Image Estimation Algorithm for Out of Focus and Blur Images to Retrieve the Barcode Value

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3 observation variable is observable and is jointly determined by two consecutive state variables. The two boundary observation variables located on the two sides of the scan line are individually determined by the first and last state variables. C. Reference Waveform Segments Generation: Observation sequence waveform is compared with the standard waveform at a specific blur level for any qualified state variable.the blurring kernel is used to generate the reference waveform. Generally the new pixels of the image are constructed by an weighted average of the pixels close to it. A little square is drawn around each pixel.if 6 neighbors next to a pixel is taken and a weighted average is performed to get the middle pixel.in the Gaussian blur case it breaks down into two one dimensional operations. For each pixel take some amount of pixels next to a pixel in the row direction only. Multiply the pixel values with weights computed from the Gaussian distribution and sum them up. Another way to take dot product between a vector and the weights obtained from the pixel. Repeat this process in the column direction. D. A Directed Graphical Model: The state variable sequence is represented by linear barcode symbol value. The observation variable sequence is represented by observed scan line. Now the stochastic relation between the linear barcode symbol value and observed scan line is determined as a directed graphical model. E. Dynamic Template Matching: Template matching is a technique used to classify an object. This technique is used to compare a portion of image against one another. Sample images are used to recognize similar objects which are found in source image. If standard deviation of the template image is compared to the source image is small then template matching is used. Generally templates are used to identify printed characters, numbers, and other small, simple objects. The matching process moves the template image to all possible positions in a larger source image and computes a numerical index that indicates how much the template matches the image in that position. Usually match is done on a pixel-by-pixel basis. In dynamic template matching technique the unused information is not considered during comparison. The necessary information is selected and compared recursively to get the exact barcode value. III. SIMULATION RESULTS The advantages of the proposed work are dynamic template matching which is memory efficient. In naïve template matching offline templates are generated initially and later each template is compared with original. In dynamic template matching the templates are compared recursively due to which database memory is used efficiently. The results obtained are successfully. A. Barcode localization: At the input stage, product with barcode image is given as input. The input image and localized barcode is shown in fig 3.The required barcode is cropped from the image and localized. The localization of that barcode is a difficult process. This localization is done using Otsu thresholding and edge detection. Fig. 3: Input image and localized barcode B. Deformed Scanline: The localized barcode region is now segmented. The segmented region is converted into a waveform. If the localized region consists of noise then the required information will not be obtained.the deformed scanline is shown in fig 4.Each segments are deformed as electrical signal. If this process is not done properly then segmented scanline cannot be converted into observation sequence. All rights reserved by 252

4 Fig. 4: Deformed scanline obtained from localized barcode C. Scanline Segment and its Observation Sequence: The scanline is segmented for each iteration. When all parallel lines of barcodes are segmented then in the next iteration observation sequences is generated. For example a single segment of scanline from first iteration is shown in fig 5.The observation sequence for that segment is shown in fig 5. Fig. 5: Single deformed scan line segment and observation sequence D. Observation Sequence Waveform: When all segments are converted into their corresponding observation sequences, then the resulting waveform is shown in fig 6. Thus the required result is obtained. Fig. 6: Observation sequence waveform IV. CONCLUSION When an input image is given barcode is extracted from that image. Then the barcode is localized using edge detection and otsu thresholding.for this localized image deformed scanline waveform is generated. This deformed waveform is used to generate observation sequence. The same procedure is carried out for out-of-focus blur image where directed graphical waveform is generated using optimal state variable. In dynamic template matching, observation sequence and directed graphical waveform is compared until they match each other, this process is recursive. Once they match each other the verification is done to retrieve the barcode value. A new logistical approach implementing the use of 2D barcodes and some of the potential methodology for the routine sample analysis. The results presented in this study offer insights regarding the feasibility, usability and effectiveness of using a barcode scanner with all the products. During a recent large project based on the reading of randomized bar code labels, the fact that the vast majority of the reading activity had to take place at night had to be taken into account, especially in All rights reserved by 253

5 relation to the affect that the illumination sources would have on the working environment. Another over-riding factor in the project was that the system had to be totally self-sufficient since health and safety regulations on the shop floor precluded any personnel being in the vicinity of any of the scanning or control equipment. REFERENCES [1] Ling Chen, Member, IEEE, Hong Man, Senior Member, IEEE, and Huading Jia, On Scanning Linear Barcodes From Out-of-Focus Blurred Images: A Spatial Domain Dynamic Template Matching Approach, IEEE TRANSACTIONS, VOL. 23, NO. 6, JUNE [2] O. Gallo and R. Manduchi, Reading 1D barcodes with mobile phones using deformable templates, IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 9, pp , Sep [3] A. Zamberletti, I. Gallo, M. Carullo, and E. Binaghi, Neural image restoration for decoding 1-D barcodes using common camera phones, in Proc. 5th Int. Conf. Comput. Vision Theory and Appl., 2010, pp [4] N. Dridi, Y. Delignon, W. Sawaya, and F. Septier, Blind detection of severely blurred 1D barcode, in Proc. GLOBECOM, Dec. 2010, pp.1-5. [5] Saeed Yahyanejad, Jacob Str om Removing Motion Blur from Barcode Images IEEE Trans. Pattern Anal. Mach. Intell., May All rights reserved by 254

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