Locally Adaptive Block Thresholding Method with Continuity Constraint

We present an algorithm that enables one to perform locally adaptive block thresholding, while maintaining image continuity. Images are divided into sub-images based some standard image attributes and thresholding technique is employed over the sub-images. The present algorithm makes use of the thresholds of neighboring sub-images to calculate a range of values. The image continuity is taken care by choosing the threshold of the sub-image under consideration to lie within the above range. After examining the average range values for various sub-image sizes of a variety of images, it was found that the range of acceptable threshold values is substantially high, justifying our assumption of exploiting the freedom of range for bringing out local details.Comment: 12 Pages, 4 figures, 1 Tabl

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

Extraction of moving objects from their background based on multiple adaptive thresholds and boundary evaluation

The extraction of moving objects from their background is a challenging task in visual surveillance. As a single threshold often fails to resolve ambiguities and correctly segment the object, in this paper, we propose a new method that uses three thresholds to accurately classify pixels as foreground or background. These thresholds are adaptively determined by considering the distributions of differences between the input and background images and are used to generate three boundary sets. These boundary sets are then merged to produce a final boundary set that represents the boundaries of the moving objects. The merging step proceeds by first identifying boundary segment pairs that are significantly inconsistent. Then, for each inconsistent boundary segment pair, its associated curvature, edge response, and shadow index are used as criteria to evaluate the probable location of the true boundary. The resulting boundary is finally refined by estimating the width of the halo-like boundary and referring to the foreground edge map. Experimental results show that the proposed method consistently performs well under different illumination conditions, including indoor, outdoor, moderate, sunny, rainy, and dim cases. By comparing with a ground truth in each case, both the classification error rate and the displacement error indicate an accurate detection, which show substantial improvement in comparison with other existing methods. © 2010 IEEE.published_or_final_versio

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field