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Remote heart rate measurement using low-cost RGB face video: a technical literature review 被引量:14
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作者 Philipp V. ROUAST Marc T. P. ADAM +2 位作者 raymond chiong David CORNFORTH Ewa LUX 《Frontiers of Computer Science》 SCIE EI CSCD 2018年第5期858-872,共15页
Remote photoplethysmography (rPPG) allows remote measurement of the heart rate using low-cost RGB imaging equipment. In this study, we review the development of the field of rPPG since its emergence in 2008. We also... Remote photoplethysmography (rPPG) allows remote measurement of the heart rate using low-cost RGB imaging equipment. In this study, we review the development of the field of rPPG since its emergence in 2008. We also classify existing rPPG approaches and derive a framework that provides an overview of modular steps. Based on this framework, practitioners can use our classification to design algorithms for an rPPG approach that suits their specific needs. Researchers can use the reviewed and classified algorithms as a starting point to improve particular features of an rPPG algorithm. 展开更多
关键词 affective computing heart rate measurement REMOTE NON-CONTACT camera-based PHOTOPLETHYSMOGRAPHY
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Evolutionary Optimization: Pitfalls and Booby Traps 被引量:7
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作者 Thomas Weise raymond chiong Ke Tang 《Journal of Computer Science & Technology》 SCIE EI CSCD 2012年第5期907-936,共30页
Evolutionary computation (EC), a collective name rithms, is one of the fastest-growing areas in computer science. for a range of metaheuristic black-box optimization algo- Many manuals and "how-to's on the use of ... Evolutionary computation (EC), a collective name rithms, is one of the fastest-growing areas in computer science. for a range of metaheuristic black-box optimization algo- Many manuals and "how-to's on the use of different EC methods as well as a variety of free or commercial software libraries are widely available nowadays. However, when one of these methods is applied to a real-world task, there can be many pitfalls and booby traps lurking certain aspects of the optimization problem that may lead to unsatisfactory results even if the algorithm appears to be correctly implemented and executed, These include the convergence issues, ruggedness, deceptiveness, and neutrality in the fitness landscape, epistasis, non-separability, noise leading to the need for robustness, as well as dimensionality and scalability issues, among others. In this article, we systematically discuss these related hindrances and present some possible remedies. The goal is to equip practitioners and researchers alike with a clear picture and understanding of what kind of problems can render EC applications unsuccessful and how to avoid them from the start. 展开更多
关键词 evolutionary computing problem difficulty OPTIMIZATION META-HEURISTICS
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