18,740 research outputs found
Automatic Face Recognition System Based on Local Fourier-Bessel Features
We present an automatic face verification system inspired by known properties
of biological systems. In the proposed algorithm the whole image is converted
from the spatial to polar frequency domain by a Fourier-Bessel Transform (FBT).
Using the whole image is compared to the case where only face image regions
(local analysis) are considered. The resulting representations are embedded in
a dissimilarity space, where each image is represented by its distance to all
the other images, and a Pseudo-Fisher discriminator is built. Verification test
results on the FERET database showed that the local-based algorithm outperforms
the global-FBT version. The local-FBT algorithm performed as state-of-the-art
methods under different testing conditions, indicating that the proposed system
is highly robust for expression, age, and illumination variations. We also
evaluated the performance of the proposed system under strong occlusion
conditions and found that it is highly robust for up to 50% of face occlusion.
Finally, we automated completely the verification system by implementing face
and eye detection algorithms. Under this condition, the local approach was only
slightly superior to the global approach.Comment: 2005, Brazilian Symposium on Computer Graphics and Image Processing,
18 (SIBGRAPI
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Semantic Visual Localization
Robust visual localization under a wide range of viewing conditions is a
fundamental problem in computer vision. Handling the difficult cases of this
problem is not only very challenging but also of high practical relevance,
e.g., in the context of life-long localization for augmented reality or
autonomous robots. In this paper, we propose a novel approach based on a joint
3D geometric and semantic understanding of the world, enabling it to succeed
under conditions where previous approaches failed. Our method leverages a novel
generative model for descriptor learning, trained on semantic scene completion
as an auxiliary task. The resulting 3D descriptors are robust to missing
observations by encoding high-level 3D geometric and semantic information.
Experiments on several challenging large-scale localization datasets
demonstrate reliable localization under extreme viewpoint, illumination, and
geometry changes
A Taxonomy of Deep Convolutional Neural Nets for Computer Vision
Traditional architectures for solving computer vision problems and the degree
of success they enjoyed have been heavily reliant on hand-crafted features.
However, of late, deep learning techniques have offered a compelling
alternative -- that of automatically learning problem-specific features. With
this new paradigm, every problem in computer vision is now being re-examined
from a deep learning perspective. Therefore, it has become important to
understand what kind of deep networks are suitable for a given problem.
Although general surveys of this fast-moving paradigm (i.e. deep-networks)
exist, a survey specific to computer vision is missing. We specifically
consider one form of deep networks widely used in computer vision -
convolutional neural networks (CNNs). We start with "AlexNet" as our base CNN
and then examine the broad variations proposed over time to suit different
applications. We hope that our recipe-style survey will serve as a guide,
particularly for novice practitioners intending to use deep-learning techniques
for computer vision.Comment: Published in Frontiers in Robotics and AI (http://goo.gl/6691Bm
InLoc: Indoor Visual Localization with Dense Matching and View Synthesis
We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph
with respect to a large indoor 3D map. The contributions of this work are
three-fold. First, we develop a new large-scale visual localization method
targeted for indoor environments. The method proceeds along three steps: (i)
efficient retrieval of candidate poses that ensures scalability to large-scale
environments, (ii) pose estimation using dense matching rather than local
features to deal with textureless indoor scenes, and (iii) pose verification by
virtual view synthesis to cope with significant changes in viewpoint, scene
layout, and occluders. Second, we collect a new dataset with reference 6DoF
poses for large-scale indoor localization. Query photographs are captured by
mobile phones at a different time than the reference 3D map, thus presenting a
realistic indoor localization scenario. Third, we demonstrate that our method
significantly outperforms current state-of-the-art indoor localization
approaches on this new challenging data
- …