detection

Recently, License Plate Detection (LPD) has been used in many applications especially in transportation systems. Many methods have been proposed in order to detect license plates, but most of them worked under restricted conditions, such as fixed illumination, stationary background, and high resolution images. LPD plays an important role in Car License Plate Recognition (CLPR) system because it affects the system's accuracy. This thesis aims to propose a fast vertical edge detector using Vertical Edge Detection Algorithm (VEDA) and to build a Car License Plate Detection (CLPD) method. Pre-processing step is performed in order to enhance and initialize the input image for the next steps. This step is divided into three processes: First, the color image conversion to a gray scale image. Second, an adaptive thresholding is used in order to constitute a binarized image. Third, Unwanted Lines Elimination Algorithm (ULEA) is used in order to enhance the image. The next step is to extract the vertical edges from the 352x288 resolution image by using VEDA. This algorithm is based on the contrast between the values in the binarized image. VEDA is proposed in order to enhance the CLPD method computation time. After the vertical edges have been extracted by VEDA, a morphological operation is used to highlight the vertical details in the image. Next, candidate regions are extracted. Finally, the license plate area is detected. This thesis shows that VEDA is faster than Sobel operator; the results reveal that VEDA is faster than Sobel by about 5-9 times, this range depends on the image resolution. The proposed CLPD method can efficiently detect the license plate area. The method shows the total time of processing one 352x288 image is 47.7 ms, and it meets the requirement of real time processing. Under the experiment datasets, which were taken from real scenes, 579 from 643 images are successfully detected. The average accuracy of car license plate detection is 90%. For more evaluation and comparison purposes, the proposed CLPD method is compared with a similar Malaysian license plate detection method, which is CAR Plate Extraction Technology (CARPET). This comparison reveled that the CLPD method is more efficient than CARPET and also has more detection rate

Car license plate detection method for Malaysian plates-styles by using a web camera

Recently, license plate detection has been used in many applications especially in transportation systems. Many methods have been proposed in order to detect license plates, but most of them work under restricted conditions such as fixed illumination, stationary background, and high resolution images. License plate detection plays an important role in car license plate recognition systems because it affects the accuracy and processing time of the system. This work aims to build a Car License Plate Detection (CLPD) system at a lower cost of its hardware devices and with less complexity of algorithms' design, and then compare its performance with the local CAR Plate Extraction Technology (CARPET). As Malaysian plates have special design and they differ from other international plates, this work tries to compare two likely-design methods. The images are taken using a web camera for both the systems. One of the most important contributions in this paper is that the proposed CLPD method uses Vertical Edge Detection Algorithm (VEDA) to extract the vertical edges of plates. The proposed CLPD method can work to detect the region of car license plates. The method shows the total time of processing one 352x288 image is 47.7 ms, and it meets the requirement of real time processing. Under the experiment datasets, which were taken from real scenes, 579 out of 643 images were successfully detected. Meanwhile, the average accuracy of locating car license plate was 90%. In this work, a comparison between CARPET and the proposed CLPD method for the same tested images was done in terms of detection rate and efficiency. The results indicated that the detection rate was 92% and 84% for the CLPD method and CARPET, respectively. The results also showed that the CLPD method could work using dark images to detect license plates, whereas CARPET had failed to do so

A new vertical edge detection algorithm and its application

Edge detection is a very important process for many image processing applications, especially in Car License Plate Detection and Recognition Systems(CLPDRS). The need to distinguish the desired details is a very important pre-process in order to give good results in short time processing. We proposed a new and fast vertical edge detection algorithm (VEDA) which is based on the contrast between the gray scale values. Once, input gray image was binarized by using adaptive threshold, unwanted lines elimination algorithm (ULEA) was proposed and applied. After that, a VEDA was applied for experimental images. Then, implementation on the application is performed and discussed in order to confirm that VEDA is robust for highlighting license plate details easily. The results revealed accurate edge detection performance and demonstrated the great efficiency of using VEDA in order to highlight license plate details. Finally, VEDA showed that it is faster than Sobel operator by about 7-9 times

Vertical-edge-based car-license-plate detection method

This paper proposes a fast method for car-license-plate detection (CLPD) and presents three main contributions. The first contribution is that we propose a fast vertical edge detection algorithm (VEDA) based on the contrast between the grayscale values, which enhances the speed of the CLPD method. After binarizing the input image using adaptive thresholding (AT), an unwanted-line elimination algorithm (ULEA) is proposed to enhance the image, and then, the VEDA is applied. The second contribution is that our proposed CLPD method processes very-low-resolution images taken by a web camera. After the vertical edges have been detected by the VEDA, the desired plate details based on color information are highlighted. Then, the candidate region based on statistical and logical operations will be extracted. Finally, an LP is detected. The third contribution is that we compare the VEDA to the Sobel operator in terms of accuracy, algorithm complexity, and processing time. The results show accurate edge detection performance and faster processing than Sobel by five to nine times. In terms of complexity, a big-O-notation module is used and the following result is obtained: The VEDA has less complexity by K2 times, whereas K2 represents the mask size of Sobel. Results show that the computation time of the CLPD method is 47.7 ms, which meets the real-time requirements

The effect of precursor concentration on the particle size, crystal size, and optical energy gap of CexSn1â’xO2 nanofabrication

In the present work, a thermal treatment technique is applied for the synthesis of CexSn1−xO2 nanoparticles. Using this method has developed understanding of how lower and higher precursor values affect the morphology, structure, and optical properties of CexSn1−xO2 nanoparticles. CexSn1−xO2 nanoparticle synthesis involves a reaction between cerium and tin sources, namely, cerium nitrate hexahydrate and tin (II) chloride dihydrate, respectively, and the capping agent, polyvinylpyrrolidone (PVP). The findings indicate that lower x values yield smaller particle size with a higher energy band gap, while higher x values yield a larger particle size with a smaller energy band gap. Thus, products with lower x values may be suitable for antibacterial activity applications as smaller particles can diffuse through the cell wall faster, while products with higher x values may be suitable for solar cell energy applications as more electrons can be generated at larger particle sizes. The synthesized samples were profiled via a number of methods, such as scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FT-IR). As revealed by the XRD pattern analysis, the CexSn1−xO2 nanoparticles formed after calcination reflect the cubic fluorite structure and cassiterite-type tetragonal structure of CexSn1−xO2 nanoparticles. Meanwhile, using FT-IR analysis, Ce-O and Sn-O were confirmed as the primary bonds of ready CexSn1−xO2 nanoparticle samples, whilst TEM analysis highlighted that the average particle size was in the range 6−21 nm as the precursor concentration (Ce(NO3)3·6H2O) increased from 0.00 to 1.00. Moreover, the diffuse UV-visible reflectance spectra used to determine the optical band gap based on the Kubelka–Munk equation showed that an increase in x value has caused a decrease in the energy band gap and vice versa

Cellular Automata-based Algorithm for Liquid Diffusion Phenomenon Modeling using imaging technique

Recently, the prediction of the dynamical behavior of Liquid Diffusion Phenomenon (LDP) has been used in many applications especially in physical and biological fields. Many models have been proposed to predict the LDP behavior, but most of them require complex mathematical calculations causing computation time consumption. This thesis proposes a dynamical behavior prediction algorithm using Cellular Automata (CA) to model the LDP. A real liquid diffusion phenomenon is recorded whereas the observed images are later extracted for comparing purpose with the predicted phenomenon. First, a mathematical method is proposed in order to track and then analyze the real diffusion behavior. This method has used thousands of original images. Then, thousands of images, as the same number of original images, are created by the CA-based algorithm. In this study, the diffusion speed of the predicted LDP is also computed by using a mathematical proposed algorithm which is the Diffusion Speed Algorithm (DSA). Finally, three benchmark strategies are used in order to compare the predicted images to the original images, which are:pixel intensity, Region-of-Diffusion (ROD) area, and ROD shape. The experiments of this thesis are divided into original and predicted images. The original images are classified into three groups based on the temperature used, which are: ±18 °C, ±24 °C, and ±30 °C. Each temperature-based experiment contains five levels of the height of droplets source. The diffusion time has been equal to 32 seconds with 15 fps comprising 480 images per each experiment. On the other hand,the predicted images are similarly classified. There will be 15 predicted experiments created by the proposed CA algorithm. The whole predicted images are compared to their corresponding original ones. Under the experiments samples, there are 30 processed experiments comprising 14400 original and predicted images. The obtained results show that the averaged similarity percentage is equal to 94.4%. Additionally,the average computation time needed to complete processing a single experiment is 1.3 second. The result obtained from the proposed LDP model compared to other competitive LDP models has higher accuracy and less computation time. The results also show that the proposed LDP model is about 15 times faster than a neural network-based model. A detailed study to explore the effects and relationships between the model‟s parameters such as temperature and liquids‟ viscosity has been performed. The results showed that there is a direct relationship between the temperature and the diffusion speed

Radiation-induced synthesis, electrical and optical characterization of conducting polyaniline of PANI/ PVA composites

This study employs the gamma radiation technique to produce conducting polyaniline from an aqueous solution. The solution comprises four materials: ANI, PVA, CaCl2 and distilled H2O, which serve as a binder or film support, precursor, Cl ions provider, and a solvent. To obtain the conducting polyaniline (PANI/PVA) composite, a film of ANI/ PVA/ CaCl2 was subjected to gamma radiation under ambient conditions. Conducting polyaniline composite was seen to form once the colour began to change from white to green in accordance with the doses of radiation. The results demonstrated that the level of gamma radiation increased to 30 KGy, thereby confirming that the electrical insulation ANI/ PVA/ CaCl2 film was converted into electrically conductive polyaniline composite film. In addition, the measurements of UV–Visible spectrophotometer revealed an absorption band of 795 nm rises alongside the radiation dose which produced PANI/PVA with a green hue that increases in intensity in accordance with rises in the doses of radiation

Morphological, structural and optical behaviour of PVA capped binary (NiO)0.5 (Cr2O3)0.5 nanoparticles produced via single step based thermal technique

In this research study, a thermal treatment approach based on a novel single-step has been utilised to addresses the synthesis of binary NiO0.5 Cr2O3 0.5 nanoparticles. Characteristics of these binary metal oxide nanoparticles were examined by employing appropriate characterization tools. Sample patterns of X-ray diffractions were used, calcined with temperature (Temp), set to 500 °C revealed the existence of face-centred cubic (fcc) and hexagonal crystalline structures (hcs). It was noted that with a rising calcination temperature, the nanoparticle dispersal was enhanced further. On the other hand, TEM micrographs have been used to calculate the size of the mean particle. It was found that there was a rising tendency with the increased calcination temperature and this the growth of the mean particle. Increased particle size inherited a rise of nanoparticles' photoluminescence intensity, as suggested by recorded spectra, and various energy band gaps. This result would have been reduced as an effect once calcination temperature was increased. Resulting NiO0.5 Cr2O3 0.5 nanoparticles could be used in the realm of semiconductors and energy applications