Determining Object Orientations from Run Length Encodings

Run length codes are widely used for image compression, and efficient algorithms have been devised for identifying objects and calculating geometric features directly from these codes. However, if the image objects are rotated it can be difficult to determine their orientation and position so that they can be grasped by manipulators. This paper describes a method for structural determination of object orientation directly from the run length codes of successive image scan lines. An algorithm is described that makes use of the equations of object boundary segments to form hypotheses about object orientations that are refined as scanning progresses. 2-dimensional polygonal objects are discussed, and it is assumed that objects do not touch or overlap, although the algorithm could be extended to include those situations

Interactive Tools: Making UIMS Usable

The earliest UIMS provided primarily run-time facilities for interface management and a set of programming tools for the development of application from the implementation requirements with which many tool designers have approached UIMS design, there are also methodological requirements that have been seriously neglected. One reason is that interface design methodology is poorly understood and rarely axiomatic. Nevertheless, it is important that we formulate methodological theories and provide UIMS with tools that support them. This paper proposes a storyboard metaphor for the conceptual design of human-computer interfaces

ONE-DIMENSIONAL EDGE DETECTION AND REPRESENTATION

One reason no single edge detector has been found that satisfies the requirements of a wide range of image analysis applications is that existing edge detectors often produce unjustifiable interpretations of edge regions without sufficient contextual or external information. One- dimensional edge semantics are examined in detail, and a one-dimensional edge-detector is proposed whose output is not just a local edge point assertion but a data structure. This data structure contains alternative interpretations of edge crossections as global gray level transition regions of variable widths. The detector is parameter-free, and a set of descriptive edge features is proposed

Evaluating the impact of scoring parameters on the structure of intra-specific genetic variation using RawGeno, an R package for automating AFLP scoring

<p>Abstract</p> <p>Background</p> <p>Since the transfer and application of modern sequencing technologies to the analysis of amplified fragment-length polymorphisms (AFLP), evolutionary biologists have included an increasing number of samples and markers in their studies. Although justified in this context, the use of automated scoring procedures may result in technical biases that weaken the power and reliability of further analyses.</p> <p>Results</p> <p>Using a new scoring algorithm, RawGeno, we show that scoring errors – in particular "bin oversplitting" (i.e. when variant sizes of the same AFLP marker are not considered as homologous) and "technical homoplasy" (i.e. when two AFLP markers that differ slightly in size are mistakenly considered as being homologous) – induce a loss of discriminatory power, decrease the robustness of results and, in extreme cases, introduce erroneous information in genetic structure analyses. In the present study, we evaluate several descriptive statistics that can be used to optimize the scoring of the AFLP analysis, and we describe a new statistic, the information content per bin (I_bin) that represents a valuable estimator during the optimization process. This statistic can be computed at any stage of the AFLP analysis without requiring the inclusion of replicated samples. Finally, we show that downstream analyses are not equally sensitive to scoring errors. Indeed, although a reasonable amount of flexibility is allowed during the optimization of the scoring procedure without causing considerable changes in the detection of genetic structure patterns, notable discrepancies are observed when estimating genetic diversities from differently scored datasets.</p> <p>Conclusion</p> <p>Our algorithm appears to perform as well as a commercial program in automating AFLP scoring, at least in the context of population genetics or phylogeographic studies. To our knowledge, RawGeno is the only freely available public-domain software for fully automated AFLP scoring, from electropherogram files to user-defined working binary matrices. RawGeno was implemented in an R CRAN package (with an user-friendly GUI) and can be found at <url>http://sourceforge.net/projects/rawgeno</url>.</p