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Full-Text Articles in Engineering
Comprehensive Analysis Of Edge Detection In Color Image Processing, Shu-Yu Zhu, Konstantinos N. Kostas Plataniotis, Anastasios N. Venetsanopoulos
Comprehensive Analysis Of Edge Detection In Color Image Processing, Shu-Yu Zhu, Konstantinos N. Kostas Plataniotis, Anastasios N. Venetsanopoulos
Electrical Engineering and Computer Science Faculty Publications
Various approaches to edge detection for color images, including techniques extended from monochrome edge detection as well as vector space approaches, are examined. In particular, edge detection techniques based on vector order statistic operators and difference vector operators are studied in detail. Numerous edge detectors are obtained as special cases of these two classes of operators. The effect of distance measures on the performance of different color edge detectors is studied by employing distance measures other than the Euclidean norm. Variations are introduced to both the vector order statistic operators and the difference vector operators to improve noise performance. They …
Robust Partial Least-Squares Regression: A Modular Neural Network Approach, Thomas M. Mcdowall, Fredric M. Ham
Robust Partial Least-Squares Regression: A Modular Neural Network Approach, Thomas M. Mcdowall, Fredric M. Ham
Electrical Engineering and Computer Science Faculty Publications
We have developed a robust Partial Least-Squares Regression (PLSR) neural network approach to statistical calibration model development. Generalized neural network learning rules derived from a weighted statistical representation error criterion that grows less than quadratically are presented. This optimization criterion allows for higher-order statistics associated with the inputs to be taken into account and also serves to robustify the results when the empirical data contains impulsive and colored noise and outliers. The learning rules presented are considered generalized because they can be used to implement several specialized cases including: robust PLSR, linear PLSR, weighted least-squares, and variance scaling. The same …
Obscured Object Detection Via Bayesian Target Modeling Techniques, Rufus H. Cofer
Obscured Object Detection Via Bayesian Target Modeling Techniques, Rufus H. Cofer
Electrical Engineering and Computer Science Faculty Publications
Underground objects are by nature often severely obscured although the general character of the intervening random media may be reasonably understood. The task of detecting these underground objects also implies that their exact location and or orientation is not known. To partially counter these difficulties, one may; however, be given a model of the target of interest, e.g. a particular tank type, a water pipe, etc. To set up a quality framework for solution of the above problem, this paper utilizes the paradigm of Bayesian decision theory that promises minimum error detection given that certain probability density functions can be …
Design Of Distortion-Invariant Correlation Filters Using Supervised Learning, Samuel Peter Kozaitis, Rufus H. Cofer, Wesley E. Foor
Design Of Distortion-Invariant Correlation Filters Using Supervised Learning, Samuel Peter Kozaitis, Rufus H. Cofer, Wesley E. Foor
Electrical Engineering and Computer Science Faculty Publications
We designed binary phase-only filters from a training set of images using a statistical approach. We forced images into clusters and designed filters to recognize objects from that cluster. We report on results obtained by computer simulation comparing the performance of filters to recognize objects from clusters of one and two classes.
Bayesian Defeat Of Camouflage, Rufus H. Cofer
Bayesian Defeat Of Camouflage, Rufus H. Cofer
Electrical Engineering and Computer Science Faculty Publications
A new technique is shown for refining and reducing incoming camouflage data based upon the Bayesian paradigm. Innovation is displayed in use of a statistical conditioning sequence that avoids the need to form target features from the data. The result is a simplified and more accurate probabilistic indication of actual target presence. This probabilistic indication can then be incorporated into a variety of target detection scenarios or, alternately, to form the basis of a theoretically optimal Bayesian target detector. Numeric simulation is presented to show the effectiveness of the technique against simulated camouflage
Explanation Mode For Bayesian Automatic Object Recognition, Thomas L. Hazlett, Rufus H. Cofer, Harold K. Brown
Explanation Mode For Bayesian Automatic Object Recognition, Thomas L. Hazlett, Rufus H. Cofer, Harold K. Brown
Electrical Engineering and Computer Science Faculty Publications
One of the more useful techniques to emerge from AI is the provision of an explanation modality used by the researcher to understand and subsequently tune the reasoning of an expert system. Such a capability, missing in the arena of statistical object recognition, is not that difficult to provide. Long standing results show that the paradigm of Bayesian object recognition is truly optimal in a minimum probability of error sense. To a large degree, the Bayesian paradigm achieves optimality through adroit fusion of a wide range of lower informational data sources to give a higher quality decision - a very …