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手语识别的分段鲁棒表示、匹配和建模
Segmentation-robust Representations, Matching, and Modeling for Sign Language Recognition |
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| 课程网址: |
http://videolectures.net/gesturerecognition2011_sarkar_segmentati...
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| 主讲教师: |
Sudeep Sarkar
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| 开课单位: |
南佛罗里达大学
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| 开课时间: |
2011-08-24
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| 课程语种: |
英语
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| 中文简介: |
在连续符号语言识别系统的设计中，将真正的符号与过渡符号区分开来，当签名者从一个符号移动到另一个符号时，所做的外来运动是一个严重的障碍。分割和闭塞的模糊性进一步加剧了这一问题，导致错误传播到更高的层次。本文将介绍我们在表示和匹配方法方面的经验，特别是那些能够处理低级分割方法中的错误和句子中符号分割的不确定性的方法。我们制定了一个新的框架，可以解决这两个问题：（i）在缺乏训练数据的情况下，使用基于嵌套层的动态规划方法；（i i）在我们有符号统计模型的情况下，使用基于HMM的通用方法来处理多个可能的观察结果。我们还将讨论一种自动方法，以弱无监督的方式从连续句子中提取和学习连续符号模型。这有助于为识别过程构建培训数据。**//免责声明：/由于口译员不是相关领域的专家，在没有全面准备的情况下进行同声翻译，因此口译可能存在错误或遗漏。**
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| 课程简介: |
Distinguishing true signs from transitional, extraneous movements made by the signer as s/he moves from one sign to the next is a serious hurdle in the design of continuous Sign Language recognition systems. This problem is further compounded by the ambiguity of segmentation and occlusions, resulting in propagation of errors to higher levels. This talk will describe our experience with representations and matching methods, particularly those that can handle errors in low-level segmentation methods and uncertainties in segmentation of signs in sentences. We have formulated a novel framework that can address both these problems (i) using a nested level-building-based dynamic programming approach, when there is dearth of training data, and (ii) using a HMM-based approach generalized to handle multiple possible observations, when we have statistical models of signs. We will also discuss an automated approach to both extract and learn models for continuous signs from continuous sentences in a weakly unsupervised manner. This can help build training data for the recognition process.*//Disclaimer:// There may be mistakes or omissions in the interpretation as the interpreters are not experts in the field of interest and performed a simultaneous translation without comprehensive preparation.**
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| 关 键 词: |
真实符号; 手语识别系统; 动态规划
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| 课程来源: |
视频讲座网
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| 最后编审: |
2021-12-23：liyy
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| 阅读次数: |
37
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