By Shuntaro Yamazaki, Masaaki Mochimaru (auth.), Xiaoyi Jiang, Olga Regina Pereira Bellon, Dmitry Goldgof, Takeshi Oishi (eds.)
This publication constitutes the refereed court cases of the foreign Workshop on intensity picture research, held at the side of ICPR 2012 in Japan in November 2012. The sixteen revised complete papers awarded on the workshop have been conscientiously reviewed and chosen from 27 submissions and are complemented with three invited papers that have been additionally peer-reviewed. The papers are equipped in topical sections on acquisition and modeling of intensity facts, processing and research of intensity facts, functions, and ICPR contest.
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Extra resources for Advances in Depth Image Analysis and Applications: International Workshop, WDIA 2012, Tsukuba, Japan, November 11, 2012, Revised Selected and Invited Papers
3 Noise in Distance Measurements The noise in the distance measurements DD and DMP within the known static scene was evaluated by investigating the standard deviation. 7◦C during the complete experiment. The standard deviation was calculated from over 660 measurement points within 15 minutes for each thermal state. The evaluation of the cold state was performed after a cool-down time of 30 minutes using the mounted fan. The warm state was evaluated after 60 minutes warm-up time without fan activity.
In contrast to that, a positive correlation between temperature and DD (red) can be noticed. 4◦ C. 88 mm per 1◦ C. The latter compares well with an increase by 8 mm per 1◦ C as reported for the SR2 time-of-ﬂight camera . The table in Fig. 4(b) shows quantitative details. 1476 28 1472 26 1468 24 1464 0 Temp. 56 22 Kinect Depth Model Plane Temperature 1460 (a) Temperature sensor (left) attached at Kinect’s ventilation slot. 29 50 100 Time [min] 150 Temperature [°C] Distance [mm] Impact of Thermal and Environmental Conditions on the Kinect Sensor 20 200 (b) Plot of temperature and distances before and after internal fan activation.
Edu Abstract. Three-dimensional simultaneous localization and mapping is a topic of significant interest in the research community, particularly so since the introduction of cheap consumer RGB-D sensors such as the Microsoft Kinect. Current algorithms are able to create rich, visually appealing maps of indoor environments using such sensors. However, state-of-the-art systems are designed for use in static environments, which severely limits the application space for such systems. We present an algorithm to explicitly detect and remove moving objects from multiple views of a scene.
Advances in Depth Image Analysis and Applications: International Workshop, WDIA 2012, Tsukuba, Japan, November 11, 2012, Revised Selected and Invited Papers by Shuntaro Yamazaki, Masaaki Mochimaru (auth.), Xiaoyi Jiang, Olga Regina Pereira Bellon, Dmitry Goldgof, Takeshi Oishi (eds.)