Signal Processing Lab Room MEC 202P College of Engineering Boise State University 1910 University Dr. Boise, ID 83725 Phone: (208) 426-5760

Integration of Printer & Scanner Models

Fields: System Modeling, Parameter Estimation, Image Processing

To create a paper document, it must be printed. Printing is also the output process of photocopying and FAXing. The component of this proposed graduate project is development of a calibration method for the printing process suitable for use in Document Image Analysis and integration of the printing & scanning models.

For this project, only the electrophotographic printing process will be considered. The toner is applied to the paper in quantities related to the charge on the photoconductor. The charge is related to the laser intensity. A Masters Student graduating in May 2004, Margaret Norris, has developed a model of how toner is dispersed on paper that includes prior work done by [] on how laser intensity is related to source image shape. The exact amount of toner applied to the paper and the resulting absorptance level (darkness grey level) are probabilistic measures, therefore a probabilistic model has been developed. This model needs to be expanded to a broader class of input pictures. A method needs to be developed to calibrate this model based on samples of printed characters.

Printing and scanning are the building block processes for document generation. OCR analysis is done on digitized images requiring all documents be scanned. Thus the scanning degradations are the easiest to isolate and were the first to be looked at in [, , , , , ]. Prior work of Dr. Barney Smith has generated new understanding of the relationship between scanner system parameters and document image degradations (stroke width and corner erosion) as well as a collection of tools on scanning models. Printing has been studied in the field of halftoning to decide what to print to get a desired grey level. These two fields will be combined for use on bilevel images common in Document Image Analysis.

The printer model must be combined with the existing model of scanning. To simplify this combination of models, we want to see whether the nonlinear printing model can be approximated by a linear two-dimensional convolution. If so, then the kernel for the convolution in the scanning model can be combined with the kernel for the convolution in the scanning model to make a single print/scan kernel. Doing this would enable all the methods that exist for the scanning model to be maintained. This is one possible route to the next goal of developing defect models that incorporate both printing and scanning sub-system models.

Methods have been developed to calibrate the scanner defect model without extensive equipment, predominantly using information in text images as opposed to specialized test charts. These need to be expanded to the combined print/scan model.

Models are only useful to the scientific community when they are validated. A method of validating defect models was proposed by Kanungo []. This method has been used by Dr. Barney Smith in research done with Dr. Qui []. Code to make this flexible and to try some other experimental possibilities is currently being developed by a pair of undergraduate research students. Using this procedure to validate of each of these models is the final part of this work.

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OCR Training Procedures

Fields: Pattern Recognition, Image Processing

The most prevalent method of improving individual character recognition is to train the classifier with as large of a training set as possible. Recognition accuracies can also be increased by matching the training set to the document. This has been done by extracting templates from the document in one step and then using them in recognition in a second pass through the document [, ]. We propose to use the model calibration developed previously to combine these two methods providing a training set that is both large and matched to the OCR process.

There are several benchmark datasets available for testing OCR systems, and OCR companies have their own large datasets. When a page can be analyzed to characterize the degradation’s relationship to the model, the database can be subdivided into large sets of matched characters.

For good recognition accuracy a large training set and a training set closely matched to the test set are needed. This work proposes as a goal to develop the framework to also match the degradation level of the document to a large training set by developing methods to partition a large training set and to use the model calibration to select the appropriate training set.

The parameters affecting the printer model currently include reflectance of the paper and ink, trace of the laser, spread of the toner, and size and quantity of toner particles. This study will also show what types of image degradations each parameter affects. The information about the relationship between the parameters and the degradations could benefit printer design.

We will determine how to divide the model and parameter spaces to capture the difference in the image degradation. At the same time we want to limit the number of partitions to keep management simple and to maintain the ability to generalize. The partitions should be set such that an error in model calibration will not often point to a different template set. We will determine a method to partition the degradation space for classifier training as one focus of this proposed research. Work by Dr. Barney Smith in [] showed that characters will have a similar appearance as quantified by the Hamming distance when degraded with combinations of degradation parameters yielding the same edge displacement degradation feature so long as the difference in PSF width was not very large. It is expected that subdividing the space in regions of common edge displacement will work better than using a Cartesian division. Other metrics of similarity will be examined also.

Ho & Baird [ ] compared how a classifier trained on a single font (25 phases, 125 (5x5x5) degradations, 94 char classes) degraded over the whole degradation space responds to samples at each point in the degradation space. This showed under which model parameters characters are difficult to recognize when the classifier is trained on a global training set. We will train a family of classifiers each on characters degraded with parameters from a different subset of the degradation space. Then evaluate the classification results for each classifier over the whole space.

We will compare recognition accuracies both with and without this partitioning method. This will be done with both a spatially invariant model and using the adaptive parameter variation. With OCR accuracy on highly degraded document images at 92%, there is need for improvement. A 1% improvement in recognition accuracy on a typical page of 2500 characters will remove 25 errors.

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Feedback Fluoroscopy Image Matching System

Fields: Biomedical image and signal processing, mechanics

Female athletes sustain anterior cruciate ligament (ACL) injuries at rates from three to seven times higher than male athletes in the same sports. Several studies have recently suggested that differences between the genders in the mechanics of landing from jumps may result in increased ACL loads in female athletes. To date, no studies have quantified the internal kinematics of the knee joint during landings in athletes of either gender. Because the ACL connects the femur and tibia at the knee joint, relative motion between these two bones during landing may predispose the ligament to injury. Accurate bony motion data cannot be collected using standard non-invasive motion capture techniques. However, Dr. Michelle Sabick (Mechanical Engineering) and Dr. Elisa Barney Smith (Electrical Engineering) are combining computer vision and image processing algorithms to develop minimally invasive techniques using new medical imaging technologies to quantify joint motion in live human subjects. This technique matches 3-D joint images of human joints with 2-D video fluoroscopy (video X-ray) image sequences to track the motions of bones at a joint very accurately. This will enable researchers to collect accurate, three-dimensional kinematic data of bones and joints in vivo and to accurately quantify how bones in a joint move relative to one another during dynamic activities. Knowledge of the exact spatial position of the two joint bones will allow biomedical researchers to develop techniques to diagnose the extent of joint injuries.

This project involves the design of computer vision and image processing algorithms to match 3-D joint images of human joints with 2-D fluoroscopy (video X-ray) image sequences. The data for this project consists of sets of CT images representing a 3-D volume, and 2-D fluoroscopy image sequences of the same joint. The CT image has already been processed to extract a 3-D solid model of the bones. The procedure will be to:

1. Segment the 3-D CT volume to separate the two bones into 2 different 3-D CT volumes
2. Match the 3-D solid models to the bones in the segmented CT 'images'
3. Use projection software (developed previously for this research) to produce 2-D simulated fluoroscopy images through a process called Digitally Reconstructed Radiographs.
4. Develop edge detection algorithms to locate the bone edges in the real and projected fluoroscopy images
5. Use simulated annealing, or a comparable algorithm, to iteratively adjust the pose of each bone model in 6 degrees of freedom (3 position and 3 orientation) until edges detected in #4 from a projection of the implant model matches edges detected in #4 from the fluoroscopy image. From this the exact spatial position of the two joint bones is known.

This will be used to study the differences in knee joint motions during landing between genders, to quantify joint motions in people with movement or skeletal abnormalities, and to study both normal and pathologic motion in a wide range of skeletal joints. Our goal is to extend the fluoroscopy technique for analysis of very dynamic activities, such as running, jumping, and cutting, which are of particular interest in the study of ACL injury mechanisms in athletes.

A similar project aimed at migrating this approach to images from Magnetic Resonance (MRI) is also available. MRI are preferable because the MR imaging technique is less dangerous to the subjects due to CT & Fluoroscopy using x-rays.

See also COBR and Ongoing biomed research

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