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High-orders motion analysis = computer vision methods /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	High-orders motion analysis/ by Yan Sun.
其他題名:	computer vision methods /
作者:	Sun, Yan.
出版者:	Singapore :Springer Nature Singapore : : 2024.,
面頁冊數:	ix, 85 p. :ill. (some col.), digital ;24 cm.
內容註:	Overview of this Book -- Describing Motion in Computer Images Stream: Optical Flow -- Analyzing Acceleration in Computer Images Stream -- Jerk and Higher Order Motion in Computer Image Streams -- Detecting Heel Strikes for Gait Analysis through Higher-order motion Flow -- More Potential Applications via Higher-order Motion.
Contained By:	Springer Nature eBook
標題:	Computer vision. -
電子資源:	https://doi.org/10.1007/978-981-99-9191-4
ISBN:	9789819991914

High-orders motion analysis = computer vision methods /
Sun, Yan.

High-orders motion analysiscomputer vision methods /[electronic resource] :by Yan Sun. - Singapore :Springer Nature Singapore :2024. - ix, 85 p. :ill. (some col.), digital ;24 cm.

Overview of this Book -- Describing Motion in Computer Images Stream: Optical Flow -- Analyzing Acceleration in Computer Images Stream -- Jerk and Higher Order Motion in Computer Image Streams -- Detecting Heel Strikes for Gait Analysis through Higher-order motion Flow -- More Potential Applications via Higher-order Motion.

This book shows how different types of motion can be disambiguated into their components in a richer way than that currently possible in computer vision. Previous research of motion analysis has generally not yet considered the basic nature of higher orders of motion such as acceleration. Hence, this book introduces an approximation of the acceleration field using established optical flow techniques. Further, acceleration is decomposed into radial and tangential based on geometry and propagated as a general motion descriptor; this book shows the capability for differentiating different types of motion both on synthesized data and real image sequences. Beyond acceleration, the higher orders of motion flow and their continuant parts are investigated for further revealing the chaotic motion fields. Naturally, it is possible to extend this notion further: to detect higher orders of image motion. In this respect, this book shows how jerk and snap can be obtained from image sequences. The derived results on test images and heel strike detection in gait analysis illustrate the ability of higher-order motion, which provide the basis for the following research and applications in the future. We hope that the publication of this book will bring a new perspective to researchers and graduate students in the field of video analysis in computer vision.

ISBN: 9789819991914

Standard No.: 10.1007/978-981-99-9191-4doiSubjects--Topical Terms:

540671
Computer vision.

LC Class. No.: TA1634

Dewey Class. No.: 006.37

High-orders motion analysis = computer vision methods /
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520 $a This book shows how different types of motion can be disambiguated into their components in a richer way than that currently possible in computer vision. Previous research of motion analysis has generally not yet considered the basic nature of higher orders of motion such as acceleration. Hence, this book introduces an approximation of the acceleration field using established optical flow techniques. Further, acceleration is decomposed into radial and tangential based on geometry and propagated as a general motion descriptor; this book shows the capability for differentiating different types of motion both on synthesized data and real image sequences. Beyond acceleration, the higher orders of motion flow and their continuant parts are investigated for further revealing the chaotic motion fields. Naturally, it is possible to extend this notion further: to detect higher orders of image motion. In this respect, this book shows how jerk and snap can be obtained from image sequences. The derived results on test images and heel strike detection in gait analysis illustrate the ability of higher-order motion, which provide the basis for the following research and applications in the future. We hope that the publication of this book will bring a new perspective to researchers and graduate students in the field of video analysis in computer vision.
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