By Shuntaro Yamazaki, Masaaki Mochimaru (auth.), Xiaoyi Jiang, Olga Regina Pereira Bellon, Dmitry Goldgof, Takeshi Oishi (eds.)
This booklet constitutes the refereed complaints 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 provided on the workshop have been rigorously reviewed and chosen from 27 submissions and are complemented with three invited papers that have been additionally peer-reviewed. The papers are prepared in topical sections on acquisition and modeling of intensity info, processing and research of intensity info, functions, and ICPR contest.
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Extra info for Advances in Depth Image Analysis and Applications: International Workshop, WDIA 2012, Tsukuba, Japan, November 11, 2012, Revised Selected and Invited Papers
M¨ uller (a) RGB image. (b) Depth image. (c) Experimental setup in a static scene. Fig. 3. The region of interest (red box) within the RGB camera image (converted to grayscale) and the depth image are shown in (a) and (b). The experimental setup consists of the following items: Kinect (1), mounted fan (2), large planar checkerboard (3), ﬂuorescent lamp (4), thermometer (5), table fan (6). both cameras. Repeating this for both heat states, we got two sets of images for each camera. The IR projector was blocked to prevent detection errors of checkerboard corners by the structured light.
3. Experrimental setup for demonstrating view invariance Fig. 4. Example images captu ured from approx. 30 degree angles with depth map (red=clossest, blue = farthest) captured from Kinect synchronized with two webcams 48 M. Shreve et al. Fig. 5. Example strain maps calculated at two views roughly 45 degrees apart, for two subjects (each row). The first two pairss of columns are for the smile expression, the second pair of columns are for the surprise expreession. Due to the amount of no oise in the depth values provided by the Kinect, smoothhing was required as pre-processsing step.
A diﬀerent approach involves the use of alternative inputs such as depth sensors in order to ﬁt geometric models to the live data. A Kinect sensor is used in  for ﬁtting a user-speciﬁc expression model to the data (their goal is expression transfer however, and not face reconstruction). This can oﬀer satisfying results, however the approach is limited by the expressiveness of the model. The use of more camera viewpoints can also increase the conﬁdence of the results. Sparse stereo information is used in  for ﬁtting a generic 3D model to the live data.