In Atlanta world, given a set of image lines, we aim to cluster them by the unknown-but-sought VPs whose number is unknown.
The proposed TIP-GNN focuses on the bilevel graph structure in temporal networks: besides the explicit interaction graph, a node's sequential interactions can also be constructed as a transition graph.
This solver generation method is also naturally applied to relative pose estimation from PCs, resulting in a new six-point method for multi-camera systems.
We propose three novel solvers for estimating the relative pose of a multi-camera system from affine correspondences (ACs).
In this paper we present four cases of minimal solutions for two-view relative pose estimation by exploiting the affine transformation between feature points and we demonstrate efficient solvers for these cases.
According to this, we propose three high-quality matching systems and a Coarse-to-Fine RANSAC estimator.
In this paper, we propose a certifiably globally optimal and efficient solver for the $N$-point problem.
An accurate similarity calculation is challenging since the mismatch between a query and a retrieval text may exist in the case of a mistyped query or an alias inquiry.
In this paper, we propose a sinogram inpainting network (SIN) to solve limited-angle CT reconstruction problem, which is a very challenging ill-posed issue and of great interest for several clinical applications.
Medical Physics Image and Video Processing
Typically, the region search methods project the score of a classifier into an image plane, and then search the region with the maximal score.
There are four main benefits of our approach: (1) Our approach accommodates non-linear additive kernels such as the popular $\chi^2$ and intersection kernel; (2) our approach is able to handle both regions in images and spatio-temporal regions in videos in a unified way; (3) the feature selection problem is convex, and both problems can be solved using a scalable reduced gradient method; (4) we point out strong connections with multiple kernel learning and multiple instance learning approaches.
Taking advantage of sampling of Fourier transform, FastMMD decreases the time complexity for MMD calculation from $O(N^2 d)$ to $O(L N d)$, where $N$ and $d$ are the size and dimension of the sample set, respectively.
In the second step, we estimate the transformation using a robust estimator called L 2 E. This is the main novelty of our approach and it enables us to deal with the noise and outliers which arise in the correspondence step.