Results of research in public health highly depend on the validity of data collection and internal research processes. To ensure validity, maintaining records of all assets in the research is very important. Researchers should be able to diagnose problems by tracing back the processes that leads to the problem. In this study, we design and implement BioRio system, a portable, flexible and secure information system for public health research that will reduce human errors, and increase traceability and efficiency.

The authors present a curve-fitting method for the recovery of elongated regions in aerial images by analyzing the local image features that constitute these regions. Elongation is defined in terms of an axial model that describes a broad class of objects found in aerial scenes. The authors fit the model using a perceptual strip clustering that maintains constraints on feature connectivity and curvilinear alignment. Connectivity is obtained from internal representations of closest-point properties, specifically, a thresholded version of the Gabriel graph. This is shown to offer the advantages of both the minimum spanning tree and the Delauney triangulation, while being easily computable from finite neighborhood operations. An iterative curve fitting is used for the clustering of maximally elongated partitions, incorporating the perceptual constraints. This has the ability to interpolate across missing elements with a smooth curve, while ignoring nearby complicating structures and systematic gross noise. The general parametric strip model is described, with specific algorithms and examples given for lines and circles. A complete analysis of linear shape in low-level region segmentations is presented, with results for a suburban road network.>

This paper surveys publications, reports, and articles dealing with automated visual inspection for industry. The references are organized according to their contents: overview and discussions, rationales, components and design considerations, commercially available systems, applications. A number of applications and their inspection methodologies are discussed in detail: the inspection of printed circuit boards, photomasks, integrated circuit chips. Other inspection applications are listed as a bibliography. A list of selectively annotated references in commercially available visual inspection tools is also included.

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.

The problem considered is the development of a method of scene analysis that employs a descriptive approach in the analysis of pictures. In an effort to facilitate implementation of the algorithm to a wide range of picture classes, a description of the scene is read in as data in the form of a tree structure that guides the search for objects from the largest to the smallest. The algorithm has the following features: the analysis of the scene is top-down; feature extraction and pattern recognition are combined in a reinforcing system; the algorithm contains a description of the class of scenes to be analyzed; region enumeration techniques are used in primitive object identification; principal parameters in the program are self-adjusting; and the concept of ``field of vision'' is used to locate the boundaries of objects. The algorithm has been implemented and tested on posteroanterior (PA) chest radiograms, anteroposterior (AP) knee radiograms, and lateral brain scans. The analysis of PA chest radiograms is used to describe the implementation of the program. To date, the algorithm has been tested only on radiographic images; however, the method should also be applicable to other image classes.

In this paper we study feedback in sequential machines that are implemented with trigger or set-reset flip-flop memory elements. A method is given for determining when a machine can be implemented with either trigger or set-reset flip-flop memory elements where the feedback in the machine is given by a Boolean function f. Several results are presented that compare the feedback when a machine is implemented with different memory elements. It is shown that if a sequential machine can be implemented with unit delay memory elements with feedback function f, then it can be realized with set-reset flip-flop with the same feedback function f. For completely specified machines, it is shown that if a machine implemented with trigger flip-flop has no feedback, then the some machine implemented with unit delays will have feedback. Conversely, if an implementation with unit delays has no feedback, then an implementation with trigger flip-flops will have feedback.

Hartmanis and Stearns defined the concept of an inessential error in their study of errors in sequential machines and represented such errors by means of an error partition Π E. Although they showed that ΠE could not be determined using only the usual partition pair algebras, they did not provide a means by which it could be determined. The purpose of this note is to develop an algorithm for the determination of ΠE for a given machine.