John W. V. Miller

Recognition of license plate images: issues and perspectives

Recognition of license plate images is the topic of the paper. The major issue in this problem is the accurate extraction of the license plate string from varying backgrounds. The images are also characterized by non-uniform illumination. Recognition of the string is fairly straightforward, if the extraction process has been correctly designed. The authors discuss three different approaches to extraction and their study revealed that fusion of gray scale morphology and homomorphic processing yielded accurate extraction of the string. Recognition studies with 700 images captured during day and night times indicated an overall acceptance rate of 80% or higher with less than 1% confusion is achievable. Results obtained from a more exhaustive study that is currently in progress are presented.

Efficient algorithm for Gaussian blur using finite-state machines

2D Gaussian blur operations are used in many image processing applications. The execution times of these operations can be rather long, especially where large kernels are involved. Proper use of two properties of Gaussian blurs can help to reduce these long execution times: (1) Large kernels can be decomposed into the sequential application of small kernels. (2) Gaussian blurs are separable into row and column operations. This paper makes use of both of these characteristics and adds a third one: (3) The row and column operations can be formulated as finite-state machines to produce highly efficient code and, for multi-step decompositions, eliminate writing to intermediate images. This paper shows the FSM formulation of the Gaussian blur for the general case and provides examples. Speed comparisons between various implementations are provided for some of the examples. The emphasis is on software implementations, but implementations in pipelined hardware are also discussed. Straightforward extensions of these concepts to 3- and higher-dimensional image processing are also presented. Implementation techniques for DOG (Difference-of-Gaussian filters) are also provided.

Spatially invariant image sequences

The authors define linearly additive spatially invariant image sequences and present an explicit mathematical model for describing them. In such a sequence, all objects are positionally invariant in each image of the sequence but have varying gray-scale contributions to the successive images of the sequence. The various components (features or processes) of the scene or object contribute additively to each image of the sequence, but each component has a characteristic variation (signature) from image to image due to the variation of the function, parameter or spectral band over the sequence. Objects with different spectral characteristics will have different image sequence signatures which can be used to distinguish them. Also presented are the general formulation, derivation, and explicit expression for the linear filter, called the simultaneous diagonalization (SD) filter, that calculates a single new image from the sequence such that a desired process is emphasized and any number of undesired processes is suppressed in the filtered image.

License plate recognition using SKIPSM

Recognition of license plate images is the topic of this paper. The major issue in this problem is the accurate extraction of the license plate character string from varying backgrounds using processing techniques that are reasonably fast. The images are also characterized by non-uniform illumination. Recognition of the string is relatively straightforward, if the extraction process has been correctly designed. The authors present three different approaches to extraction and their study revealed that the combination of gray-scale morphology with a log gray-scale transform provided accurate extraction of the string. Recognition studies with 700 images captured during day and night periods indicated an overall acceptance rate of nearly 90% with less than 1% confusion.

Gray-scale image processing algorithms using finite-state machine concepts

e pte Abstract. The early work on separated-kernel image processing using finite state machines (SKIPSM) concentrated mainly on binary processing which provided a fast means to implement binary morphology with very large structuring elements in a single pass using relatively small lookup tables. The basic concept can be extended to certain gray-scale processing techniques with a useful improvement in processing speed. Gray-scale SKIPSM algorithms present more of a challenge because the extremely large number of states that exist make the use of the lookup tables for the direct implementation of finite state machines impractical. However, as will be demonstrated, it is still possible to realize significant speed advantages with SKIPSM-implemented gray-scale processing using appropriate coding techniques. This paper describes two such techniques, grayscale morphology and Gaussian low-pass filtering.

Execution speed comparisons for binary morphology

The SKIPSM paradigm gives very fast execution of binary morphology operations with large arbitrary SEs. Hardware- based applications using lookup tables to implement the FSMs have been in use for almost a decade. More recently, software-based applications have benefited form comparable speed increases. This paper provides speed comparisons between software implementations using lookup tables and those using direct implementations of the FSMs, for a range of SE sizes and shapes.

Image processing benchmark study

This paper describes the benchmarking of image processing algorithms using high-performance workstations and personal desktop computers. For the various platforms evaluated which included machines from Sun, SGI, Apple, and Gateway, compiler options were varied to obtain the fastest execution times. Algorithms evaluated included typical image processing operations such as derivatives, logical operations, morphology, subtraction, median filter, and the new SKIPSM approach. Data were collected using the different platforms and are presented here in tabular form. The results indicate that the latest generation of personal computers have processing capabilities that are similar to UNIX-based work stations.

Very Low-Cost In-Process Gauging System

A vision system to gauge two types of automotive parts has been developed. One of the part types is a power steering connector in which the depth and width of the groove, and the distance between the start of the groove and the end of the power steering line are gauged. For the second type of part, a crimped connector attached to a brake hose, measurements of interest are the two diameters ofthe crimp and the bell length where the hose is inserted into the connector. A standard video camera is used to acquire the image of a back-illuminated image of the part which is digitized and captured with a frame grabber. The basic hardware to accomplish the gauging tasks consists of a standard video camera, light source, frame grabber and industrial personal computer. In order to minimize hardware costs, a standard 50mm C-mount camera lens and extension tube was used with the video camera. Consideration had been made to use more expensive telecentric optics so that parts placement would not cause a change in magnification with a resulting loss of accuracy. With the 50 mm lens, however, magnification effects were lessened due to the greater standoff distance between camera and part. For image acquisition, a low-cost PCI-bus frame grabber-card was chosen. With this type of card, high-speed video capture is possible due to the very wide bandwidth of the PCI bus. Combined with a Pentium-based PC, rapid image acquisition and analysis can be performed so that every part can be gauged at full production rates. Since the gauging rate exceeds the production rate by a significant factor, a single computer and frame grabber with camera multiplexer can process data in real time from up to four measurement stations simultaneously.

Comparison of a Virtual Game-Day Experience on Varying Devices

Collegiate athletics, particularly football, provide tremendous value to schools through branding, revenue, and publicity. As a result, extensive effort is put into recruiting talented students. When recruiting, home games are exceptional tools used to show a schools unique game-day atmosphere. However, this is not a viable option during the offseason or for off-site visits. This paper explores a solution to these challenges by using virtual reality (VR) to recreate the game-day experience. The Virtual Reality Application Center in conjunction with Iowa State University (ISU) athletics, created a VR application mimicking the game-day experience at ISU. This application was displayed using the worlds highest resolution six-sided CAVETM, an Oculus Rift DK2 computer-driven head mounted display (HMD) and a Merge VR smart phone-driven HMD. A between-subjects user study compared presence between the different systems and a video control. In total, 82 students participated, indicating their presence using the Witmer and Singer questionnaire. Results revealed that while the CAVETM scored the highest in presence, the Oculus and Merge only experienced a slight drop compared to the CAVETM. This result suggests that the mobile ultra-low-cost Merge is a viable alternative to the CAVE TM and Oculus for delivering the game-day experience to ISU recruits.

Fast software implementations of common 3x3 operators

In a typical machine vision algorithm, a few complex operators may account for a significant fraction of the overall execution time. In addition to these, there are usually many simple 3x3 operators which are used over and over. Although individually fairly fast, these operators sometimes dominate the overall execution time because they are used so many times. And because they are fairly fast, little attention is paid to speeding them up. The many previous papers describing the SKIPSM paradigm have concentrated mainly on large-neighborhood operations, because speed improvements are the most dramatic in such cases. In this paper, SKIPSM implementations of some common 3x3 linearly-separable and nonseparable operators are considered. Examples include low-pass (i.e., blurring and smoothing) filters, band-pass filters, high-pass (i.e., edge detector) filters, and gradient operators. Speed comparisons between conventional implementations and SKIPSM implementations are presented.