Albert Sadovnikov

Detection of irregularities in regular patterns

Regular patterns, as defined in this study, are found in areas of industry and science, for example, halftone raster patterns used in the printing industry and crystal lattice structures in solid state physics. The need for quality inspection of products containing regular patterns has aroused interest in the application of machine vision for automatic inspection. Quality inspection typically corresponds to detecting abnormalities, defined as irregularities in this case. In this study, the problem of irregularity detection is described in analytical form and three different detection methods are proposed. All the methods are based on characteristics of the Fourier transform to compactly represent regular information. The Fourier transform enables the separation of regular and irregular parts of an input image. The three methods presented are shown to differ in their generality and computational complexities.

Mottling assessment of solid printed areas and its correlation to perceived uniformity

Mottling is one of the most important printing defects in modern offset printing using coated papers. Mottling can be defined as undesired unevenness in perceived print density. In our research, we have implemented three methods to evaluate print mottle: the standard method, the cluster-based method, and the bandpass method. Our goal was to study the methods presented in literature, and modify them by taking relevant characteristics of the human visual system into account. For comparisons, we used a test set of 20 grey mottle samples which were assessed by both humans and the modified methods. The results show that when assessing low-contrast unevenness of print, humans have diverse opinions about quality, and none of the methods accurately capture the characteristics of human vision.

Automated mottling assessment of colored printed areas

Mottling is one of the most significant defects in modern offset printing using coated papers. Mottling can be defined as undesired unevenness in perceived print density. Previous research in the field considered only gray scale prints. In our work, we extend current methodology to color prints. Our goal was to study the characteristics of the human visual system, perform psychometric experiments and develop methods which can be used at industrial level applications. We developed a method for color prints and extensively tested it with a number of experts and laymen. Suggested approach based on pattern-color perception separability proved to correlate with the human evaluation well.

Quantified and perceived unevenness of solid printed areas

Mottling is one of the most severe printing defects in modern offset printing using coated papers. It can be defined as undesired unevenness in perceived print density. In our studies, we have implemented two methods known from the literature to quantify print mottle: the standard method for prints from office equipment and the bandpass method specially designed for mottling. Our goal was to study the performance of the methods when compared to human perception. For comparisons, we used a test set of 20 grey samples which were assessed by professional and non-professional people, and the artificial methods. The results show that the bandpass method can be used to quantify mottling of grey samples with a reasonable accuracy. However, we propose a modification to the bandpass method. The enhanced bandpass method utilizes a contrast sensitivity function for the human visual system directly in the frequency domain and the function parameters are optimized based on the human assessment. This results a significant improvement in the correlation to human assessment when compared to the original bandpass method.

On estimation of perceived mottling prior to printing

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.

Object class detection using local image features and point pattern matching constellation search

Several novel methods based on locally extracted image features and spatial constellation models have recently been introduced for invariant object class detection and recognition. The accuracy and reliability of the methods depend on the success of both tasks: image feature extraction and spatial constellation model search. In this study a novel method for object class detection is introduced. It combines supervised Gabor-based confidence-ranked image features and affine invariant point pattern matching. The method is able to deal with occlusions and its potential is demonstrated on a standard face database.

Visual inspection on paper by machine vision

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.

Detecting irregularities in regular patterns

This study compares three different methods designed for detecting irregularities from regular dot patterns. Frequency domain information is used to split an original regular pattern into two images: the first image contains the perfect repeating pattern and the second one includes all irregularities in the original image. The methods are based on the Fourier transform, but they differ in how they separate or utilize the regular and irregular image parts. Performances of these methods are compared, and their strengths and weaknesses are discussed

Automating visual inspection of print quality

Automatic evaluation of visual print quality is addressed in this study. Due to many complex factors of perceived visual quality its evaluation is divided to separate parts which can be individually evaluated using standardized assessments. Most of the assessments however require active evaluation by trained experts. In this paper one quality assessment, missing dot detection from printed dot patterns, is addressed by defining sufficient hardware for image acquisition and method for detecting and counting missing dots from a test strip. The experimental results are evidence how the human assessment can be automated with the help of machine vision, thus making the test more repeatable and accurate.