By Yo-Sung Ho, Jitao Sang, Yong Man Ro, Junmo Kim, Fei Wu
The two-volume lawsuits LNCS 9314 and 9315, represent the lawsuits of the 16th Pacific-Rim convention on Multimedia, PCM 2015, held in Gwangju, South Korea, in September 2015.
The overall of 138 complete and 32 brief papers offered in those lawsuits was once rigorously reviewed and chosen from 224 submissions. The papers have been prepared in topical sections named: photograph and audio processing; multimedia content material research; multimedia functions and companies; video coding and processing; multimedia illustration studying; visible figuring out and popularity on giant information; coding and reconstruction of multimedia information with spatial-temporal info; 3D image/video processing and functions; video/image caliber review and processing; social media computing; human motion acceptance in social robotics and video surveillance; fresh advances in image/video processing; new media illustration and transmission applied sciences for rising UHD providers.
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Extra info for Advances in Multimedia Information Processing – PCM 2015: 16th Pacific-Rim Conference on Multimedia Gwangju, South Korea, September 16–18, 2015, Proceedings, Part I
Internal Generative Mechanism 9 Fig. 5. MR-T2 brain slices. Fig. 6. Segmentation results obtained by the proposed method with p = 4. The comparison of uniformity measure for the proposed method and other segmentation methods is depicted in Table 1. The best results have been marked in bold. From Table 1, it can be observed that, except the slice #042, the proposed algorithm obtains the highest value of the uniformity measure on the remaining seven brain slices, which justiﬁes its superiority to others.
In: BMVC, pp. 27–1 (2011) 28. : Advances in active appearance models. In: ICCV, pp. 137–142 (1999) 29. : Interactive facial feature localization. , Schmid, C. ) ECCV 2012, Part III. LNCS, vol. 7574, pp. 679–692. Springer, Heidelberg (2012) 30. : Interactive facial feature localization. In: ECCV, pp. kr Abstract. This paper proposes a deblur algorithm using IMU sensor and long/ short exposure-time image pair. First, we derive an initial blur kernel from gyro data of IMU sensor. Second, we reﬁne the blur kernel by applying Lucas-Kanade algorithm to long/short exposure-time image pair.
Fourth, the initial kernel is reﬁned by using a pair of long/short exposure-time images. Finally, an input blurred image is deblurred using the reﬁned blur kernel and non-uniform residual deconvolution. The proposed algorithm has the following contributions: First, accurate blur kernel estimation is possible because the blur kernel can be reﬁned by using near-true camera motion derived from IMU sensor embedded in the camera body. Second, probable arti‐ facts such as ringing are eﬀectively suppressed by employing residual deconvolution based on non-uniform blur kernel.