计量经济分析 = Econometric analysis

Automatic license plate recognition (ALPR) is the extraction of vehicle license plate information from an image or a sequence of images. The extracted information can be used with or without a database in many applications, such as electronic payment systems (toll payment, parking fee payment), and freeway and arterial monitoring systems for traffic surveillance. The ALPR uses either a color, black and white, or infrared camera to take images. The quality of the acquired images is a major factor in the success of the ALPR. ALPR as a real-life application has to quickly and successfully process license plates under different environmental conditions, such as indoors, outdoors, day or night time. It should also be generalized to process license plates from different nations, provinces, or states. These plates usually contain different colors, are written in different languages, and use different fonts; some plates may have a single color background and others have background images. The license plates can be partially occluded by dirt, lighting, and towing accessories on the car. In this paper, we present a comprehensive review of the state-of-the-art techniques for ALPR. We categorize different ALPR techniques according to the features they used for each stage, and compare them in terms of pros, cons, recognition accuracy, and processing speed. Future forecasts of ALPR are given at the end.

https://romisatriawahono.net/lecture/rm/survey/computer%20vision/Du%20-%20Automatic%20License%20Plate%20Recognition%20-%202013.pdf

Automatic License Plate Recognition (ALPR): A State-of-the-Art Review

This paper is one of two companion papers which survey price elasticities of transport demand. In addition to reviewing empirical elasticity estimates for both freight and passenger demand, this paper also deals with theoretical and empirical issues in estimating transport demand elasticities. The second survey, by Dr. Philip Goodwin, is primarily concerned with empirical estimates of demand elasticities of public traansit system and automobile usage. The origins of motivations of these two papers were different. As was the coverage and sources used. The two papers complemented each other and are therefore published together with a pooled bibliography.

Concepts of price elasticities of transport demand and recent empirical estimates: an interpretative survey. in: urban transport

Multimedia security is extremely significant concern for the internet technology because of the ease of the duplication, distribution and manipulation of the multimedia data. The digital watermarking is a field of information hiding which hide the crucial information in the original data for protection illegal duplication and distribution of multimedia data. This paper presents a survey on the existing digital image watermarking techniques. The results of various digital image watermarking techniques have been compared on the basis of outputs. In the digital watermarking the secret information are implanted into the original data for protecting the ownership rights of the multimedia data. The image watermarking techniques may divide on the basis of domain like spatial domain or transform domain or on the basis of wavelets. The spatial domain techniques directly work on the pixels and the frequency domain works on the transform coefficients of the image. This survey elaborates the most important methods of spatial domain and transform domain and focuses the merits and demerits of these techniques.

/pdf/a-survey-digital-image-watermarking-techniques-59n1okuerc.pdf

A Survey: Digital Image Watermarking Techniques

This paper presents a novel convolutional neural network (CNN) -based method for high-accuracy real-time car license plate detection. Many contemporary methods for car license plate detection are reasonably effective under the specific conditions or strong assumptions only. However, they exhibit poor performance when the assessed car license plate images have a degree of rotation, as a result of manual capture by traffic police or deviation of the camera. Therefore, we propose the a CNN-based MD-YOLO framework for multi-directional car license plate detection. Using accurate rotation angle prediction and a fast intersection-over-union evaluation strategy, our proposed method can elegantly manage rotational problems in real-time scenarios. A series of experiments have been carried out to establish that the proposed method outperforms over other existing state-of-the-art methods in terms of better accuracy and lower computational cost.

A New CNN-Based Method for Multi-Directional Car License Plate Detection

Detecting the region of a license plate is the key component of the vehicle license plate recognition (VLPR) system. A new method is adopted in this paper to analyze road images which often contain vehicles and extract LP from natural properties by finding vertical and horizontal edges from vehicle region. The proposed vehicle license plate detection (VLPD) method consists of three main stages: (1) a novel adaptive image segmentation technique named as sliding concentric windows (SCWs) used for detecting candidate region; (2) color verification for candidate region by using HSI color model on the basis of using hue and intensity in HSI color model verifying green and yellow LP and white LP, respectively; and (3) finally, decomposing candidate region which contains predetermined LP alphanumeric character by using position histogram to verify and detect vehicle license plate (VLP) region. In the proposed method, input vehicle images are commuted into grey images. Then the candidate regions are found by sliding concentric windows. We detect VLP region which contains predetermined LP color by using HSI color model and LP alphanumeric character by using position histogram. Experimental results show that the proposed method is very effective in coping with different conditions such as poor illumination, varied distances from the vehicle and varied weather.

Vehicle License Plate Detection Method Based on Sliding Concentric Windows and Histogram

A combined DWT and DCT based watermarking technique with low frequency watermarking with weighted correction is proposed DWT has excellent spatial localization, frequency spread and multi-resolution characteristics, which are similar to the theoretical models of the human visual system (HVS) DCT based watermarking techniques offer compression while DWT based watermarking techniques offer scalability These desirable properties are used in this combined watermarking technique In the proposed method watermark bits are embedded in the low frequency band of each DCT block of selected DWT sub-band The weighted correction is also used to improve the imperceptibility The extracting procedure reverses the embedding operations without the reference of the original image Compared with the similar approach by DCT based approach and DWT based approach, the experimental results show that the proposed algorithm apparently preserves superior image quality and robustness under various attacks such as JPEG compression, cropping, sharping, contrast adjustments and so on

Combined DWT-DCT based digital image watermarking technique for copyright protection

Vehicle license plate recognition (VLPR) is one of the most important topics of using computer vision and pattern recognition in intelligent transportation systems. In order to recognize a license plate (LP) expeditiously, the location of the LP in most cases, must be detected in the initial step. For this reason, detecting the exact and perfect location of a LP from a vehicle image is considered to be the most important and crucial step of a VLPR system, which greatly affects the recognition process and directly influences the accuracy and speed of entire system. In this paper a HSI color based license plate detection method is proposed. In this method, (a) HSI color model is used for detecting candidate regions and (b) vehicle license plate (VLP) regions are verified and detected by using position histogram. In the proposed method, input vehicle images are converted into HSI color images. Then the candidate regions are found by HSI color model on the basis of using hue, saturation and/or intensity. These candidate regions may include LP regions; geometrical properties of LP are then used for classification. Finally, VLP regions containing predetermined LP alphanumeric character are verified and detected by using position histogram. The proposed method is very effective in coping with different conditions such as poor illumination and varied weather comparing with traditional approaches. Experimental results show that the distance from the vehicle varied according to the camera setup.

HSI color based vehicle license plate detection

Shadows in an image can reveal information about the object’s shape and orientation, and even about the light source. Thus shadow detection and removal is a very crucial and inevitable task of some computer vision algorithms for applications such as image segmentation and object detection and tracking. This paper proposes a simple framework using the luminance, chroma: blue, chroma: red (YCbCr) color space to detect and remove shadows from images. Initially, an approach based on statistics of intensity in the YCbCr color space is proposed for detecting shadows. After the shadows are identified, a shadow density model is applied. According to the shadow density model, the image is segmented into several regions that have the same density. Finally, the shadows are removed by relighting each pixel in the YCbCr color space and correcting the color of the shadowed regions in the red-green-blue (RGB) color space. The most salient feature of our proposed framework is that after removing shadows, there is no harsh transition between the shadowed parts and non-shadowed parts, and all the details in the shadowed regions remain intact. Various shadow images were used with a variety of conditions (i.e. outdoor and semi-indoor) to test the proposed framework, and results are presented to prove its effectiveness.

Shadow Detection and Removal Based on YCbCr Color Space

https://cyberleninka.org/article/n/358600.pdf

Kaushik Deb

Papers

Vehicle License Plate Detection Method Based on Sliding Concentric Windows and Histogram

Combined DWT-DCT based digital image watermarking technique for copyright protection

HSI color based vehicle license plate detection

Shadow Detection and Removal Based on YCbCr Color Space

An Efficient Method of Vehicle License Plate Recognition Based on Sliding Concentric Windows and Artificial Neural Network