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Image and Video Technology: 7th Pacific-Rim Symposium, PSIVT by Thomas Bräunl, Brendan McCane, Mariano Rivera, Xinguo Yu

By Thomas Bräunl, Brendan McCane, Mariano Rivera, Xinguo Yu

This publication constitutes the completely refereed post-conference complaints of the seventh Pacific Rim Symposium on photo and Video expertise, PSIVT 2015, held in Auckland, New Zealand, in November 2015.

The overall of sixty one revised papers used to be rigorously reviewed and chosen from 133 submissions. The papers are equipped in topical sections on colour and movement, image/video coding and transmission, computational images and humanities, machine imaginative and prescient and purposes, photo segmentation and type, video surveillance, biomedical photo processing and research, item and development reputation, machine imaginative and prescient and development attractiveness, image/video processing and research, and development recognition.

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Extra resources for Image and Video Technology: 7th Pacific-Rim Symposium, PSIVT 2015, Auckland, New Zealand, November 25-27, 2015, Revised Selected Papers

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Mag. 18, 80–92 (2011) 16. : Incremental motion estimation through local bundle adjustment. jp Abstract. A robust scheme for identifying JPEG XR coded images is proposed in this paper. The aim is to identify the images that are generated from the same original image under various compression ratios. The proposed scheme is robust against a difference in compression ratios, and does not produce false negative matches in any compression ratio. A new property of the positive and negative signs of lapped biorthogonal transform coefficients is considered to robustly identify the images.

In our work we applied the Levenberg-Marquardt algorithm to solve the objective iteratively. In such a framework, the accuracy entirely relies on two factors - the tracking of features and the stereo matching algorithm, given that the system is well calibrated. e. the movements of the system at time slots j and j + 1 are considered to be independent events), the drift grows in a super-linear manner as the inter-frame ego-motion estimations are chained to derive the global trajectory of the system.

Multi-frame Feature Integration for Multi-camera Visual Odometry 31 The estimated motion can also be used to improve the tracking of features. The prediction of a feature’s location in the current frame is calculated by projecting its previously integrated 3D coordinates into the current frame. The projection is then compared to the image coordinates obtained by feature matching. The deviation between both is then used to denote the reliability of the measurement, and taken into account to adjust the weighting term ω accordingly.

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