Vehicle license plate recognition (VLPR) system has a wide range of applications in real life, such as parking lots, private and public entrances, theft and border control. This paper presents a system overview of client-server license plate recognition system for vehicles in Kurdistan Region of Iraq besides, the vehicle type and city in each plate is identified. The client/server model consist of two components called client-side and server-side. The images are provided by the client side and will be sent over the server to be processed. The process of license plate recognition is done in the server side with the integration with the Matlab software. The proposed recognition system is developed based on digital images and the main component of the system consist of: preprocessing, license plate detection, segmentation and recognition process. A preprocessing step using image enhancement, filtering, and adaptive thresholding is employed. For localizing and extracting the license plate region, Haar wavelet transformation and edge detection algorithm are used. license plate number segmentation is carried out using bounding box based morphological operations and finally color histogram technique and correlation coefficient based similarity measurement are used for recognition task. The experimental results show that our proposed system for the license plate detection and recognition process have obviously higher recognition accuracy rate.
The competitive advantage of aspect oriented programming (AOP) is that it improves the maintainability and understandability of software systems by modularizing crosscutting concerns. However, some concerns, such as logging or debugging, may be overlooked and should be entangled and distributed across the code base. AOP is a software development paradigm that enables developers to capture crosscutting concerns in split-aspect modes. Additionally, it is a novel notion that has the potential to improve the quality of software programs by removing the complexity involved with the production of code tangles via the usage of separation of concerns. As a result, it provides more modularity. Throughout its early development, some believed that AOP was easier to build and maintain than other implementations since it was based on an existing one. The statements are predicated on the premise that local improvements are easier to implement. Additionally, without appropriate visualization tools for both static and dynamic structures, cross-cutting challenges may be difficult for developers and researchers to appreciate. In recent years, AspectJ has begun to enable the depiction of crosscutting concerns via the release of IDE plugins. This article explains aspect oriented programming and how it may be used to improve the readability and maintainability of software projects. Additionally, it will evaluate the challenges it presents to application developers and academics.
Ethnicity identification of face images is of interest in many areas of application. Different from face recognition of individuals, ethnicity identification classifies faces according to the common features of a specific ethnic group. This paper presents a multi-level fusion scheme for ethnicity identification that combines texture features of local areas of a face using local binary patterns with color features using HSV binning. The scheme fuses the decisions from a k-nearest neighbor classifier and a support vector machine classifier into a final identification decision. We have tested the scheme on a collection of face images from a number of publicly available databases. The results demonstrate the effectiveness of the combined features and improvements on accuracy of identification by the fusion scheme over the identification using individual features and other state-of-art techniques.
Mammography is the most effective procedure for the early detection of breast cancer. In this paper an efficient a Computer Aided Diagnosis (CADx) system is proposed to discriminate between benign and malignant. The system comprises mainly of three steps: preprocessing of the images, feature extraction, and finally classification and performance analysis. The case sample mammographic images, originating from the mini MIAS (Mammographic Image Analysis Society) database. In the preprocessing phase the ROI is cropped and resized by 128 x 128. at the very beginning of the feature extraction process, we have applied Haar Wavelet Transform (HWT) for five levels and, in each level, Discrete Cosine Transform applied with various selection of coefficients. After that, different types of features are fed into the feature similarity measure City Block for the diagnosis of breast cancer. The images are of two classes benign and malignant classes. Finally, K-Nearest Number is employed here as a classifier. In our proposed system, we found competitive results.
Ethnicity identification and recognition is a key biometric technology with a wide range of applications related to homeland security, safety, access control, and automatic annotation. Ethnicity identification from face images is a process of gathering facial features of an individual face image compared to existing face images in the dataset to interpretation his/her ethnic class. In this paper, a propose method in multi-level fusion schema for ethnicity identification by using two global features; fast Fourier transform (FFT) and discrete cosine transform (DCT) on the pre-processed face image of size 128 * 128 in YCbCr color space. A dataset is consisting of 750 face image of three different ethnicities (Kurd 300, Oriental 300 and African 150). The query image feature is compared with a dataset image features using k – nearest neighbor classifier using City block distance for evaluating similarity measurement. The experimental result shows good accuracy and demonstrate the effectiveness of the combined features reached an accuracy rate 96.22% of classification.
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