公开(公告)号：US09110501B2

公开(公告)日：2015-08-18

申请号：US13800486

申请日：2013-03-13

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Sudha Velusamy ,  Viswanath Gopalakrishnan ,  Bilva Bhalachandra Navathe ,  Anshul Sharma

IPC: G06K9/00 ,  G06F3/01 ,  G06K9/46

CPC classification number: G06F3/011 ,  G06K9/00335 ,  G06K9/4642 ,  G06K2009/4666

Abstract: A method and system for detecting temporal segments of talking faces in a video sequence using visual cues. The system detects talking segments by classifying talking and non-talking segments in a sequence of image frames using visual cues. The present disclosure detects temporal segments of talking faces in video sequences by first localizing face, eyes, and hence, a mouth region. Then, the localized mouth regions across the video frames are encoded in terms of integrated gradient histogram (IGH) of visual features and quantified using evaluated entropy of the IGH. The time series data of entropy values from each frame is further clustered using online temporal segmentation (K-Means clustering) algorithm to distinguish talking mouth patterns from other mouth movements. Such segmented time series data is then used to enhance the emotion recognition system.

Abstract translation: 一种使用视觉提示来检测视频序列中的谈话面部的时间段的方法和系统。 该系统通过使用视觉线索对一系列图像帧中的说话和非说话段进行分类来检测通话段。 本公开通过首先定位脸部，眼睛以及因此嘴部区域来检测视频序列中的说话面部的时间片段。 然后，通过视觉特征的积分梯度直方图（IGH）对视频帧上的局部化口腔区进行编码，并使用IGH的评估熵进行量化。 来自每个帧的熵值的时间序列数据使用在线时间分割（K-Means clustering）算法进一步聚类，以区分说话嘴模式与其他口部动作。 然后使用这种分段时间序列数据来增强情绪识别系统。

公开(公告)号：US11726681B2

公开(公告)日：2023-08-15

申请号：US16724607

申请日：2019-12-23

Applicant: Samsung Electronics Co., Ltd.

Inventor： Arun George ,  Anshul Sharma ,  Rajesh Krishnan ,  Vishak G

IPC: G06F3/06

CPC classification number: G06F3/0631 ,  G06F3/0604 ,  G06F3/0644 ,  G06F3/0647 ,  G06F3/0659 ,  G06F3/0679

Abstract: A method for converting an electronic flash storage device having a byte addressable storage (ByAS) and a block addressable flash storage (BlAS) to a single byte addressable storage includes receiving, by a host, a request for memory allocation from the ByAS, the receiving being from a first application among of a plurality of applications running on a processor; deallocating, by the host, a least relevant page allocated to at least one second application among the plurality of applications; moving, by the host, a content to the BlAS at a first BlAS location, the content related to the least relevant page, the moving based on the deallocation; allocating, by the host, the least relevant page to the first application; and updating, by the host, a cache metadata and a page lookup table of the first application and the at least one second application based on the deallocation and allocation.

公开(公告)号：US20130271361A1

公开(公告)日：2013-10-17

申请号：US13800486

申请日：2013-03-13

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Sudha Velusamy ,  Viswanath Gopalakrishnan ,  Bilva Bhalachandra Navathe ,  Anshul Sharma

IPC: G06F3/01

CPC classification number: G06F3/011 ,  G06K9/00335 ,  G06K9/4642 ,  G06K2009/4666

Abstract: A method and system for detecting temporal segments of talking faces in a video sequence using visual cues. The system detects talking segments by classifying talking and non-talking segments in a sequence of image frames using visual cues. The present disclosure detects temporal segments of talking faces in video sequences by first localizing face, eyes, and hence, a mouth region. Then, the localized mouth regions across the video frames are encoded in terms of integrated gradient histogram (IGH) of visual features and quantified using evaluated entropy of the IGH. The time series data of entropy values from each frame is further clustered using online temporal segmentation (K-Means clustering) algorithm to distinguish talking mouth patterns from other mouth movements. Such segmented time series data is then used to enhance the emotion recognition system.

Abstract translation: 一种使用视觉提示来检测视频序列中的谈话面部的时间段的方法和系统。 该系统通过使用视觉线索对一系列图像帧中的说话和非说话段进行分类来检测通话段。 本公开通过首先定位脸部，眼睛以及因此嘴部区域来检测视频序列中的说话面部的时间片段。 然后，通过视觉特征的积分梯度直方图（IGH）对视频帧上的局部化口腔区进行编码，并使用IGH的评估熵进行量化。 来自每个帧的熵值的时间序列数据使用在线时间分割（K-Means clustering）算法进一步聚类，以区分说话嘴模式与其他口部动作。 然后使用这种分段时间序列数据来增强情绪识别系统。