Robust Face Recognition Using Sparse and Dense Hybrid Representation with Local CorrelationFace Recognition is one of the biometrics that can be used to uniquely identify an individual based on the matching performed against known faces. The real world face recognition is very challenging since the ...
Videos can be contaminated by noise even when captured by high-quality cameras.Because video data has both spatial and temporal redundancies,low rank factorization has been developed.Originally,most denoising methods relied on a single statistical distribution to model noise,such as Gaussian distribution...
Supports both sparse and semi-dense matching of local features; Compact descriptors (64D); Performance comparable to known deep local features such as SuperPoint while being significantly faster and more lightweight. Also, XFeat exhibits much better robustness to viewpoint and illumination changes than...
Accurate, Dense, and Robust Accurate, Dense, and Robust Multi-View Stereopsis (PMVS)1
It combines the advantages of the "v-disparity" approach and a quasi-dense matching algorithm. In this aim, road surface and vertical planes of the scene are first extracted using the sparse "v-disparity" approach. The knowledge of these global surfaces of the scene is then used to guide ...
robust and sparse fuzzy k-means clustering - ijcai [Paper] carben: composite adversarial robustness benchmark [Paper] robust medical image segmentation by adapting neural ... [Paper] measuring robustness in deep learning based compressive ... [Paper] conditional synthetic data generation for ro...
Learning Two-View Stereo Matching Summary: We propose a graph-based semi-supervised symmetric matching framework that performs dense matching between two uncalibrated wide-baseline images by exploiting the results of sparse matching as labeled data. Our method utilizes m... J Xiao,J Chen,DY Yeung,...
45 introduced a novel transformation estimation method using L2E estimator for building robust sparse and dense correspondences. Some feature descriptors, such as shape context, are utilized for establish rough correspondences in their work. Ma et al.46 considered point set registration as the ...
Along with the event in which crowd participates, its volume and density are also important in managing the crowd. Hence, characterizing the crowd as dense or sparse is an essential component of a crowd handling system. In this context, most of the existing methods try to estimate the ...
In this paper, these difficult problems are handled effectively by a robust model that outputs an accurate and dense reconstruction as the final result from an input of multiple images captured by a normal camera. First, the positions of the camera and sparse 3D points are estimated by a ...