Materials scientists make use of image processing tools more and more as technology advances and the data
volume that needs to be analyzed increases. We propose a method to optically measure magnetic eld induced
strain (MFIS) as well as twin boundary movement in Ni2MnGa single crystal shape memory alloys to facilitate
spatially resolved tracking of deformation. Current magneto-mechanical experiments used to measure MFIS
can measure strain only in one direction and do not provide information about the movement of individual
twin boundaries. A sequence of images captured from a high resolution camera is analyzed by a boundary
detection algorithm to provide strain data in multiple directions. Subsequent motion detection and Hough feature
extraction provide quantitative information about the location and movement of active twin boundaries.
Scanning a halftone image introduces halftone artifacts, known as Moir´e patterns, which significantly degrade the
image quality. Printers that use amplitude modulation (AM) screening for halftone printing position dots in a
periodic pattern. Therefore, frequencies relating halftoning are easily identifiable in the frequency domain. This
paper proposes a method for descreening scanned color halftone images using a custom band reject filter designed
to isolate and remove only the frequencies related to halftoning while leaving image edges sharp without image
segmentation or edge detection. To enable hardware acceleration, the image is processed in small overlapped
windows. The windows are filtered individually in the frequency domain, then pieced back together in a method
that does not show blocking artifacts.
Photocopies of the ballots challenged in the 2008 Minnesota elections, which constitute a public
record, were scanned on a high-speed scanner and made available on a public radio website. The
PDF files were downloaded, converted to TIF images, and posted on the PERFECT website. Based
on a review of relevant image-processing aspects of paper-based election machinery and on
additional statistics and observations on the posted sample data, robust tools were developed for
determining the underlying grid of the targets on these ballots regardless of skew, clipping, and
other degradations caused by high-speed copying and digitization. The accuracy and robustness of
a method based on both index-marks and oval targets are demonstrated on 13,435 challenged
ballot page images.
This paper proposes a novel method for document enhancement. The method is based on the combination of
two state-of-the-art filters through the construction of a mask. The mask is applied to a TV (Total Variation) -
regularized image where background noise has been reduced. The masked image is then filtered by NLmeans (Non
LocalMeans) which reduces the noise in the text areas located by the mask. The document images to be enhanced
are real historical documents from several periods which include several defects in their background. These defects
result from scanning, paper aging and bleed-through. We observe the improvement of this enhancement method
through OCR accuracy.
This paper presents a novel 2×1D phase correlation based image registration method for verification of printer
emulator output. The method combines the basic phase correlation technique and a modified 2×1D version of
it to achieve both high speed and high accuracy. The proposed method has been implemented and tested using
images generated by printer emulators. Over 97% of the image pairs were registered correctly, accurately dealing
with diverse images with large translations and image cropping.
The effects of different image pre-processing methods for document image binarization are explored. They are
compared on five different binarization methods on images with bleed through and stains as well as on images
with uniform background speckle. The binarization method is significant in the binarization accuracy, but
the pre-processing also plays a significant role. The Total Variation method of pre-processing shows the best
performance over a variety of pre-processing methods.
A mathematical model of the electrophotographic printing process has been developed. This model can be
used for analysis. From this a print simulation process has been developed to simulate the effects of the model
components on toner particle placement. A wide variety of simulated prints are produced from the model's
three main inputs, laser spread, charge to toner proportionality factor and toner particle size. While the exact
placement of toner particles is a random process, the total effect is not. The effect of each model parameter on
the ISO 13660 print quality attributes line width, fill, raggedness and blurriness is described.
Analyzing paper-based election ballots requires finding all marks added to the base ballot. The position, size, shape,
rotation and shade of these marks are not known a priori. Scanned ballot images have additional differences from the
base ballot due to scanner noise. Different image processing techniques are evaluated to see under what conditions they
are able to detect what sorts of marks. Basing mark detection on the difference of raw images was found to be much
more sensitive to the mark darkness. Converting the raw images to foreground and background and then removing the
form produced better results.
INCITS W1.1 is a project chartered to develop an appearance-based image quality standard. This paper summarizes the
work to date of the W1.1 Text and Line Quality ad hoc team, and describes the progress made in developing a Text
Quality test pattern and an analysis procedure based on experience with previous perceptual rating experiments.
OCR often performs poorly on degraded documents. One approach to improving performance is to determine a good filter
to improve the appearance of the document image before sending it to the OCR engine. Quality metrics have been
measured in document images to determine what type of filtering would most likely improve the OCR response for that
document image. In this paper those same quality metrics are measured for several word images degraded by known
parameters in a document degradation model. The correlation between the degradation model parameters and the quality
metrics is measured. High correlations do appear in many places that were expected. They are also absent in some
expected places and offer a comparison of quality metric definitions proposed by different authors.
In September 2000, INCITS W1 (the U.S. representative of ISO/IEC JTC1/SC28, the standardization committee for office equipment) was chartered to develop an appearance-based image quality standard.(1),(2) The resulting W1.1 project is based on a proposal(4) that perceived image quality can be described by a small set of broad-based attributes. There are currently five ad hoc teams, each working towards the development of standards for evaluation of perceptual image quality of color printers for one or more of these image quality attributes. This paper summarizes the work in progress of the teams addressing the attributes of Macro-Uniformity, Color Rendition, Text and Line Quality and Micro-Uniformity.
Calibration of scanners and cameras usually involves measuring the point spread function (PSF). When edge data is used to measure the PSF, the differentiation step amplifies the noise. A parametric fit of the functional form of the edge spread function (ESF) directly to the measured edge data is proposed to eliminate this. Experiments used to test this method show that the Cauchy functional form fits better than the Gaussian or other forms tried. The effect of using a functional form of the PSF that differs from the true PSF is explored by considering bilevel images formed by thresholding. The amount of mismatch seen can be related to the difference between the respective kurtosis factors.
KEYWORDS: Point spread functions, Optical character recognition, Detection and tracking algorithms, Data modeling, Printing, Current controlled current source, Scanners, Convolution, Electronic imaging, Visualization
Generally speaking optical character recognition algorithms tend to perform better when presented with homogeneous data. This paper studies a method that is designed to increase the homogeneity of training data, based on an understanding of the types of degradations that occur during the printing and scanning process, and how these degradations affect the homogeneity of the data. While it has been shown that dividing the degradation space by edge spread improves recognition accuracy over dividing the degradation space by threshold or point spread function width alone, the challenge is in deciding how many partitions and at what value of edge spread the divisions should be made. Clustering of different types of character features, fonts, sizes, resolutions and noise levels shows that edge spread is indeed shown to be a strong indicator of the homogeneity of character data clusters.
The National Library of Medicine has developed a system for the automatic extraction of data from scanned journal articles to populate the MEDLINE database. Although the 5-engine OCR system used in this process exhibits good performance overall, it does make errors in character recognition that must be corrected in order for the process to achieve the requisite accuracy. The correction process works by feeding words that have characters with less than 100% confidence (as determined automatically by the OCR engine) to a human operator who then must manually verify the word or correct the error. The majority of these errors are contained in the affiliate information zone where the characters are in italics or small fonts. Therefore only affiliate information data is used in this research. This paper examines the correlation between OCR errors and various character attributes in the MEDLINE database, such as font size, italics, bold, etc. The motivation for this research is that if a correlation between the types of characters and types of errors exists it should be possible to use this information to improve operator productivity by increasing the probability that the correct word option is presented to the human editor. Using a categorizing program and confusion matrices, we have determined that this correlation exists, in particular for the case of characters with diacritics.
Small image deformations such as those introduced by optical scanners significantly reduce the accuracy rate of optical character recognition (OCR) software. Characterization of the scanner used in the OCR process may diminish the impact on recognition rates. Theoretical methods have been developed to characterize a scanner based on the bi-level image resulting from scanning a high contrast document. Two bottlenecks in the naïve implementation of these algorithms have been identified, and techniques were developed to improve the execution time of the software. The algorithms are described and analyzed. Since approximations are used in one of the techniques, the error of the approximations is examined.
Two major degradations, edge displacement and corner erosion, change the appearance of bilevel images. The displacement of an edge determines stroke width, and the erosion of a corner affects crispness. These degradations are functions of the system parameters: the point spread function (PSF) width and functional form, and the binarization threshold. Changing each of these parameters will affect an image differently. A given amount of edge displacement or amount of erosion of black or white corners can be caused by several combinations of the PSF width and the binarization threshold. Any pair of these degradations are unique to a single PSF width and binarization threshold for a given PSF function. Knowledge of all three degradation amounts provides information that will enable us to determine the PSF functional form from the bilevel image. The effect of each degradation on characters will be shown. Also, the uniqueness of the degradation triple {dw, db, dc} and the effect of selecting an incorrect PSF functional form will be shown, first with relation to PSF width and binarization threshold estimate, then for how this is visible in sample characters.
Degradations that occur during scanning can cause errors in Optical Character Recognition (OCR). Scans made in bilevel mode (no gray scale) from high contrast source patterns are the input to the estimation processes. Two scanner system parameters are estimated from bilevel scans using models of the scanning process and bilevel source patterns. The scanner's point spread function (PSF) width and the binarization threshold are estimated by using corner features in the scanned images. These estimation algorithms were tested in simulation and with scanned test patterns. The resulting estimates are close in value to what is expected based on gray-level analysis. The results of estimation are used to produce synthetically scanned characters that in most cases bear a strong resemblance to the characters scanned on the scanner at the same settings as the test pattern used for estimation.
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