Computer Vision – ECCV 2016: 14th European Conference, by Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling

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By Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling

The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed complaints of the 14th eu convention on desktop imaginative and prescient, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016.
The 415 revised papers awarded have been conscientiously reviewed and chosen from 1480 submissions. The papers hide all features of machine imaginative and prescient and development popularity similar to 3D desktop imaginative and prescient; computational images, sensing and exhibit; face and gesture; low-level imaginative and prescient and snapshot processing; movement and monitoring; optimization equipment; physics-based imaginative and prescient, photometry and shape-from-X; reputation: detection, categorization, indexing, matching; segmentation, grouping and form illustration; statistical tools and studying; video: occasions, actions and surveillance; functions. they're geared up in topical sections on detection, acceptance and retrieval; scene realizing; optimization; picture and video processing; studying; motion, task and monitoring; 3D; and nine poster sessions.

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Extra info for Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part V

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Zhang et al. [3] carefully designed the phase congruency and gradient magnitude as complementary features of FSIM for IQA. Different from FR-IQA methods, the RR-IQA methods [7,8] focus on utilizing only parts of the reference image information to accomplish the assessment. In general, FR-IQA and RR-IQA mimic different sensitivity of HVS to different image signals [2,17], including spatial frequency, luminance [12], structural information [5] etc. to devise metric for IQA. As explained before, most FR or RR-IQA methods are extremely sensitive to small geometrical misalignment between the reference and the distorted images, which severely limits their applications.

G. to measure the quality of JPEG 2000 (JP2K) compression. Unfortunately, in more general scenario, the imaging process that generates the distorted image and the reference image may not produce aligned pair. For instance, a cell phone camera may capture a photo with hand-shake, and it is difficult to then capture an aligned high-quality image as reference. An image enhancement module on an automatic vehicle can improve a low-quality capture of the road but to assess the performance of the enhancement, it is impossible to put the vehicle at the same position to shot a pixel-aligned picture for reference.

IEEE Trans. Image Process. 21(12), 4695–4708 (2012) 12. : Image quality assessment and human visual system. In: Visual Communications and Image Processing 2010, International Society for Optics and Photonics, pp. 77440Z–77440Z (2010) 13. : Multiscale structural similarity for image quality assessment. In: Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, 2004, vol. 2, pp. 1398–1402. IEEE (2003) 14. : The attention system of the human brain. Technical report, DTIC Document (1989) 15.

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