**3. Image processing and ANPR using WT**

4 Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology

The objective of this work is to investigate a suitable hardware that is able to perform image processing algorithms using WT in real time. Processing an image with the WT filter is faster in terms of computational cost in applications such as edge detection where a single filter is capable of producing three types of edges in comparison to standard methods where more than one filter masks are required to achieve the same results. In this section we

GPUs provide programmable vertex and pixel engines that accelerates algorithm mapping such as image processing. An example of a cost effective SIMD algorithm that performs the convolution-based DWT completely on a GPU using a normal PC (baseline processor) is reported by Wong (Wong et al., 2007). It is reported, the algorithm unifies forward and inverse WT to an almost identical process for efficient implementation on the GPU through parallel processing (Wong et al., 2007). This demonstrate that GPUs are capable of processing WT algorithms cost effectively, however it is not suitable for our application,

An example of a scalable FPGA-based architecture for the separable 2-D Biorthogonal Discrete Wavelet Transform (DWT) decomposition is presented by (Benkrid et al., 2001). The architecture is based on the Pyramid Algorithm Analysis, which handles computation along the border efficiently by using the method of symmetric extension using Xilinx Virtex-E (Benkrid et al., 2001). FPGA's are suitable for real-time embedded applications due to their

DSPs are also reported to be powerful and portable for embedded systems. An example system by Desneux and Legat (Desneux & Legat, 2000) show a DSP with an architecture designed specifically for DWT. Their DSP design stops any wait cycles during algorithm execution by using a bi-processor organization. It is able to perform a 3-stage multiresolution transform in real time. Their DSP is fully programmable in terms of filters

Using a floating-point DSP, Patil and Abel (Patil & Abel, 2006) used redundant wavelet transform as a tool for the analysis of non-stationary signals as well as the localization and characterization of singularities. Their work focused on producing an optimized method for the implementation of a B-spline based redundant wavelet transform (RWT) using a (DSP) for integer scales leads to an improvement in the execution speed over the standard method. A DSP-based edge detection comparison is explained in (Abdel-Qader & Maddix, 2005) where three edge detection algorithms performance on DSP are compared using Canny, Prewitt and Haar wavelet-based. The reported outcome is that the Haar wavelet-based edge detector performed best in terms of SNR in noisy images. The authors recommended post-

The review favours DSPs as a suitable choice for our ANPR application. In addition, following successful results in LP detection using a DSP as reported in (Musoromy et al., 2010) using WT, this work extends the use of WT in the LP character segmentation investigation of SD and HD images using a Texas Instrument's C64plus DSP with minimum

and picture format as well as being capable of image edge processing.

processing of the output edges to make them more optimal.

of 600MHZ clock speed and 1MB of RAM (TI, 2006).

review the special hardware dedicated for WT including DSPs, FPGAs and GPUs.

**2. Dedicated hardware for WT review** 

which is PC independent.

parallel processing abilities.

This section gives a review of interesting ANPR algorithms using WT. The use of discrete wavelet transform (DWT) (described in Section 4.2) in ANPR is reported by Wu (Wu et al., 2009) in LP detection process. The methodology works by applying the **"high-low"** subband feature of 2D Haar DWT twice to increase the recognition of vertical edges while decreasing background noise in real world applications. The authors noted an increase in the ease of location and extraction of the license plate by orthogonal projection histogram analysis from the scene image in comparison with the vertical Sobel operator (a single level 2D Haar DWT) used in most License Plate Detection Algorithms. However, due to the downsampling used in this technique, it is only suitable for use with high-resolution images or cameras in close proximity to the plate (Wu et al., 2009).

An interesting algorithm is proposed by Roomi (Roomi et al., 20011) that consists of two main modules, one for the rough detection of the region of interest (ROI) using vertical gradients and another for the accurate localisation of vertical edges using the vertical subband feature of 2D discrete wavelet transform (DWT). This is followed by the identification of the orthogonal projection histogram for the extraction of the license plate. This method combines the advantage of relatively short runtimes whilst still maintaining accuracy, across a range of vehicle types. The authors reported that the number plates recognition accuracy was reduced where the plates were tilted (Roomi et al., 20011).

WT is also used in the simplification of skew correction in order to reduce computational demands to make the process suitable for real time applications (Paunwala et al., 2010). The method uses two levels WT to extract a skewed feature image of the original LP image, which is then transformed into a binary image from which the feature points can be identified by applying a threshold. These feature points help identify the angle at which the plate is tilted using principal component analysis, from which the correction to the whole plate image can be applied (Paunwala et al., 2010).

To conclude, the use of WT and the advantages are widely reported in the ANPR algorithms and therefore the focus of this chapter is the suitability of WT in HD images and DSPs for real time performance in LP character segmentation but firstly, LP detection process used in this work is summarized in the following section.
