Correlation Filters with Limited Boundaries

Correlation filters take advantage of specific properties in the Fourier domain allowing them to be estimated efficiently: O(NDlogD) in the frequency domain, versus O(D^3 + ND^2) spatially where D is signal length, and N is the number of signals. Recent extensions to correlation filters, such as MOSSE, have reignited interest of their use in the vision community due to their robustness and attractive computational properties. In this paper we demonstrate, however, that this computational efficiency comes at a cost. Specifically, we demonstrate that only 1/D proportion of shifted examples are unaffected by boundary effects which has a dramatic effect on detection/tracking performance. In this paper, we propose a novel approach to correlation filter estimation that: (i) takes advantage of inherent computational redundancies in the frequency domain, and (ii) dramatically reduces boundary effects. Impressive object tracking and detection results are presented in terms of both accuracy and computational efficiency.Comment: 8 pages, 6 figures, 2 table

MULTI-CHANNEL CORRELATION FILTERS WITH LIMITED BOUNDARIES: THEORY AND APPLICATIONS

Ph.DDOCTOR OF PHILOSOPH

Learning Background-Aware Correlation Filters for Visual Tracking

Correlation Filters (CFs) have recently demonstrated excellent performance in terms of rapidly tracking objects under challenging photometric and geometric variations. The strength of the approach comes from its ability to efficiently learn - "on the fly" - how the object is changing over time. A fundamental drawback to CFs, however, is that the background of the object is not be modelled over time which can result in suboptimal results. In this paper we propose a Background-Aware CF that can model how both the foreground and background of the object varies over time. Our approach, like conventional CFs, is extremely computationally efficient - and extensive experiments over multiple tracking benchmarks demonstrate the superior accuracy and real-time performance of our method compared to the state-of-the-art trackers including those based on a deep learning paradigm

The effect of redshift-space distortions on projected 2-pt clustering measurements

Although redshift-space distortions only affect inferred distances and not angles, they still distort the projected angular clustering of galaxy samples selected using redshift dependent quantities. From an Eulerian view-point, this effect is caused by the apparent movement of galaxies into or out of the sample. From a Lagrangian view-point, we find that projecting the redshift-space overdensity field over a finite radial distance does not remove all the anisotropic distortions. We investigate this effect, showing that it strongly boosts the amplitude of clustering for narrow samples and can also reduce the significance of baryonic features in the correlation function. We argue that the effect can be mitigated by binning in apparent galaxy pair-centre rather than galaxy position, and applying an upper limit to the radial galaxy separation. We demonstrate this approach, contrasting against standard top-hat binning in galaxy distance, using sub-samples taken from the Hubble Volume simulations. Using a simple model for the radial distribution expected for galaxies from a survey such as the Dark Energy Survey (DES), we show that this binning scheme will simplify analyses that will measure baryon acoustic oscillations within such galaxy samples. Comparing results from different binning schemes has the potential to provide measurements of the amplitude of the redshift-space distortions. Our analysis is relevant for other photometric redshift surveys, including those made by the Panoramic Survey Telescope & Rapid Response System (Pan-Starrs) and the Large Synoptic Survey Telescope (LSST).Comment: 13 pages, 15 figures, accepted by MNRAS, corrected typos, revised argument in section 3, figure added in section 3, results unchange

EMPATH: A Neural Network that Categorizes Facial Expressions

There are two competing theories of facial expression recognition. Some researchers have suggested that it is an example of "categorical perception." In this view, expression categories are considered to be discrete entities with sharp boundaries, and discrimination of nearby pairs of expressive faces is enhanced near those boundaries. Other researchers, however, suggest that facial expression perception is more graded and that facial expressions are best thought of as points in a continuous, low-dimensional space, where, for instance, "surprise" expressions lie between "happiness" and "fear" expressions due to their perceptual similarity. In this article, we show that a simple yet biologically plausible neural network model, trained to classify facial expressions into six basic emotions, predicts data used to support both of these theories. Without any parameter tuning, the model matches a variety of psychological data on categorization, similarity, reaction times, discrimination, and recognition difficulty, both qualitatively and quantitatively. We thus explain many of the seemingly complex psychological phenomena related to facial expression perception as natural consequences of the tasks' implementations in the brain

Remove Cosine Window from Correlation Filter-based Visual Trackers: When and How

Correlation filters (CFs) have been continuously advancing the state-of-the-art tracking performance and have been extensively studied in the recent few years. Most of the existing CF trackers adopt a cosine window to spatially reweight base image to alleviate boundary discontinuity. However, cosine window emphasizes more on the central region of base image and has the risk of contaminating negative training samples during model learning. On the other hand, spatial regularization deployed in many recent CF trackers plays a similar role as cosine window by enforcing spatial penalty on CF coefficients. Therefore, we in this paper investigate the feasibility to remove cosine window from CF trackers with spatial regularization. When simply removing cosine window, CF with spatial regularization still suffers from small degree of boundary discontinuity. To tackle this issue, binary and Gaussian shaped mask functions are further introduced for eliminating boundary discontinuity while reweighting the estimation error of each training sample, and can be incorporated with multiple CF trackers with spatial regularization. In comparison to the counterparts with cosine window, our methods are effective in handling boundary discontinuity and sample contamination, thereby benefiting tracking performance. Extensive experiments on three benchmarks show that our methods perform favorably against the state-of-the-art trackers using either handcrafted or deep CNN features. The code is publicly available at https://github.com/lifeng9472/Removing_cosine_window_from_CF_trackers.Comment: 13 pages, 7 figures, submitted to IEEE Transactions on Image Processin

The AKARI 2.5-5 Micron Spectra of Luminous Infrared Galaxies in the Local Universe

We present AKARI 2.5-5um spectra of 145 local luminous infrared galaxies in the Great Observatories All-sky LIRG Survey. In all of the spectra, we measure the line fluxes and EQWs of the polycyclic aromatic hydrocarbon (PAH) at 3.3um and the hydrogen recombination line Br-alpha, with apertures matched to the slit sizes of the Spitzer spectrograph and with an aperture covering ~95% of the total flux in the AKARI 2D spectra. The star formation rates (SFRs) derived from Br-alpha measured in the latter aperture agree well with SFRs(LIR), when the dust extinction correction is adopted based on the 9.7um absorption feature. Together with the Spitzer spectra, we are able to compare the 3.3 and 6.2um PAH features, the two most commonly used near/mid-IR indicators of starburst (SB) or active galactic nucleus (AGN) dominated galaxies. We find that the 3.3 and 6.2um PAH EQWs do not follow a linear correlation and at least 1/3 of galaxies classified as AGN-dominated using 3.3um PAH are classified as starbursts based on 6.2um PAH. These galaxies have a bluer continuum slope than galaxies that are indicated to be SB-dominated by both PAH features. The bluer continuum emission suggests that their continuum is dominated by stellar emission rather than hot dust. We also find that the median Spitzer spectra of these sources are remarkably similar to the pure SB-dominated sources indicated by high PAH EQWs in both 3.3 and 6.2um. We propose a revised SB/AGN diagnostic diagram using 2-5um data. We also use the AKARI and Spitzer spectra to examine the performance of our new diagnostics and to estimate 3.3um PAH fluxes using the JWST photometric bands in 0<z<5. Of the known PAH features and mid-IR high ionization emission lines used as SB/AGN indicators, only the 3.3um PAH feature is observable with JWST at z>3.5, because the rest of the features at longer wavelengths fall outside the JWST wavelength coverage.Comment: 13 pages (without appendices), 12 figures, Accepted for publication in A&