One of the common techniques of printing gray scale images using a bi-level device such as a commercial laser printer is Error Diffusion. Since most of the fingerprint images are gray scale images, they too can be printed using error diffusion. However, none of the existing popular error diffusion algorithms makes use of the ridge-flow information present in a fingerprint image. In this note, we explore the use of ridge-flow directions in a fingerprint image for the purpose of the error diffusion in printing fingerprint images.
Proc. SPIE. 2932, Human Detection and Positive Identification: Methods and Technologies
KEYWORDS: Digital signal processing, Databases, Image processing, Fingerprint recognition, Machine vision, System identification, Local area networks, Computer graphics, Computer architecture, Evolutionary algorithms
Automated Fingerprint Identification has a history of more than 20 years. In the last 5 years, there has been an explosion of technologies that have dramatically changed the face of AFIS. Few other engineering and science fields offer such a widespread use of technology as does computerized fingerprint recognition. Optics, computer vision, computer graphics, artificial intelligence, artificial neural networks, parallel processing, distributed client server applications, fault tolerant computing, scaleable architectures, local and wide area networking, mass storage, databases, are a few of the fields that have made quantum leaps in recent years. All of these improvements have a dramatic effect on the size, speed, and accuracy of automated fingerprint identification systems. ThIs paper offers a historical overview of these trends and discuss the state of the art. It culminates with an overview an educated forecast on future systems, especially those 'real time' systems for use in area of law enforcement and civil/commercial applications.
In this research, the use of an approximate method to derive edge localization parameter (edge width) at the output of the IDS filter is shown. This parameter is used to develop an algorithm for image reconstruction of the response of the IDS filter. Simulation of this algorithm on real images is illustrated.
The class of Intensity-Dependent Spread (IDS) filters proposed by Cornsweet and Yellott produces the reflectance ratio map at the output of the filter independent of illumination. We propose a new method to optimize the spread function in the context of the IDS filters. The optimum spread is the solution to a variational formulation where the image noise is minimized subject to a smoothness constraint. In our solution, the Lagrangian parameter is space-dependent and also is inversely proportional to the spread function. This solution is a function of the input image and its first and second derivatives. The optimized scale-function is then applied to the IDS filter structure to produce sharp edge localization as well as reflectance ratio estimates independent of illumination. The simulation results illustrate the fact that the Optimum Intensity-Dependent Spread filter improves the performance of the IDS filter and also is two orders of magnitude faster for 512 X 512 images. Examples comparing the results of the two filter structures are illustrated.
This paper deals with the correction ofthe distortion of magnetic resonance pictures due to the presence of bulky stereotactic surgical equipment or other kinds of ferromagnetic perturbation. The distortion is
measured over a ring of calibration rods distributed around the patient's head. This boundary information is used to correct the distortion all over the transverse scan plane. The unique feature of the proposed approach is that the error all over the transverse plane is bounded by the error around the ring of rods. This is accomplished by making use of subharmonic functions.
For medical applications such as stereotactic surgery and radiation treatment accurate information about the size and location of the lesion is very crucial. Although magnetic resonance (MR) images does convey more clinical information than computerized tomography (CT) the geometry of MR image is distorted because of the inhomogeneous main magnetic field and nonlinear gradient fields. This paper deals with the correction of the MR image distortions by modeling the distortion function as certain high order polynomial equations. The coefficients of the polynomial equations can be determined by mapping a set of distorted reference points to its a priori locations. A special phantom is designed to correct the distortion in all three directions. To avoid roundoff error a subpixel mapping scheme is used which corrects not only the physical distortions but also the intensities of the image as well. Finally a special subharmonic condition is evaluated which when satisfied will make the use of MR images for robotic stereotactic procedures feasible.
We propose the use of a compressive function of contrast measure ( - operator) as the spread of
a Gaussian to be used in the context of the Intensity-Dependant Spread (IDS) and generalized IDS
filters. It is shown that the new approach enhances the performance of the IDS filter such as smoothing
( noise reduction/rejection), avoids blurring of closely spaced edges and also if they are under low and
nonuniform illumination functions. Simulation results verify that the time complexity of the CDS is one
order of magnitude faster than the IDS filter in 1D. Illustrative examples comparing the two filters are