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LIFT: Learned Invariant Feature Transform
This work introduces a novel Deep Network architecture that implements the full feature point handling pipeline, that is, detection, orientation estimation, and feature description, and shows how to learn to do all three in a unified manner while preserving end-to-end differentiability. Expand
Learning to Find Good Correspondences
A novel normalization technique, called Context Normalization, is introduced, which allows the network to process each data point separately while embedding global information in it, and also makes the network invariant to the order of the correspondences. Expand
TILDE: A Temporally Invariant Learned DEtector
We introduce a learning-based approach to detect repeatable keypoints under drastic imaging changes of weather and lighting conditions to which state-of-the-art keypoint detectors are surprisinglyExpand
LF-Net: Learning Local Features from Images
A novel deep architecture and a training strategy to learn a local feature pipeline from scratch, using collections of images without the need for human supervision, and shows that it can optimize the network in a two-branch setup by confining it to one branch, while preserving differentiability in the other. Expand
The Visual Object Tracking VOT2013 Challenge Results
The evaluation protocol of the VOT2013 challenge and the results of a comparison of 27 trackers on the benchmark dataset are presented, offering a more systematic comparison of the trackers. Expand
Detection of Moving Objects with Non-stationary Cameras in 5.8ms: Bringing Motion Detection to Your Mobile Device
To achieve real time capability with satisfying performance, the proposed method models the background through dual-mode single Gaussian model (SGM) with age and compensates the motion of the camera by mixing neighboring models. Expand
Learning to Assign Orientations to Feature Points
This work shows how to train a Convolutional Neural Network to assign a canonical orientation to feature points given an image patch centered on the feature point, and proposes a new type of activation function for Neural Networks that generalizes the popular ReLU, maxout, and PReLU activation functions. Expand
Image Matching Across Wide Baselines: From Paper to Practice
It is shown that with proper settings, classical solutions may still outperform the perceived state of the art, and the conducted experiments reveal unexpected properties of structure from motion pipelines that can help improve their performance, for both algorithmic and learned methods. Expand
ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning
This paper shows how to normalize the feature maps with weights that are estimated within the network, excluding outliers from this normalization, and uses this mechanism to leverage two types of attention: local and global – by combining them, the method is able to find the essential data points in high-dimensional space in order to solve a given task. Expand
Intelligent visual surveillance — A survey
The first part surveys image enhancement, moving object detection and tracking, and motion behavior understanding, and the second part reviews wide-area surveillance techniques based on the fusion of multiple visual sensors, camera calibration and cooperative camera systems. Expand