Template matching is widely used for many applications in image and signal processing. This paper proposes a novel template matching algorithm, called algebraic template matching. Given a template and an input image, algebraic template matching efficiently calculates similarities between the template and the partial images of the input image, for various widths and heights. The partial image most similar to the template image is detected from the input image for any location, width, and height. In the proposed algorithm, a polynomial that approximates the template image is used to match the input image instead of the template image. The proposed algorithm is effective especially when the width and height of the template image differ from the partial image to be matched. An algorithm using the Legendre polynomial is proposed for efficient approximation of the template image. This algorithm not only reduces computational costs, but also improves the quality of the approximated image. It is shown theoretically and experimentally that the computational cost of the proposed algorithm is much smaller than the existing methods.
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http://dx.doi.org/10.1109/tip.2007.901243 | DOI Listing |
iScience
January 2025
Division of Optometry, Health Sciences, City University of London, London EC1V 0HB, UK.
A key property of our environment is the mirror symmetry of many objects, although symmetry is an abstract global property with no definable shape template, making symmetry identification a challenge for standard template-matching algorithms. We therefore ask whether Deep Neural Networks (DNNs) trained on typical natural environmental images develop a selectivity for symmetry similar to that of the human brain. We tested a DNN trained on such typical natural images with object-free random-dot images of 1, 2, and 4 symmetry axes.
View Article and Find Full Text PDFLangmuir
January 2025
Department of Chemistry and Biochemistry, Fordham University, 441 East Fordham Road, The Bronx, New York 10458, United States.
The first protocells are speculated to have arisen from the self-assembly of simple abiotic carboxylic acids, alcohols, and other amphiphiles into vesicles. To study the complex process of vesicle formation, we combined laboratory automation with AI-guided experimentation to accelerate the discovery of specific compositions and underlying principles governing vesicle formation. Using a low-cost commercial liquid handling robot, we automated experimental procedures, enabling high-throughput testing of various reaction conditions for mixtures of seven (7) amphiphiles.
View Article and Find Full Text PDFClin Neuroradiol
January 2025
Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Moorenstraße 5, 40225, Düsseldorf, Germany.
Introduction: Ventriculoperitoneal shunts (VPS) are an essential part of the treatment of hydrocephalus, with numerous valve models available with different ways of indicating pressure levels. The model types often need to be identified on X‑rays to assess pressure levels using a matching template. Artificial intelligence (AI), in particular deep learning, is ideally suited to automate repetitive tasks such as identifying different VPS valve models.
View Article and Find Full Text PDFStem cells adapt to their local mechanical environment by rearranging their cytoskeleton, which underpins the evolution of their shape and fate as well as the emergence of tissue structure and function. Here, in the second part of a two-part experimental series, we aimed to elucidate spatiotemporal cytoskeletal remodeling and resulting changes in morphology and mechanical properties of cells and their nuclei. Akin to mechanical testing of the most basic living and adapting unit of life, i.
View Article and Find Full Text PDFAnimals (Basel)
January 2025
Department of Chemistry and Biochemistry, Florida International University, Miami, FL 33199, USA.
Two convex polyhedra that markedly resemble the head of the flatback sea turtle hatchling are identified. The first example is a zygomorphic tetragonal dodecahedron, while the other, an even better matching structure, is a related tetradecahedron, herein speculated to arise from this particular dodecahedron via known mechanisms gleaned from studies of the behavior of foams. A segmented, biomorphic, convex polyhedral model to address cephalic topology is thus presented stemming from solid geometry, anatomical observations, and a recently computed densest local packing arrangement of fifteen slightly oblate spheroids in which fourteen oblate spheroids surround a central such spheroid.
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