By Daniel Cremers, Ian Reid, Hideo Saito, Ming-Hsuan Yang
The five-volume set LNCS 9003--9007 constitutes the completely refereed post-conference court cases of the twelfth Asian convention on desktop imaginative and prescient, ACCV 2014, held in Singapore, Singapore, in November 2014.
The overall of 227 contributions provided in those volumes used to be rigorously reviewed and chosen from 814 submissions. The papers are prepared in topical sections on reputation; 3D imaginative and prescient; low-level imaginative and prescient and contours; segmentation; face and gesture, monitoring; stereo, physics, video and occasions; and poster periods 1-3.
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Additional info for Computer Vision -- ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part III
IEEE Trans. Med. Imaging 23, 501–509 (2004) 19. : The Design and Use of Steerable Filters. IEEE Trans. Pattern Anal. Mach. Intell. 13, 891–906 (1991) 20. : Automated Segmentation of Pulmonary Vascular Tree from 3D CT Images. Proc. SPIE Med. Imaging 5369, 107–116 (2004) 21. : Enhancement, Detection, and Visualization of 3D Volume Data. D. Thesis, Dept. EE, Linkoping University, SE-581 83 Linkoping, Sweden, Dissertations No. 824, May 2003 22. : A 3D thin nets extraction method for medical imaging.
In addition to the superior accuracy, our system is more eﬃcient than the MTJSR. In the testing stage of MTJSR, it solves an optimization problem to represent every frame of the probe video sequence as a sparse linear combination of video sequences of a gallery subject. The classiﬁcation is then based on the accumulated reconstruction error. Denote the number of gallery subjects as M , the number of frames of the probe video is N ; the number of matching times of MTJSR is generally N × M . Moreover, each matching needs to solve an optimization problem of a sparse representation which by itself is a complex computation.
A) Original image. (b) A ground truth image. (c) and (d) indicate easy and challenging vessel pixels on the ground truth image. (e) - (h) show the segmentation results of four methods. Pro-vesselness+CR achieves the best performance. 14 X. Yang et al. Fig. 8. Illsutration of segmentation results on a cerebral image. (a) Original image. (b) A ground truth image. (c) and (d) indicate easy and challenging vessel pixels on the ground truth image. (e) - (h) display the segmentation results of four methods.