Due to the rise in performance of digital printing, image-based applications are gaining popularity. This creates needs for
specifying the quality potential of printers and materials in more detail than before. Both production and end-use
standpoints are relevant. This paper gives an overview of an
on-going study which has the goal of determining a
framework model for the visual quality potential of paper in color image printing. The approach is top-down and it is
founded on the concept of a layered network model. The model and its subjective, objective and instrumental
measurement layers are discussed. Some preliminary findings are presented. These are based on data from samples
obtained by printing natural image contents and simple test fields on a wide range of paper grades by ink-jet in a color
managed process. Color profiles were paper specific. Visual mean opinion score data by human observers could be
accounted for by two or three dimensions. In the first place these are related to brightness and color brightness. Image
content has a marked effect on the dimensions. This underlines the challenges in designing the test images.
Print mottle is one of the most significant defects in modern offset printing influencing overall print quality.
Mottling can be defined as undesired unevenness in perceived print density. Previous research in the field
considered designing and improving perception models for evaluating print mottle. Mottle has traditionally
been evaluated by estimating the reflectance variation in the print. In our work, we present an approach of
estimating mottling effect prior to printing. Our experiments included imaging non printed media under various
lighting conditions, printing the samples with sheet fed offset printing and imaging afterwards. For the preprint
examinations we used a set of preprint images and for the outcome testing we used high resolution scans. For
the set of papers used in experiment only uncoated mechanical speciality paper showed a good chance of print
mottle prediction. Other tested paper types had a low correlation between non-printed and printed images.
The achieved results allow predicting the amount of mottling on the final print using preprint area images for
a certain paper type. Current experiment settings suited well for uncoated paper, but for the coated samples
other settings need to be tested. The results show that the estimation can be made on the coarse scale and for
better results extra parameters will be required, i.e., paper type, coating, printing process in question.
During the manufacturing process steel bars are cleaned of roll scale by shot blasting, before further processing the bars by drawing. The main goal of this project is to increase the automation of the shot blasting process by machine vision. For this purpose a method is needed for estimating the surface roughness and other anomalies from the steel bars from digital images after the shot blasting. The goal of this method is to estimate if the quality of shot blasting is sufficient considering the quality of the final products after the drawing. In this project a method for normalising the images is considered and several methods for estimating the actual roughness level are experimented. During the experiments a best method was one where the roughness levels are calculated directly from the images as if the images were similar to other measuring sources and the grey-level values in the images represent the deviation on the bar surface. This at least separates the different samples.
There are several important standard laboratory experiments for determining the quality of produced paper in the paper making industry. To know the quality is essential since it defines the use of paper for various purposes. Moreover, customers are expecting a certain degree of quality. Many of paper printability tests are based on off-line visual inspection. Currently these tests are done by printing test marks on a piece of paper and then observing the quality by a human evaluator. In this report visual inspection on paper by machine vision is discussed from a point of off-line
industrial measurements. The work focuses on the following paper printability problems: missing dots (Heliotest), print dot density, unevenness of printing image, surface strength (IGT), ink setting, linting, fiber counting, and digital printing. Compared to visual inspection by human evaluation, automated machine vision systems could offer several useful advantages: less deviations in measurements, better measurement accuracy, new printability parameters, shorter measurement times, less manpower to monotonic measurements, many quality parameters by one system, and automatic
data transfer to mill level information systems. Current results with paper and board samples indicate that human evaluators could be replaced. However, further research is needed since the printability problems vary mill by mill, there is a large number of various paper and board samples, and the relationships between off-line and on-line measurements must be considered.
Parallel systems provide a robust approach for high performance computing. Lately the use of parallel computing has become more available as new parallel environments have evolved. Low cost and high performance of off-the-shelf PC processors have made PC-based multiprocessor systems popular. These systems typically contain two or four processors. Standardized POSIX-threads have formed an environment for the effective utilization of several processors. Moreover, distributed computing using networks of workstations has increased. The motivation for this work is to apply these techniques in computer vision. The Hough Transform (HT) is a well-known method for detecting global features in digital images. However, in practice, the sequential HT is a slow method with large images. We study the behavior of line detecting HT with both message passing workstation networks and shared-memory, multiprocessor systems. Parallel approaches suggested in this paper seem to decrease the computation time of HT significantly. Thus, the methods are useful for real-world applications.
Visual quality control is an important application area of machine vision. In ceramics industry, it is essential that in each set of ceramic tiles every single tile looks similar, while considering e.g. color and texture. Our goal is to design a machine vision system that can estimate the sufficient similarity or same appearance to the human eye. Currently, the estimation is usually done by human vision. Our main approach is to use accurate spectral representation of color, and compare spectral features to the RGB color features. The authors have recently proposed preliminary methods and results for the classification of color features. In this paper the approach is developed further to cope with illumination effects and to take more advantage of spectral features more. Experiments with five classes of brown tiles are discussed. Besides the k-NN classifier, a neural network, called the Self-Organizing Map (SOM) is used for understanding spectral features. Every single spectrum in each tile is used as input to a 2-D SOM with 30 X 30 nodes or neurons. The SOM is analyzed in order to understand how spectra are clustered. As a result, the nodes are labeled according to the classes. Another interest is to know whether we can find the order of spectral colors. In our approach, all spectra are clustered by 32 nodes in a 1-D SOM, and each pixel (spectrum) is presented by pseudocolors according to the trained nodes. Thus, each node corresponds to one pseudocolor and every spectrum is mapped into one of these nodes. Finally, the results are compared to experiments with human vision.
This work studies visual quality control in ceramics industry. In tile manufacturing, it is important that in each set of tiles, every single tile looks similar. For example, the tiles should have similar color and texture. Our goal is to design a machine vision system that can estimate the sufficient similarity or same appearance to the human eye. Currently, the estimation is usually done by human vision. Differing from other approaches our aim is to use accurate spectral representation of color, and we are comparing spectral features to the RGB color features. A laboratory system for color measurement is built. Experimentations with five classes of brown tiles are presented. We use chromaticity RGB features and several spectral features for classification with the k-NN classifier and with a neural network, called Self-Organizing Map. We can classify many of the tiles but there are several problems that need further investigations: larger training and test sets are needed, illuminations effects must be studied further, and more suitable spectral features are needed with more sophisticated classifiers. It is also interesting to develop further the neural approach.