应用程序接口(简称 API),又称为应用编程接口,就是软件系统不同组成部分衔接的约定。

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报告题目:

OpenCV 4.x and more new tools for CV R&D

报告简介:

当今的计算机视觉算法大多采用深度学习技术,这种技术既需要计算,又需要数据。现代硬件通常是异构的,并且越来越难以有效地编程。OpenCV 4提供了新的功能来解决这些新的挑战,并为开发人员提供了方便的API来处理复杂性。您可以在一个流行的深度学习框架(Caffe/TensorFlow/Darknet/Torch/ONNX格式兼容框架)中训练神经网络模型,并使用OpenCV运行它,而不依赖于origin框架。本教程涵盖OpenCV 4特性介绍,深度学习模块使用C++中的代码示例、Python、Java和JavaScript(EnScript绑定)。我们还将回顾深网络的不同计算后端,如OpenCL和Intel®推理机。

嘉宾介绍:

Alexander Bovyrin,Nikita Manovich,Sergei Nosov,Dmitry Kurtaev。

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最新论文

We introduce an unsupervised approach for correcting highly imperfect speech transcriptions based on a decision-level fusion of stemming and two-way phoneme pruning. Transcripts are acquired from videos by extracting audio using Ffmpeg framework and further converting audio to text transcript using Google API. In the benchmark LRW dataset, there are 500 word categories, and 50 videos per class in mp4 format. All videos consist of 29 frames (each 1.16 s long) and the word appears in the middle of the video. In our approach we tried to improve the baseline accuracy from 9.34% by using stemming, phoneme extraction, filtering and pruning. After applying the stemming algorithm to the text transcript and evaluating the results, we achieved 23.34% accuracy in word recognition. To convert words to phonemes we used the Carnegie Mellon University (CMU) pronouncing dictionary that provides a phonetic mapping of English words to their pronunciations. A two-way phoneme pruning is proposed that comprises of the two non-sequential steps: 1) filtering and pruning the phonemes containing vowels and plosives 2) filtering and pruning the phonemes containing vowels and fricatives. After obtaining results of stemming and two-way phoneme pruning, we applied decision-level fusion and that led to an improvement of word recognition rate upto 32.96%.

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