This paper introduces a Discrete Fourier Transform (DFT)-based algorithm to extract the Electric Network Frequency (ENF) information from an audio recording for use in audio authentication. The basic idea of the proposed algorithm is to calculate the specific spectral lines by DFT in the frequency domain at the desired frequency point instead of throughout the entire frequency band. Then a binary search technique is employed to search the next desired frequency bin to repeat the spectral line calculation until the hidden ENF information is extracted. The purpose is to improve the accuracy and precision of conventional ENF extraction methods and also to enhance the calculation efficiency. Both simulated audio signals with different signal-to-noise ratios (SNRs) and actual audio recordings are studied to verify the performance of the proposed algorithm. Two error-evaluation criteria, frequency offset and frequency bias, are defined to evaluate the algorithm performance on accuracy and precision. The test results and the error evaluation prove the validation and demonstrate the improvement of the proposed algorithm.

X-ray scanners are considered one of the best technologies for detecting illicit materials because of their ability to characterize a material at the molecular and atomic levels, and also because of their relatively inexpensive cost. Using X-ray technology, it is possible to determine a material's density and effective atomic number, or$Z_ eff$-related information. In theory, an illicit material can be identified using those two pieces of information. The$R$-$L$technology developed at Virginia Tech is the first true multisensing technology for explosive detection. It uses X-ray dual-energy transmission and X-ray scatter technologies to obtain characteristic values of an object; i.e.,$R$and$L$. The material type of this object can then be determined using the$R$-$L$plane.$R$is related to$Z_ eff$and is obtained from dual-energy transmission signals.$L$is related to density, and is obtained using transmission and scatter signals. Compared to single-sensing technologies and pseudo-multisensing technologies,$R$-$L$technology should provide a much higher level of detection accuracy. However, the$R$and$L$values can only be computed from an object's true gray levels, which are defined as the measured gray levels of an object in different sensing modalities when it is not overlapped with any other objects. Because an object in a bag is always overlapped with many other objects, being able to identify the object of interest and remove the overlap effects becomes the key issue in determining the true gray levels of that object. This paper focuses on the development of an image processing system to determine an object's true gray levels in all the sensing modalities used in this work.

This paper presents a real-time algorithm for color sorting edge-glued panel parts. The algorithm consists of color class training and real-time sorting procedures. The algorithm uses 3-D histograms to characterize the surface color of a part's face as well as a color class and employs the minimum-distance classifier to label the face. In addition to real-time sorting, a technique is used to select the better of the part's two faces based on class priorities and color difference values for the two part faces. This algorithm has been tested in a plant, and the sorting results are satisfactory.

This paper describes the use of the MORRPH board (an FPGA-based CCM) in an industrial machine vision system. The system is designed to solve the color sorting problem in the furniture manufacturing industry. This entails classifying wooden samples (staves) into one of several predetermined color classes, so that all members of a particular class "match". In this system, two MORRPH boards are used to perform the "early" processing, including: field-of-view operators, light-intensity monitoring, shading correction background extraction, color-quantization, gray-scale channel generation, and histogram generation. This functionality utilizes the following MORRPH board features: open support sockets are filled with various SRAMs depending on necessary requirements, functionality is time-multiplexed by reprogramming the FPGAs, one of three I/O Busses is used for high-bandwidth input of color camera data, the ISA Bus interface is used for low-bandwidth output of histogram data, user-programmable ports allow simple modification of operating parameters. The use of two MORRPH boards to perform the early processing provides almost 200 times the overall system throughput when compared to a system that used two 486-based PCs to perform the "early" processing.

To fully optimize the value of material produced from a hardwood log requires information about type and location of internal defects in the log. This paper describes a prototype vision system that automatically locates and identifies certain classes of defects in hardwood logs. This system uses computer tomograph (CT) imagery. The system uses a number of processing steps. A set of basic features are defined to capture basic 3-D characteristics of wood defects. For 3-D object (defect) recognition, a set of hypothesis tests are employed that use this set of features. To further help cope with the above mentioned variability, the Dempster-Shafer theory of evidential reasoning is used to classify defect objects. Results of preliminary experiments employing two different types of hardwood logs are given.

The MORRPH architecture is a general purpose reconfigurable processing unit, primarily designed to solve real time 2D image processing problems. Its robust architecture allows it to be used for other applications including 1D signal processing, 2D cellular automata problems, and 3D image processing. The modular, open ended architecture consists of an M/spl times/N rectangular mesh of processing elements (PEs), called the processing array. Each PE contains a single field programmable gate array (FPGA) chip and interconnections for several support chips. The FPGA chips within the PEs provide an array of logic resources, consisting of combinational logic functions, flip flops and internal chip routing resources. The types of support chips which are included in the PEs are not fixed, they are determined by the requirements of the computational task performed by the MORRPH. These memory, arithmetic, or processing support chips are specified and assembled on the MORRPH board for each particular application that is developed. Currently, the MORRPH architecture is implemented as an adapter card for the Industry Standard Architecture (ISA) computer bus. A constructed prototype with a 23 array of PEs is used in a current machine vision system to perform low level image processing functions. A significant performance increase is obtained by using the MORRPH as a preprocessing unit for the host processing computer. The MORRPH architecture is shown to be an inexpensive solution for relatively simple or very complex real time processing tasks.

A computer vision system for locating and identifying defects on rough hardwood lumber in a species-independent manner is described. The system consists of three modules: a low-level module, a mid-level module, and a high-level module. The low-level module is a segmentation module that segments an input board image using a histogram-based thresholding method. The mid-level module eliminates small noise regions, merges similar adjacent regions, and computes region properties of merged regions. The high-level module identifies the defects present in each of the regions passed to it using a rule-based approach. The system is designed to be used in an automatic edger and trimmer for hardwood lumber in order to optimize the value of the hardwood boards processed.>

To develop robust computer vision system methodologies, ones that have a range of applicability, early vision operators capable of matching a level of human perceptual performance are required. This implies the need to develop operators that can perform a variety of image analysis tasks in a unified and consistent fashion. These image analysis tasks include finding boundaries between regions of uniform but different gray levels and textures. They also include gauging Gestalt grouping concepts such as uniformity and proximity, so that these concepts are incorporated into the segmentation process. Lastly, the operators should be able to gauge information that allows the characteristics of surfaces to be made explicit, e.g., so-called shape-from-shading and shape-from-texture methods. Developing robust methods for performing these tasks corresponds to developing a quantitative model of human preattentive vision. A basis for such a quantitative model is proposed that is contrary to current theories in computer vision but that seemingly provides a unified method for image analysis tasks and to explain a number of perceptual phenomena.>

Continued increases in the cost of materials and labor make it imperative for furniture manufacturers to control costs by improved yield and increased productivity. This paper describes an Automated Lumber Processing System (ALPS) that employs computer tomography, optical scanning technology, the calculation of an optimum cutting strategy, and a computer-driven laser cutting device. While certain major hardware components of ALPS are already commercially available, a major missing element is the automatic inspection system needed to locate and identify surface defects on boards. This paper reports research aimed at developing such an inspection system. The basic strategy is to divide the digital image of a board into a number of disjoint rectangular regions and classify each independently. This simple procedure has the advantage of allowing an obvious parallel processing implementation. The study shows that measures of tonal and pattern related qualities are needed. The tonal measures are the mean, variance, skewness, and kurtosis of the gray levels. The pattern related measures are those based on cooccurrence matrices. In this initial feasibility study, these combined measures yielded an overall 88.3 percent correct classification on the eight defects most commonly found in lumber. To minimize the number of calculations needed to make the required classifications a sequential classifier is proposed.

An evaluation of the ability of four texture analysis algorithms to perform automatic texture discrimination will be described. The algorithms which will be examined are the spatial gray level dependence method (SGLDM), the gray level run length method (GLRLM), the gray level difference method (GLDM), and the power spectral method (PSM). The evaluation procedure employed does not depend on the set of features used with each algorithm or the pattern recognition scheme. Rather, what is examined is the amount of texturecontext information contained in the spatial gray level dependence matrices, the gray level run length matrices, the gray level difference density functions, and the power spectrum. The comparison will be performed in two steps. First, only Markov generated textures will be considered. The Markov textures employed are similar to the ones used by perceptual psychologist B. Julesz in his investigations of human texture perception. These Markov textures provide a convenient mechanism for generating certain example texture pairs which are important in the analysis process. In the second part of the analysis the results obtained by considering only Markov textures will be extended to all textures which can be represented by translation stationary random fields of order two. This generalization clearly includes a much broader class of textures than Markovian ones. The results obtained indicate that the SGLDM is the most powerful algorithm of the four considered, and that the GLDM is more powerful than the PSM.