A method has been developed using an image analysis system that simulates human print quality perception. Previous work in the area of print quality assessment has only produced methods that measure individual print quality variables, or assess small parts of an image. The image analysis system developed in this investigation is different from the previous work because it analyses the combined effects of different variables using neural network technology. In addition, measurements from an entire image can be obtained and the system can assess images irrespective of their shape. The image analysis system hardware consists of a monochrome CCD camera, a Matrox image acquisition board and a 200 MHz Pentium computer. A data pre-processing program was developed using Visual Basic version 5 to process the image data from the camera. The processed data was fed into a neural network so that empirical models of print quality could be formulated. The neural network code originated from the Matlab neural network toolbox. Backpropagation and radial basis neural network functions were used in the investigation. The hardware and software of the image analysis system were tested for non-impact printing techniques. Images of a square, a circle and text characters with dimensions of 1 cm or less were used as test images for the image analysis system. It was established that it was possible to identify the different printing processes that produced the simple shapes and text characters using the image analysis system. This was achieved by training the neural network using pre-processed image data. This produced multi-dimensional mathematical models that were used to classify the different printing processes. The classification of the different printing processes involved the objective measurement of print quality variables. Different printing processes can produce print that differs in print quality when assessed by observers. Therefore the successful classification of the printing processes demonstrated that the image analysis system could, in some cases, simulate human print quality perception. To consolidate on the preceding printing process identification result, a simulation of print quality perception was made. A neural network was trained using observer assessments of a simple pictorial image of a face. These face images were produced using a variety of different non-impact printing techniques. The neural network model was used to predict the outcomes of a further set of assessments of face images by the same observer. The accuracy of the predictions was 23 out of 24 for both the backpropagation and radial basis function neural network functions used in the test. The investigation also produced two possible practical applications for the system. Firstly, it was shown that the system has the potential to be used as a machine that can objectively assess the print quality from photocopiers. Secondly, it was demonstrated that the system might be used for forensic work, since it can identify different printing processes.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:264398 |
| Date | January 1998 |
| Creators | Tchan, Jack Soning |
| Publisher | Open University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://oro.open.ac.uk/57912/ |
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