Titlebook: Learn Computer Vision Using OpenCV; With Deep Learning C Sunila Gollapudi Book 2019 Sunila Gollapudi 2019 Computer Vision.Open CV.Python.De

摘要

978-1-4842-4260-5Sunila Gollapudi 2019

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https://doi.org/10.1007/978-1-4842-4261-2Computer Vision; Open CV; Python; Deep Learning; Artificial intelligence; Image Segmentation; Object Detec

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Artificial Intelligence and Computer Vision,l intelligence (AI) is an enabler that potentially facilitates machines doing everything that humans can do. This includes perceiving, reasoning, rationalizing, and problem-solving while working within a context or interacting with the environment with more efficiency and accuracy. Here, the word .

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人类的发源

Image Manipulation and Segmentation,h a few real-world use cases. A brief introduction of these concepts was already given in Chapter ., so these chapters will take you deeper into the implementation specifics. This chapter, specifically, covers image manipulations and segmentation-related functions that are core to image processing i

可用

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Motion Analysis and Object Tracking,ts in motion, understand techniques to remove background and foreground information, and see real-time tracking options with hands-on implementation steps. The topics in this chapter are an extension of the object detection and recognition techniques you learned about in Chapter . and hence require

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Bressan. Bressan’s notes start with an extensive review of the theory of hyperbolic conservation laws. Then he introduces the vanishing viscosity approach and explains clearly the building blocks of the theory in particular the crucial role of the decomposition by travelling waves. Serre focuses on

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Sunila Gollapudih a Tutorial on Center Manifold Theorem by Bressan. Bressan’s notes start with an extensive review of the theory of hyperbolic conservation laws. Then he introduces the vanishing viscosity approach and explains clearly the building blocks of the theory in particular the crucial role of the decomposi