A Comparative Study between Spatial-Temporal Orthogonal Moments for Volumes Description

2012 Fourth International Conference on Intelligent Networking and Collaborative Systems Pub Date : 2012-09-19 DOI:10.1109/iNCoS.2012.40

Manel Boutaleb, I. Lassoued, E. Zagrouba

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Abstract

Object motion description in videos is one of the most active research topics in pattern recognition and computer vision. In this paper we study and compare Krawtchouk, Tchebytchev and Zernike spatial-temporal moments for volumes description. Indeed, reconstruction process of sequences volumes is elaborated to select the best moment descriptor for spatial-temporal volumes. Structural and temporal information of a video sequence can be captured by this moments. The first step of this method is to segment the video into volume space-time images. Then, all objects silhouettes will be extracted from these images. So this set will define the space-time form. The next step is to apply the orthogonal space-time moments on the resulting shape or just on the silhouette's defined patches around the interest detected points. This approach allows to define a descriptor for each video in the database. These descriptors will then rebuild the volumes of silhouettes with different orders to select the optimal for description process.

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体描述中时空正交矩的比较研究

视频中的物体运动描述是模式识别和计算机视觉领域最活跃的研究课题之一。本文研究并比较了Krawtchouk、Tchebytchev和Zernike三种时空矩对体积描述的影响。实际上，详细阐述了序列体的重建过程，以选择时空体的最佳矩描述子。这些瞬间可以捕捉到视频序列的结构和时间信息。该方法的第一步是将视频分割成体时空图像。然后，从这些图像中提取所有物体的轮廓。这个集合定义了时空形式。下一步是将正交时空矩应用于最终形状或仅应用于轮廓在感兴趣检测点周围定义的补丁上。这种方法允许为数据库中的每个视频定义一个描述符。然后，这些描述符将以不同的顺序重建轮廓的体积，以选择最优的描述过程。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2012 Fourth International Conference on Intelligent Networking and Collaborative Systems

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