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Titlebook: Applied Pattern Recognition; Horst Bunke,Abraham Kandel,Mark Last Book 2008 Springer-Verlag Berlin Heidelberg 2008 Markov.Statistica.Wavel

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發(fā)表于 2025-3-21 19:06:49 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Applied Pattern Recognition
影響因子2023Horst Bunke,Abraham Kandel,Mark Last
視頻videohttp://file.papertrans.cn/161/160027/160027.mp4
發(fā)行地址Recent results in applied pattern recognition.Includes supplementary material:
學(xué)科分類Studies in Computational Intelligence
圖書封面Titlebook: Applied Pattern Recognition;  Horst Bunke,Abraham Kandel,Mark Last Book 2008 Springer-Verlag Berlin Heidelberg 2008 Markov.Statistica.Wavel
影響因子.A sharp increase in the computing power of modern computers, accompanied by a decrease in the data storage costs, has triggered the development of extremely powerful algorithms that can analyze complex patterns in large amounts of data within a very short period of time. Consequently, it has become possible to apply pattern recognition techniques to new tasks characterized by tight real-time requirements (e.g., person identification) and/or high complexity of raw data (e.g., clustering trajectories of mobile objects). The main goal of this book is to cover some of the latest application domains of pattern recognition while presenting novel techniques that have been developed or customized in those domains. .
Pindex Book 2008
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Facial Image Processing have attracted considerable attention in the last decades. In this chapter we focus on a strict view of facial image processing, i.e. transforming an input facial image into another and involving no high-level semantic classification like face recognition. A brief overview of facial image processin
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4D Segmentation of Cardiac Data Using Active Surfaces with Spatiotemporal Shape Priorsatic segmentation of cardiac MR sequences where the blood pool of the left ventricle is segmented and important diagnostic measurements extracted from the segmentation. When used in a clinical setting, our algorithm could greatly alleviate the time that clinicians must spend working with the acquire
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發(fā)表于 2025-3-22 09:28:32 | 只看該作者
Measuring Similarity Between Trajectories of Mobile Objectsis constantly growing. Mining spatio-temporal data can direct products and services to the right customers at the right time; it can also be used for resources optimization or for understanding mobile patterns. In this chapter, we cluster trajectories in order to find movement patterns of mobile obj
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,Vorschriften für die Untersuchung,inations (shadows, high-lights, non-white lights) and in cluttered backgrounds. The LG Graph embeds both the local information (the shape of facial feature is stored within the local graph at each node) and the global information (the topology of the face). The LGG approach for detecting faces with
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