Eleftherios Kayafas

A License Plate-Recognition Algorithm for Intelligent Transportation System Applications

In this paper, a new algorithm for vehicle license plate identification is proposed, on the basis of a novel adaptive image segmentation technique (sliding concentric windows) and connected component analysis in conjunction with a character recognition neural network. The algorithm was tested with 1334 natural-scene gray-level vehicle images of different backgrounds and ambient illumination. The camera focused in the plate, while the angle of view and the distance from the vehicle varied according to the experimental setup. The license plates properly segmented were 1287 over 1334 input images (96.5%). The optical character recognition system is a two-layer probabilistic neural network (PNN) with topology 108-180-36, whose performance for entire plate recognition reached 89.1%. The PNN is trained to identify alphanumeric characters from car license plates based on data obtained from algorithmic image processing. Combining the above two rates, the overall rate of success for the license-plate-recognition algorithm is 86.0%. A review in the related literature presented in this paper reveals that better performance (90% up to 95%) has been reported, when limitations in distance, angle of view, illumination conditions are set, and background complexity is low

License Plate Recognition From Still Images and Video Sequences: A Survey

License plate recognition (LPR) algorithms in images or videos are generally composed of the following three processing steps: 1) extraction of a license plate region; 2) segmentation of the plate characters; and 3) recognition of each character. This task is quite challenging due to the diversity of plate formats and the nonuniform outdoor illumination conditions during image acquisition. Therefore, most approaches work only under restricted conditions such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. Numerous techniques have been developed for LPR in still images or video sequences, and the purpose of this paper is to categorize and assess them. Issues such as processing time, computational power, and recognition rate are also addressed, when available. Finally, this paper offers to researchers a link to a public image database to define a common reference point for LPR algorithmic assessment.

Vehicle Logo Recognition Using a SIFT-Based Enhanced Matching Scheme

In this paper, a new algorithm for vehicle logo recognition on the basis of an enhanced scale-invariant feature transform (SIFT)-based feature-matching scheme is proposed. This algorithm is assessed on a set of 1200 logo images that belong to ten distinctive vehicle manufacturers. A series of experiments are conducted, splitting the 1200 images to a training set and a testing set, respectively. It is shown that the enhanced matching approach proposed in this paper boosts the recognition accuracy compared with the standard SIFT-based feature-matching method. The reported results indicate a high recognition rate in vehicle logos and a fast processing time, making it suitable for real-time applications.

Vehicle model recognition from frontal view image measurements

This paper deals with a novel vehicle manufacturer and model recognition scheme, which is enhanced by color recognition for more robust results. A probabilistic neural network is assessed as a classifier and it is demonstrated that relatively simple image processing measurements can be used to obtain high performance vehicle authentication. The proposed system is assisted by a previously developed license plate recognition, a symmetry axis detector and an image phase congruency calculation modules. The reported results indicate a high recognition rate and a fast processing time, making the system suitable for real-time applications.

Operator context scanning to support high segmentation rates for real time license plate recognition

Introducing high definition videos and images in object recognition has provided new possibilities in the field of intelligent image processing and pattern recognition. However, due to the large amount of information that needs to be processed, the computational costs are high, making the HD systems slow. To this end, a novel algorithm applied to sliding window analysis, namely Operator Context Scanning (OCS), is proposed and tested on the license plate detection module of a License Plate Recognition (LPR) system. In the LPR system, the OCS algorithm is applied on the Sliding Concentric Windows pixel operator and has been found to improve the LPR systems performance in terms of speed by rapidly scanning input images focusing only on regions of interest, while at the same time it does not reduce the system effectiveness. Additionally, a novel characteristic is presented, namely, the context of the image based on a sliding windows operator. This characteristic helps to quickly categorize the environmental conditions upon which the input image was taken. The algorithm is tested on a data set that includes images of various resolutions, acquired under a variety of environmental conditions.

Enhanced partial discharges due to temperature increase in the combined system of a solid-liquid dielectric

Partial discharges which emanate from solid dielectrics immersed in insulating oil when high impulse voltages are applied under different environmental temperatures (20, 40 and 80 degrees C) are discussed. The solid dielectrics used for the measurements are phenol-impregnated pressboard and industrial bakelite. The samples are cut in wafers of 150 mm diameter and are 1 or 2 mm thick. The insulating oil is a typical transformer oil. The switching type of the impulse voltage used, 250/2500 mu s, corresponds to functional situations and is long enough to produce the effects that can be detected without the consideration of charge transfer phenomena due to fast rates of field changes. Experimental results show that the total charge transfer due to partial discharge is increased by temperature. A possible explanation of the undergoing physical process may be that in the low-field regime, temperature perturbation effects on the injected current are characterized by the conductivity changes in the volume of the dielectric, while as partial discharges start occurring, avalanche multiplication of conduction electrons appears to be the dominant phenomenon, characterizing the behavior of a metal/thick insulator/metal structure in the high-field regime. The circuits, measurement, and measuring procedures are discussed. >

Classifying Web pages employing a probabilistic neural network

The paper proposes a system capable of identifying and categorising Web pages on the basis of information filtering. The system is a three-layer probabilistic neural network (PNN) with biases and radial basis neurons in the middle layer and competitive neurons in the output layer. The domain of study involves the e-commerce area. Thus, the PNN scopes to identify e-commerce Web pages and classify them to the respective type according to a framework which describes the fundamental phases of commercial transactions in the Web. The system was tested with many types of Web pages, demonstrating the robustness of the method, since no restrictions were imposed except for the language of the content, which is English. The probabilistic classifier was used for estimating the population of specific e-commerce Web pages. Potential applications involve surveying Web activity in commercial servers, as well as Web page classification in largely expanding information areas like e-government or news and media.

A computer vision approach for textile quality control

Textile manufacturers have to monitor the quality of their products in order to maintain the high-quality standards established for the clothing industry. Thus, textile quality control is a key factor for the increase of competitiveness of their companies. Textile faults have traditionally been detected by human visual inspection. However, human inspection is time consuming and does not achieve a high level of accuracy. Therefore, industrial vision units are of strategic interest for the textile industry as they could form the basis of a system achieving a high degree of accuracy on textile inspection. This work describes the software core of a system designed for fabric inspection on the basis of simple image-processing operations as well as its efficiency on detection of usual textile defects. The prerequisites of the overall system are then discussed analytically, as well as the limitations and the restrictions imposed due to the nature of the problem. The software algorithm and the evaluation of the first results are also presented in details. Copyright

Collaborative e-learning environments enhanced by wiki technologies

E-learning environments have met rapid technological advancements in the previous years. Nevertheless, current e-learning techniques do not adequately support student interaction and collaboration, resulting in decreased student progress and motivation. In this paper, a blended technique combining collaborative forums and wiki technologies is proposed. Through collaborative forums, students discuss course related topics assigned by the tutors to produce new educational material. This material is then stored in the wiki platform for further use. The proposed technique was applied on an e-learning course provided by the National Technical University of Athens and its effectiveness was evaluated using student activity data and questionnaire analysis. Results showed that the technique adequately supported teamwork, increasing student motivation and progress while simultaneously producing satisfactory level educational material.

M-SIFT: A new method for Vehicle Logo Recognition

In this paper, a new algorithm for Vehicle Logo Recognition is proposed, on the basis of an enhanced Scale Invariant Feature Transform (Merge-SIFT or M-SIFT). The algorithm is assessed on a set of 1500 logo images that belong to 10 distinctive vehicle manufacturers. A series of experiments are conducted, splitting the 1500 images to a training set (database) and to a testing set (query). It is shown that the MSIFT approach, which is proposed in this paper, boosts the recognition accuracy compared to the standard SIFT method. The reported results indicate an average of 94.6% true recognition rate in vehicle logos, while the processing time remains low (~0.8sec).