60,105 research outputs found
Rotationally Invariant Image Representation for Viewing Direction Classification in Cryo-EM
We introduce a new rotationally invariant viewing angle classification method
for identifying, among a large number of Cryo-EM projection images, similar
views without prior knowledge of the molecule. Our rotationally invariant
features are based on the bispectrum. Each image is denoised and compressed
using steerable principal component analysis (PCA) such that rotating an image
is equivalent to phase shifting the expansion coefficients. Thus we are able to
extend the theory of bispectrum of 1D periodic signals to 2D images. The
randomized PCA algorithm is then used to efficiently reduce the dimensionality
of the bispectrum coefficients, enabling fast computation of the similarity
between any pair of images. The nearest neighbors provide an initial
classification of similar viewing angles. In this way, rotational alignment is
only performed for images with their nearest neighbors. The initial nearest
neighbor classification and alignment are further improved by a new
classification method called vector diffusion maps. Our pipeline for viewing
angle classification and alignment is experimentally shown to be faster and
more accurate than reference-free alignment with rotationally invariant K-means
clustering, MSA/MRA 2D classification, and their modern approximations
Rotation-invariant features for multi-oriented text detection in natural images.
Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes
High-resolution ab initio three-dimensional X-ray diffraction microscopy
Coherent X-ray diffraction microscopy is a method of imaging non-periodic
isolated objects at resolutions only limited, in principle, by the largest
scattering angles recorded. We demonstrate X-ray diffraction imaging with high
resolution in all three dimensions, as determined by a quantitative analysis of
the reconstructed volume images. These images are retrieved from the 3D
diffraction data using no a priori knowledge about the shape or composition of
the object, which has never before been demonstrated on a non-periodic object.
We also construct 2D images of thick objects with infinite depth of focus
(without loss of transverse spatial resolution). These methods can be used to
image biological and materials science samples at high resolution using X-ray
undulator radiation, and establishes the techniques to be used in
atomic-resolution ultrafast imaging at X-ray free-electron laser sources.Comment: 22 pages, 11 figures, submitte
Towards Analyzing Semantic Robustness of Deep Neural Networks
Despite the impressive performance of Deep Neural Networks (DNNs) on various
vision tasks, they still exhibit erroneous high sensitivity toward semantic
primitives (e.g. object pose). We propose a theoretically grounded analysis for
DNN robustness in the semantic space. We qualitatively analyze different DNNs'
semantic robustness by visualizing the DNN global behavior as semantic maps and
observe interesting behavior of some DNNs. Since generating these semantic maps
does not scale well with the dimensionality of the semantic space, we develop a
bottom-up approach to detect robust regions of DNNs. To achieve this, we
formalize the problem of finding robust semantic regions of the network as
optimizing integral bounds and we develop expressions for update directions of
the region bounds. We use our developed formulations to quantitatively evaluate
the semantic robustness of different popular network architectures. We show
through extensive experimentation that several networks, while trained on the
same dataset and enjoying comparable accuracy, do not necessarily perform
similarly in semantic robustness. For example, InceptionV3 is more accurate
despite being less semantically robust than ResNet50. We hope that this tool
will serve as a milestone towards understanding the semantic robustness of
DNNs.Comment: Presented at European conference on computer vision (ECCV 2020)
Workshop on Adversarial Robustness in the Real World (
https://eccv20-adv-workshop.github.io/ ) [best paper award]. The code is
available at https://github.com/ajhamdi/semantic-robustnes
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