**8. References**


It is also noted in Table 2 that there is a small character segmentation success advantage in images taken at night compared to images taken in the day time. This can be explained due to the fact that at night, an Infra-Red (IR) camera is used to capture license plate which provides good images due to license plate's reflectivity to IR camera where the other objects

As well as the "bad edges", there are a number of factors that cause license plate character

It is demonstrated from the results that Haar based edges can be used not only to enhance image features but also to give an idea on where the objects of interest are located. The major advantages of Haar edges in LP character segmentation application are: ability to detect most edges in image, higher character segmentation rate on HD images, fewer noises (unwanted edges) when using the appropriate decomposition and threshold levels, and

A licence plate algorithm under 40ms is capable of delivering 25 fps, which is in real-time and able to deal with vehicles moving at 70 miles per hour. Therefore, the results suggest that the proposed algorithm will work in real time with SD and HD images in both PC and

In conclusion, the methodology provides a unified character segmentation process that caters to number plates captured at any time of the day (both day and night), and also different types of noises existing in real World applications, low and high resolution images. It is observed that higher character segmentation rate is at higher decomposition levels; therefore the future work will focus on further DSP optimisation methods for implementing

Abdel-Qader, I. M. & Maddix, M. E. (2005). Edge detection: wavelets versus conventional

Benkrid, A., Crookes D. & K. Benkrid. (2001). Design and Implementation of Generic 2-D

Canny, J. F. (1986). A computational approach to edge detection. I*EEE Trans. on Patt. Anal.* 

Biorthogonal Discrete Wavelet Transform on and FPGA, *IEEE Symposium on* 

Illegal against known rules such as seven characters per LP in the UK

in the background are not captured.

Dirty due to mud or rain drops

segmentation failure including;

 Broken due to accidents Non reflective to IR camera Over exposure or uneven lit

DSP for embedded systems.

**8. References** 

higher level decompositions on both HD and SD images.

*And Machine Intell.* Vol. 8, pp. 679-698.

methods on DSP processors. *In MG&V* 14, 1, 83-101.

*FieldProgrammable Custom Computing Machines*, pp 1 – 9.

**7. Conclusion** 

speed.


**2** 

*Tunisia* 

**Wavelet Transform Based Motion** 

Najib Ben Aoun, Maher El'arbi and Chokri Ben Amar

*University of Sfax, National Engineering School of Sfax (ENIS)* 

*REsearch Groups on Intelligent Machines (REGIM)* 

**Estimation and Compensation for Video Coding** 

With the big evolution in the quantity of video data issued from an increased number of video applications over networks such as the videophone, the videoconferencing, and multimedia devices such as the personal digital assistants and the high-definition cameras, it has become crucial to reduce the quantity of video data which will be stored or transmitted. In fact, since the capacity of the storage Medias has become high and sufficient, the data storage problem was resolved but the transmission of the data remains an

Actually, the necessity of the development of an efficient video coding method has made video compression a fundamental task for video-based digital communications. Video compression reduces the quantity of video data by eliminating the spatial and the temporal redundancy. Spatial compression is done by transforming video frames and representing them otherwise using the spatial correlation between frames pixels. In the other side, motion estimation and compensation are employed in video coding systems to remove temporal redundancy while keeping a high visual quality. They are the most important parts of the video coding process since they require the most computational power and the biggest consumption in resources and bandwidth. Therefore, many techniques have been developed

Motion estimation and compensation (ME/MC) was conducted in many domains such as spatial domain by applying it directly on images pixels without any transformation, the frequency domain by driving it on the Discrete Cosine Transform (DCT) or the Discrete Fourier Transform (DFT) coefficients. It can be also done in the multiresolution domain by running it on the Discrete Wavelet Transform (DWT) coefficients. However, giving the promising performances of the multiresolution analysis especially the DWT which provides a multiresolution expression of the signal with localization in both space and frequency, many methods have been developed to construct a wavelet based video coding system (Shenolikar, 2009) and the DWT was integrated in new coding standards such as JPEG2000, MPEG-4, and H.264. Furthermore, recently, many motion estimation and compensation systems (BEN AOUN, 2010) have also confirmed that the DWT is the most suitable and the

For this, we have developed a block based ME/MC method in the wavelet domain. Our method exploits the benefits of DWT and the hierarchical relationship between its subbands

important problem especially with the limited channel bandwidth.

most efficient domain that gives efficient and precise motion estimation.

to estimate motion between successive frames.

**1. Introduction** 

Wu, M., Wei, J., Shih, H. & Ho, C.C. (2009). "2-Level-Wavelet-Based License Plate Edge Detection," *Information Assurance and Security, 2009. IAS '09. Fifth International Conference on* , vol.2, no., pp.385-388, 18-20.
