Hierarchical Fast Mean-Shift Segmentation in Depth Images

被引：0

作者：

Surkala, Milan ^{[1
]}

Fusek, Radovan ^{[1
]}

Holusa, Michael ^{[1
]}

Sojka, Eduard ^{[1
]}

机构：

[1] Tech Univ Ostrava, Dept Comp Sci, FEECS, 17 Listopadu 15, Ostrava 70833, Czech Republic

来源：

ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS, ACIVS 2016 | 2016年 / 10016卷

关键词：

Fast; Mean-shift; Tracking; Segmentation;

D O I：

10.1007/978-3-319-48680-2_39

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Head position and head pose detection systems are very popular in recent times, especially with the rise of depth cameras like Microsoft Kinect and Intel RealSense. The goal is to recognize and segment a head in depth data. The systems could also detect the direction in which the head is pointing and we use these data to improve the gaze direction detection system and provide useful information to allow detectors to work properly. We present the Hierarchical Fast Blurring Mean Shift algorithm that is able to extract the data from depth images in real-time from above mentioned cameras. We also present some modifications for an effective reduction of the mean-shift dataset during the computation that allow us to increase the precision of the method. We use a hierarchical approach to reduce the dataset during the computation process and to improve the speed.

引用

页码：441 / 452

页数：12

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