Microsoft Technology Licensing, LLC

Other

Zepp Labs, Inc.

Jiangyu Liu

Zhengyou Zhang

Jian Qiao Sun

Seiichi Kato

Lu Yuan

Zheng Han

Ivan Stojiljkovic

Xiaowei Dai

Aleksandar Tomic

Yasufumi Shiraishi

Daniel J Moffitt

Kenji Nikaido

Daniel Eduardo Martinez

Kaiming He

Zeyu Liu

Aleksandar B Tomic

A solution is provided for detecting video highlights of a sports video. A video highlight of a sports video is a portion of the sports video and represents a semantically important event captured in the sports video. An audio stream associated with the sports video is evaluated, e.g., the loudness and length of the loudness of the portions of the audio stream. Video segments of the sports video are selected based on the evaluation of the audio stream. Each selected video segment represents a video highlight candidate of the sports video. A trained audio classification model is used to recognize the voice patterns in the audio stream associated with each selected video segment. Based on the comparison of the recognized video patterns with a set of desired voice patterns, one or more video segments are selected as the video highlights of the sports video.

Detect sports video highlights based on voice recognition

Image processing for productivity applications is provided. An image may be received by a computing device. The computing device may detect the edges comprising the received image and adjust the image based on a skew state of the detected edges. The computing device may then process the adjusted image to correct imbalances. The computing device may then assign an image classification to the processed image. The computing device may then adjust the processed image based on the assigned image classification.

Image processing for productivity applications

A system is provided for real-time single-view action recognition for sports videos based on key pose analysis of the sports videos. A training module of the system trains feature models for a sports action distinctively associated with each sports type using a large corpus of training videos. The trained feature models include a player detector for detecting locations of a player in video frames of a training video, a set of key pose identifiers for identifying distinctive poses of a sports action associated with a type of sports, and a meta classifier for determining a likelihood that the sports action has happened in a sports video based on the key poses analysis. Responsive to an input sports video being received for real-time action recognition, a set of trained feature models associated with the sports type of the input video are selected and applied to the input video.

Real-time single-view action recognition based on key pose analysis for sports videos

Tools disclosed herein comprise progressive, paint stroke based region recognition and selection tools. Using these tools, a user may partially paint a region of interest directly on an image (by using a paint brush or other similar tool). Unlike conventional selection tools, a user is not required to paint the entire region pixel-by-pixel. Rather the desired region is automatically and intelligently recognized based on the partial selection. This is accomplished via a progressive selection algorithm. In addition, these tools provide the ability to quickly execute such region selections on multi-megapixel images.

Image selection techniques

A user interface enables interactive image matting to be performed on an image The user interface may provide results including an alpha matte as feedback in real time. The user interface may provide interactive tools for selecting a portion of the image, and an unknown region for alpha matte processing may be automatically generated adjacent to the selected region. The user may interactively refine the alpha matte as desired to obtain a satisfactory result.

Interactive image matting

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Beijing, China

Jiangyu Liu