Vehicle plate localization and extraction based on hough transform and bilinear operations

Abstract

<p>In general, the extraction of the vehicle plate is a previous step of plate recognition, and it actively studied for several decades. Plate localization is used in various security and traffic applications. In this paper, the proposed method is efficient to localize a plate for the multinational countries. The proposed method consists of three levels. The first level is the preprocessing<strong> </strong>that contains several steps. The digital camera capture images have been taken about twenty meters from the car with zooming two to three meters. Images are resampled using the zooming technique (bilinear interpolation) that makes the dimension of image (1024 x 768) pixels. The resampled images are resized to (940x 730). These images are converted to grayscale (green channel), and the bilateral filter is applied for removing noise. The second level is plate detection that adopts morphological operations, image subtraction, and vertical edge detection (Sobel). At the last Connected component analysis and Hough transform are used. The third level is the Deskew plate that notifies the plate may skew so that Hough transform is used to detect the largest line segment. Then, the images are rotated using bilinear interpolation. About 860 images are tested for different countries (Iraq, Belarus, Armenia, Hungary), and the accuracy is 98.99 % for extraction of the plate and 100% for the Deskew plate. Thus, the proposed system shows high efficiency in achievement.</p>

An experimental evaluation of localization methods used in wireless sensor networks

“…The mobile node measures the RSSI value for each beacon when a neighbor node responds to the Hello message. The distance between mobile nodes and neighbor anchors is calculated using (1). By resolving the equation system created by ( 2) and (3), we obtain the mobile node position.…”

“…With the proliferation of smart objects, localization has become a critical component in deploying future IT services and applications. These future applications will mainly involve the exchange of time-sensitive, fresh and regular information for monitoring and control, as in the case of autonomous vehicles [1]. Although many localization methods have emerged for ad hoc networks, few of them can adapt to all types of environments and support the multiple constraints of wireless communications, most notably wireless sensor networks.…”

Symmetric quadratic tetration interpolation using forward and backward operation combination

“…In order to obtain the unknown data from its related known data, interpolation is an important method. It has been widely applied in several applications, i.e., signal reconstruction [1]- [9], signal and image denoising [10]- [12], medical image processing [13]- [18], image resizing [19]- [28], image classification [29], image compression [30], image enhancement [31], change detection [32], video processing [33], and more [34], [35]. The interpolation is used to generate the new data points from the discrete set of known data points in case of its position is within the range of known data.…”