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In this correspondence, some image transforms and features such as projections along linear patterns, convex hull approximations, Hough transform for line detection, diameter, moments, and principal components will be considered. Specifically, we present algorithms for computing these features which are suitable for implementation in image analysis pipeline architectures. In particular, random access memories and other dedicated hardware components which may be found in the implementation of classical techniques are not longer needed in our algorithms. The effectiveness of our approach is demonstrated by running some of the new algorithms in conventional short-pipelines for image analysis. In related papers, we have shown a pipeline architecture organization called PPPE (Parallel Pipeline Projection Engine), which unleashes the power of projection-based computer vision, image processing, and computer graphics. In the present correspondence, we deal with just a few of the many algorithms which can be supported in PPPE. These algorithms illustrate the use of the Radon transform as a tool for image analysis.