Learning programming in early introductory classes is challenging for first year university students, and introducing parallel programming (PDC) in early classes along with traditional sequential programming is even more challenging. Unplugged activities may help alleviate some of the difficulties for students. Unplugged activities have been shown to increase student interest, and to enhance student understanding of CS programming concepts. We have used unplugged activities to teach PDC concepts before introducing parallel programming. Our experiences show that using unplugged activities to introduce the PDC concepts reduce the barrier to learn parallel programming.

Point-matching is a widely applied image registration method and many algorithms have been developed. Registration of 2-D electrophoresis gels is an important problem in biological research that presents many of the technical difficulties that beset point-matching: large numbers of points with variable densities, large nonrigid transformations between point sets, paucity of structural information and large numbers of unmatchable points (outliers) in either set. In seeking the most suitable algorithm for gel registration we have evaluated a number of approaches for accuracy and robustness in the face of these difficulties. Using synthetic images we test combinations of three algorithm components: correspondence assignment, distance metrics and image transformation. We show that a version of the iterated closest point (ICP) algorithm using a non-Euclidean distance metric and a robust estimation of transform parameters provides best performance, equalling SoftAssign in the presence of moderate image distortion, and providing superior robustness against large distortions and high outlier proportions. From this evaluation we develop a gel registration algorithm based on robust ICP and a novel distance metric combining Euclidean, shape context and image-related features. We demonstrate the accuracy of gel matching using synthetic distortions of real gels and show that robust estimation of transform parameters using M-estimators can enforce inverse consistency, ensuring that matching results are independent of the order of the images.

Proteomics research relies heavily on electrophoresis gels, which are complex images containing many protein ‘spots’. The identification and quantification of these spots is a bottleneck in the proteomics workflow. We describe a statistical model of protein spot appearance that is both general enough to represent unusual spots, and specific enough to introduce constraints on the interpretation of complex images. We propose a robust method of automatic model construction that is used to circumvent manual model construction which is subjective and time-consuming. We show that the statistical model of spot appearance is able to fit to image data more closely than the commonly used spot parameterisations which are based solely on Gaussian and diffusion formulations.

Pointdistributionmodelshavebeensuccessfulindescribingtheshape constraintsontwodimensionalobjectsforshapedescriptionandimage search. It is often the case that a class of objects to be modelled contains certain features which may be wholly present or absent in difierentinstances. Moustachesonfacesareacommonexample. Here we describe a method of coding the presence or absence of a feature within the PDM framework. We show that the method captures the intermittentnatureofthefeatureasoneofthemodesofvariation,and demonstrate that, where features are intermittently present, greater model speciflcity is achieved.