Visual tracking via spatially aligned correlation filters network

Correlation filters based trackers rely on a periodic assumption of the search sample to efficiently distinguish the target from the background. This assumption however yields undesired boundary effects and restricts aspect ratios of search samples. To handle these issues, an end-to-end deep architecture is proposed to incorporate geometric transformations into a correlation filters based network. This architecture introduces a novel spatial alignment module, which provides continuous feedback for transforming the target from the border to the center with a normalized aspect ratio. It enables correlation filters to work on well-aligned samples for better tracking. The whole architecture not only learns a generic relationship between object geometric transformations and object appearances, but also learns robust representations coupled to correlation filters in case of various geometric transformations. This lightweight architecture permits real-time speed. Experiments show our tracker effectively handles boundary effects and aspect ratio variations, achieving state-of-the-art tracking results on recent benchmarks

Learning SO(3) Equivariant Representations with Spherical CNNs

We address the problem of 3D rotation equivariance in convolutional neural networks. 3D rotations have been a challenging nuisance in 3D classification tasks requiring higher capacity and extended data augmentation in order to tackle it. We model 3D data with multi-valued spherical functions and we propose a novel spherical convolutional network that implements exact convolutions on the sphere by realizing them in the spherical harmonic domain. Resulting filters have local symmetry and are localized by enforcing smooth spectra. We apply a novel pooling on the spectral domain and our operations are independent of the underlying spherical resolution throughout the network. We show that networks with much lower capacity and without requiring data augmentation can exhibit performance comparable to the state of the art in standard retrieval and classification benchmarks.Comment: Camera-ready. Accepted to ECCV'18 as oral presentatio

Convolutional neural network architecture for geometric matching

We address the problem of determining correspondences between two images in agreement with a geometric model such as an affine or thin-plate spline transformation, and estimating its parameters. The contributions of this work are three-fold. First, we propose a convolutional neural network architecture for geometric matching. The architecture is based on three main components that mimic the standard steps of feature extraction, matching and simultaneous inlier detection and model parameter estimation, while being trainable end-to-end. Second, we demonstrate that the network parameters can be trained from synthetically generated imagery without the need for manual annotation and that our matching layer significantly increases generalization capabilities to never seen before images. Finally, we show that the same model can perform both instance-level and category-level matching giving state-of-the-art results on the challenging Proposal Flow dataset.Comment: In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017

The dependence of intrinsic alignment of galaxies on wavelength using KiDS and GAMA

The outer regions of galaxies are more susceptible to the tidal interactions that lead to intrinsic alignments of galaxies. The resulting alignment signal may therefore depend on the passband if the colours of galaxies vary spatially. To quantify this, we measured the shapes of galaxies with spectroscopic redshifts from the GAMA survey using deep gri imaging data from the KiloDegree Survey. The performance of the moment-based shape measurement algorithm DEIMOS was assessed using dedicated image simulations, which showed that the ellipticities could be determined with an accuracy better than 1% in all bands. Additional tests for potential systematic errors did not reveal any issues. We measure a significant difference of the alignment signal between the g,r and i-band observations. This difference exceeds the amplitude of the linear alignment model on scales below 2 Mpc/h. Separating the sample into central/satellite and red/blue galaxies, we find that that the difference is dominated by red satellite galaxies.Comment: 16 pages, 13 figures, accepted, to appear in A&

HST and UKIRT imaging observations of z~1 6C radio galaxies - II. Galaxy morphologies and the alignment effect

(abridged) Powerful radio galaxies often display enhanced optical/UV emission regions, elongated and aligned with the radio jet axis. The aim of this series of papers is to separately investigate the effects of radio power and redshift on the alignment effect, together with other radio galaxy properties. In this second paper, we present a deeper analysis of the morphological properties of these systems, including both the host galaxies and their surrounding aligned emission. The host galaxies of our 6C subsample are well described as de Vaucouleurs ellipticals, with typical scale sizes of ~10kpc. This is comparable to the host galaxies of low-z radio sources of similar powers, and also the more powerful 3CR sources at the same redshift. The contribution of nuclear point source emission is also comparable, regardless of radio power. The 6C alignment effect is remarkably similar to that seen around more powerful 3CR sources at the same redshift in terms of extent and degree of alignment with the radio source axis, although it is generally less luminous. The bright, knotty features observed in the case of the z~1 3CR sources are far less frequent in our 6C subsample; neither do we observe such strong evidence for evolution in the strength of the alignment effect with radio source size/age. However, we do find a very strong link between the most extreme alignment effects and emission line region properties indicative of shocks, regardless of source size/age or power. In general, the 6C alignment effect is still considerably stronger than that seen around lower redshift galaxies of similar radio powers. (abridged)Comment: 23 pages, 15 figures, accepted for publication in MNRAS. See http://www.mrao.cam.ac.uk/~kji/MorphPaper/ for version of paper with full resolution images of Figs 1-1