The proceedings contain 466 papers. The topics discussed include: shape, albedo, and illumination from a single image of an unknown object;robust photometric stereo using sparse regression;2.5D building modeling by di...
ISBN:
(纸本)9781467312264
The proceedings contain 466 papers. The topics discussed include: shape, albedo, and illumination from a single image of an unknown object;robust photometric stereo using sparse regression;2.5D building modeling by discovering global regularities;angular domain reconstruction of dynamic 3D fluid surfaces;example-based 3D object reconstruction from line drawings;robust stereo with flash and no-flash image pairs;progressive graph matching: making a move of graphs via probabilistic voting;affine-invariant, elastic shape analysis of planar contours;jigsaw puzzles with pieces of unknown orientation;accidental pinhole and pinspeck cameras: revealing the scene outside the picture;decomposing global light transport using time of flight imaging;from pixels to physics: probabilistic color de-rendering;the shape Boltzmann machine: a strong model of object shape;discrete texture traces: topological representation of geometric context;and cross-based local multipoint filtering.
This paper reviews state-of-the-art methods for UAV navigation in GNSS-denied environments, focusing on map-based spatial matching and vision-based techniques. It identifies trends and challenges in current appro...
详细信息
The proceedings contain 132 papers. The topics discussed include: groupwise morphometric analysis based on morphological appearance manifold;GPU-accelerated, gradient-free MI deformable registration for atlas-based MR...
ISBN:
(纸本)9781424439911
The proceedings contain 132 papers. The topics discussed include: groupwise morphometric analysis based on morphological appearance manifold;GPU-accelerated, gradient-free MI deformable registration for atlas-based MR brain image segmentation;3D stochastic completion fields for fiber tractography;robust estimation of stem cell lineages using local graph matching;tunable tensor voting improves grouping of membrane-bound macromolecules;distance guided selection of the best base classifier in an ensemble with application to cervigram image segmentation;deformable tree models for 2D and 3D branching structures extraction;accurate estimation of pulmonary nodule's growth rate in CT images with nonrigid registration and precise nodule detection and segmentation;3D segmentation of rodent brains using deformable models and variational methods;automatic symmetry-integrated brain injury detection in MRI sequences;bicycle chain shape models;and automatic estimation of left ventricular dysfunction from echocardiogram videos.
This work presents AFRIFASHION1600, an openly accessible contemporary African fashion image dataset containing 1600 samples labelled into 8 classes representing some African fashion styles. Each sample is coloured and...
详细信息
ISBN:
(纸本)9781665448994
This work presents AFRIFASHION1600, an openly accessible contemporary African fashion image dataset containing 1600 samples labelled into 8 classes representing some African fashion styles. Each sample is coloured and has an image size of 128 x 128. This is a niche dataset that aims to improve visibility, inclusion, and familiarity of African fashion in computervision ***1600 dataset is available here.
Self-attention is a corner stone for transformer models. However, our analysis shows that self-attention in vision transformer inference is extremely sparse. When applying a sparsity constraint, our experiments on ima...
详细信息
ISBN:
(纸本)9781665448994
Self-attention is a corner stone for transformer models. However, our analysis shows that self-attention in vision transformer inference is extremely sparse. When applying a sparsity constraint, our experiments on image (ImageNet-1K) and video (Kinetics-400) understanding show we can achieve 95% sparsity on the self-attention maps while maintaining the performance drop to be less than 2 points. This motivates us to rethink the role of self-attention in vision transformer models.
In the context of variational auto-encoders, learning disentangled latent variable representations remains a challenging problem. In this abstract, we consider the semi-supervised setting, in which the factors of vari...
详细信息
ISBN:
(纸本)9781665448994
In the context of variational auto-encoders, learning disentangled latent variable representations remains a challenging problem. In this abstract, we consider the semi-supervised setting, in which the factors of variation are labelled for a small fraction of our samples. We examine how the quality of learned representations is affected by the dimension of the unsupervised component of the latent space. We also consider a variational lower bound for the mutual information between the data and the semi-supervised component of the latent space, and analyze its role in the context of disentangled representation learning.
We propose an iterative method for estimating rigid transformations from point sets using adiabatic quantum computation. Compared to existing quantum approaches, our method relies on an adaptive scheme to solve the pr...
详细信息
ISBN:
(纸本)9781665469463
We propose an iterative method for estimating rigid transformations from point sets using adiabatic quantum computation. Compared to existing quantum approaches, our method relies on an adaptive scheme to solve the problem to high precision, and does not suffer from inconsistent rotation matrices. Experimentally, our method performs robustly on several 2D and 3D datasets even with high outlier ratio.
The aim of this paper is to demonstrate that a state of the art feature matcher (LoFTR) can be made more robust to rotations by simply replacing the backbone CNN with a steerable CNN which is equivariant to translatio...
详细信息
ISBN:
(纸本)9781665487399
The aim of this paper is to demonstrate that a state of the art feature matcher (LoFTR) can be made more robust to rotations by simply replacing the backbone CNN with a steerable CNN which is equivariant to translations and image rotations. It is experimentally shown that this boost is obtained without reducing performance on ordinary illumination and viewpoint matching sequences.
暂无评论