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简介:

使用Python、OpenCV库构建计算机视觉的实际应用程序。这本书讨论了不同方面的计算机视觉,如图像和对象检测,跟踪和运动分析及其应用实例。作者首先介绍了计算机视觉,然后使用Python从头开始创建OpenCV。下一节讨论专门的图像处理和分割,以及计算机如何存储和处理图像。这涉及到使用OpenCV库进行模式识别和图像标记。接下来,将使用OpenCV处理对象检测、视频存储和解释,以及人类检测。跟踪和运动也进行了详细的讨论。该书还讨论了如何使用CNN和RNN创建复杂的深度学习模型。最后对计算机视觉的应用现状和发展趋势进行了总结。

阅读本书之后,您将能够理解并使用Python、OpenCV实现计算机视觉及其应用程序。您还将能够使用CNN和RNN创建深度学习模型,并了解这些前沿的深度学习架构是如何工作的。

您将学习

  • 了解什么是计算机视觉以及它在智能自动化系统中的整体应用。
  • 探索构建计算机视觉应用程序所需的深度学习技术。
  • 使用OpenCV、Python和NumPy中的最新技术构建复杂的计算机视觉应用程序。
  • 创建实际的应用程序,如:人脸检测和识别,手写识别,对象检测,跟踪和运动分析。

作者:

Sunila Gollapudi是Broadridge Financial Solutions India (Pvt)有限公司的执行副总裁。拥有超过17年的架构、设计和开发以客户为中心、企业级和数据驱动的解决方案的经验。在过去的十年中,她主要专注于银行和金融服务领域,是一名数据鉴赏家和架构师,擅长设计一个通过分析最大化数据价值的整体数据策略。她的专长包括通过综合业务和领域驱动因素以及大数据工程和分析领域的新兴技术趋势来构建整体智能自动化战略;领导针对CI/CD的云迁移和DevOps战略;指导应用程序现代化、重用和技术标准化计划。

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This paper proposes a deep learning framework for classification of BBC television programmes using audio. The audio is firstly transformed into spectrograms, which are fed into a pre-trained convolutional Neural Network (CNN), obtaining predicted probabilities of sound events occurring in the audio recording. Statistics for the predicted probabilities and detected sound events are then calculated to extract discriminative features representing the television programmes. Finally, the embedded features extracted are fed into a classifier for classifying the programmes into different genres. Our experiments are conducted over a dataset of 6,160 programmes belonging to nine genres labelled by the BBC. We achieve an average classification accuracy of 93.7% over 14-fold cross validation. This demonstrates the efficacy of the proposed framework for the task of audio-based classification of television programmes.

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This paper proposes a deep learning framework for classification of BBC television programmes using audio. The audio is firstly transformed into spectrograms, which are fed into a pre-trained convolutional Neural Network (CNN), obtaining predicted probabilities of sound events occurring in the audio recording. Statistics for the predicted probabilities and detected sound events are then calculated to extract discriminative features representing the television programmes. Finally, the embedded features extracted are fed into a classifier for classifying the programmes into different genres. Our experiments are conducted over a dataset of 6,160 programmes belonging to nine genres labelled by the BBC. We achieve an average classification accuracy of 93.7% over 14-fold cross validation. This demonstrates the efficacy of the proposed framework for the task of audio-based classification of television programmes.

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