The estimation of the direction of visual attention is critical to a large number of interactive systems. This paper investigates the cross-modal relation of the position of one's feet (or standing stance) to the focus of gaze. The intuition is that while one CAN have a range of attentional foci from a particular stance, one may be MORE LIKELY to look in specific directions given an approach vector and stance. We posit that the cross-modal relationship is constrained by biomechanics and personal style. We define a stance vector that models the approach direction before stopping and the pose of a subject's feet. We present a study where the subjects' feet and approach vector are tracked. The subjects read aloud contents of note cards in 4 locations. The order of `visits' to the cards were randomized. Ten subjects read 40 lines of text each, yielding 400 stance vectors and gaze directions. We divided our data into 4 sets of 300 training and 100 test vectors and trained a neural net to estimate the gaze direction given the stance vector. Our results show that 31% our gaze orientation estimates were within 5°, 51% of our estimates were within 10°, and 60% were within 15°. Given the ability to track foot position, the procedure is minimally invasive.

We analyze the moire patterns in halftone images scanned by a popular commercial scanner. We show that the non uniform sampling scheme employed by the scanner introduces extra aliasing components compared to uniform sampling and thus complicates the moire patterns formed. The analysis applies to other scanners that employ non uniform sampling. We also suggest methods for suppressing the moire patterns in scanned halftones.

This paper concerns the problem of obtaining subpixel estimates of the locations of straight edges in binary digital images using dithering. By adding uniformly distributed independent random noise it is shown that estimation bias may be removed and that the estimation variance is inversely proportional to the length of the line segment. The sensitivity to incorrect dither amplitude is calculated, and implementation is discussed.>

It is generally acknowledged that the production of quality user interfaces requires a thorough understanding of the user and that this involves evaluating the interface by observing the user working with the system, or by performing human factors experiments. Such methods traditionally involve the use of video tape, protocol analysis, critical incident analysis, etc. These methods require time consuming analyses and may be invasive. In addition, the data obtained through such methods represent a relatively small portion of the use of a system. An alternative approach is to record all user input and systems output, i.e., log the user session. Such transcripts can be collected automatically and non-invasively over a long period of time. Unfortunately, this produces voluminous amounts of data. There is, therefore, a need for tools and techniques that allow an evaluator to identify potential performance and usability problems from such data. It is hypothesized that repetition of user actions is an important indicator of potential user interface problems.

A computational framework is presented to show how Dempster-Shafer (D-S) evidence theory can be applied to a hierarchically structured hypothesis space in a computer vision system. It is shown how to make use of partial and locally ambiguous information at different levels of abstraction to achieve a reliable interpretation. It is also shown how the reasoning process can make use of spatial relationships among pieces of visual evidence to strengthen the reasoning results. Because a frame of discernment consists of a set of mutually exclusive visual events, the reasoning process is visual evidence driven, and the goal is to assign labels to visual events. However, at each level of the event hierarchy it is possible to pose additional D-S problems involving spatial relationships among hypotheses at that level and other evidence. The results of these subsidiary D-S problems contribute to the belief functions for the original hypotheses at that level. Thus, a framework for incorporating a relational model into an event-driven reasoning process is formed. An implementation example is given.>

A set of multispectral image context classification techniques are discussed which are based on a recursive algorithm for optimal estimation of the state of a two-dimensional discrete Markov random field. The three recursive algorithms are forms of dynamic programming. Because the estimation equations of the recursive algorithm are quite simple, the computation complexity of the approach is low. It is shown that recursive contextual classification can improve classification performance, as compared to noncontextual classification. In addition, this algorithm has the advantage over other techniques in that it handles multispectral data naturally and simultaneously.>

As the complexity of human-computer interfaces increases, those who use these interfaces as well as those reponsible for their design have recognized an urgent need for substantive research in the human factors of software development [2], [5]. Because of the magnitude of the task of producing software for individual human-computer interfaces, appropriate tools are needed for defining and improving such interfaces, both in research and production environments. This paper describes the research being carried out to construct DMS (Dialogue Management System), which is a complete system for defining, modifying, executing, and metering human-computer dialogues.

Recently geologists have been recognizing the importance of line or edge-like features called lineaments that are commonly detected in Landsat terrain reconnaissance. Lineaments are piecewise discontinuous global lines or edges of low curvature, and they generally have widely varying widths or edge characteristics that make them difficult to detect.

Optimal decision strategies such as Bayes and Neyman-Pearson require the computation of likelihood ratios that are difficult to compute in all but a few special cases. In practice, unfounded assumptions are frequently made about the nature of the pattern classes so that these strategies can be used. In this correspondence suboptimal decision strategies are explored that are attractive because they are easy to compute. These offer two rather unusual advantages. If, during the operation of the classifier a measurement is undefined or too difficult to make, it is easy to alter the dimensionality of the decision rule. Furthermore, it is possible to use different sets of features for testing different classes so that dimensionality can be minimized rather easily. Normally the features used for each class are "specialists" in discriminating that class from the mixture of remaining classes.

In a number of applications of image processing, much information about objects or textures in the image can be obtained by sequential analysis of individual scan lines.

In this paper a structure for a contextual cursive script recognition system is presented. This system makes use of letter context to determine word length, letter segmentation, and character identity to achieve contextual recognition at the word level. The system consists of a character recognizer that presents a set of best alternatives for each character to a contextual postprocessor whose task it is to determine the correct word.

This paper describes a special-purpose character recognition system which uses contextual information for the recognition of words from any given dictionary of words. Previous techniques that utilized context involved letter transition probabilities of digrams and trigrams. This research introduces the concept of binary digrams which overcomes some of the problems of past approaches. They can be used to extract offectively the "syntax" of the dictionary while requiring very modest amounts of storage. A computationally feasible procedure is described which allows the accuracy requirements of the character recognizer to be relaxed if it is followed by a contextual postprocessor. The modified recognition system is allowed to output several alternatives for each character, while the postprocessor selects the proper string of characters by having access to both the dictionary and the dictionary syntax. A theoretical estimate of the recognition rate is derived, and experimental results demonstrate the ability of the system to achieve low error and rejection rates.

A general method is proposed for decoding any cyclic binary code at extremely high speed using only modulo2adders and threshold elements, and the decoders may be designed for maximum-likelihood decoding. The number of decoding cycles is a fraction of the number of digits in the code word.