III WVC 2007 - Iris.sel.eesc.sc.usp.br - USP

WVC'2007 - III Workshop de Visão Computacional, 22 a 24 de Outubro de 2007, São José do Rio Preto, SP.single FFT coefficient graph) of all the FFT’scoefficient was plotted (figure 3.2).small. So that, the patterns were obtained using theprocess average, according to the formula below:IF=where:N∑i=1IiNI F = filterI i = i-th image (sample)N = total number of samplesFigure 3.2: Cumulative graph of all the FFT’scalculated from the license plates development set.Inspecting the figure above, it is possible to verifythat there is a characteristic horizontal frequency rangedefined between 0.03 and 0.06. So this range will beused as a pre-processing input to its filter. It is alsopossible to see that a frequency component around 0.3,introduced by a specific noise from the camera used toacquire the images. However it can be easily filteredand will not interfere in the final result.The pre-processing will search in the image forregions that have significant horizontal frequencycomponents around 0.03 and 0.06. Only in theseregions, the matched filter will act on.This approach was used to reduce thecomputational complexity of the algorithm and makethe system faster.As a result of this operation, we have the followingfilters for each of the 3 patterns:Figure 3.4: Dot filter.Figure 3.5: License plate filter.3.2. Matched filterA matched filter is obtained by correlating a knownsignal, or template, with an unknown signal to detectthe presence of the template in the unknown signal.This is equivalent to convolving the unknown signalwith a time-reversed version of the template. Thematched filter is the optimal linear filter formaximizing the signal to noise ratio (SNR) in thepresence of additive stochastic noise [4].The matched filters developed in this project arebased on patterns that occur in the vehicle image, suchas the license plate, its characters and the dot betweenthe numbers and the letters. It is possible to see the lastpattern below:Figure 3.3: Dot between the numbers and the letters.In this work, it was considered that the statisticalfloat among the samples used to build the filter wasFigure 3.6: Character filters.From the figures above it is possible to notice thatthe float among the characters samples that were usedto build the set of character filters is smaller than thefloat in the samples used to build the license plate andthe dot filters.So now, with the pre-processing parameters and thematched filters constructed, it is possible to moveforward and analyze the entire system.3.3. The systemAfter presenting the filters used in the project, wecan put them together and show the sequence oftechniques used to have the entire locating system.189