Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images

Iris centre localization in low-resolution visible images is a challenging problem in computer vision community due to noise, shadows, occlusions, pose variations, eye blinks, etc. This paper proposes an efficient method for determining iris centre in low-resolution images in the visible spectrum. Even low-cost consumer-grade webcams can be used for gaze tracking without any additional hardware. A two-stage algorithm is proposed for iris centre localization. The proposed method uses geometrical characteristics of the eye. In the first stage, a fast convolution based approach is used for obtaining the coarse location of iris centre (IC). The IC location is further refined in the second stage using boundary tracing and ellipse fitting. The algorithm has been evaluated in public databases like BioID, Gi4E and is found to outperform the state of the art methods.Comment: 12 pages, 10 figures, IET Computer Vision, 201

Scalable and Sustainable Deep Learning via Randomized Hashing

Current deep learning architectures are growing larger in order to learn from complex datasets. These architectures require giant matrix multiplication operations to train millions of parameters. Conversely, there is another growing trend to bring deep learning to low-power, embedded devices. The matrix operations, associated with both training and testing of deep networks, are very expensive from a computational and energy standpoint. We present a novel hashing based technique to drastically reduce the amount of computation needed to train and test deep networks. Our approach combines recent ideas from adaptive dropouts and randomized hashing for maximum inner product search to select the nodes with the highest activation efficiently. Our new algorithm for deep learning reduces the overall computational cost of forward and back-propagation by operating on significantly fewer (sparse) nodes. As a consequence, our algorithm uses only 5% of the total multiplications, while keeping on average within 1% of the accuracy of the original model. A unique property of the proposed hashing based back-propagation is that the updates are always sparse. Due to the sparse gradient updates, our algorithm is ideally suited for asynchronous and parallel training leading to near linear speedup with increasing number of cores. We demonstrate the scalability and sustainability (energy efficiency) of our proposed algorithm via rigorous experimental evaluations on several real datasets

Real-time human action recognition on an embedded, reconfigurable video processing architecture

Copyright @ 2008 Springer-Verlag.In recent years, automatic human motion recognition has been widely researched within the computer vision and image processing communities. Here we propose a real-time embedded vision solution for human motion recognition implemented on a ubiquitous device. There are three main contributions in this paper. Firstly, we have developed a fast human motion recognition system with simple motion features and a linear Support Vector Machine (SVM) classifier. The method has been tested on a large, public human action dataset and achieved competitive performance for the temporal template (eg. “motion history image”) class of approaches. Secondly, we have developed a reconfigurable, FPGA based video processing architecture. One advantage of this architecture is that the system processing performance can be reconfiured for a particular application, with the addition of new or replicated processing cores. Finally, we have successfully implemented a human motion recognition system on this reconfigurable architecture. With a small number of human actions (hand gestures), this stand-alone system is performing reliably, with an 80% average recognition rate using limited training data. This type of system has applications in security systems, man-machine communications and intelligent environments.DTI and Broadcom Ltd

FPGA implementation of real-time human motion recognition on a reconfigurable video processing architecture

In recent years, automatic human motion recognition has been widely researched within the computer vision and image processing communities. Here we propose a real-time embedded vision solution for human motion recognition implemented on a ubiquitous device. There are three main contributions in this paper. Firstly, we have developed a fast human motion recognition system with simple motion features and a linear Support Vector Machine(SVM) classifier. The method has been tested on a large, public human action dataset and achieved competitive performance for the temporal template (eg. ``motion history image") class of approaches. Secondly, we have developed a reconfigurable, FPGA based video processing architecture. One advantage of this architecture is that the system processing performance can be reconfigured for a particular application, with the addition of new or replicated processing cores. Finally, we have successfully implemented a human motion recognition system on this reconfigurable architecture. With a small number of human actions (hand gestures), this stand-alone system is performing reliably, with an 80% average recognition rate using limited training data. This type of system has applications in security systems, man-machine communications and intelligent environments

Unveiling the nature of M94's (NGC4736) outer region: a panchromatic perspective

We have conducted a deep multi-wavelength analysis (0.15-160 mum) to study the outer region of the nearby galaxy M94. We show that the non-optical data support the idea that the outskirts of this galaxy is not formed by a closed stellar ring (as traditionally claimed in the literature) but by a spiral arm structure. In this sense, M94 is a good example of a Type III (anti-truncated) disk galaxy having a very bright outer disk. The outer disk of this galaxy contains ~23% of the total stellar mass budget of the galaxy and contributes ~10% of the new stars created showing that this region of the galaxy is active. In fact, the specific star formation rate of the outer disk (~0.012 Gyr^{-1}) is a factor of ~2 larger (i.e. the star formation is more efficient per unit stellar mass) than in the inner disk. We have explored different scenarios to explain the enhanced star formation in the outer disk. We find that the inner disk (if considered as an oval distortion) can dynamically create a spiral arm structure in the outer disk which triggers the observed relatively high star formation rate as well as an inner ring similar to what is found in this galaxy.Comment: Accepted for publication in ApJ, 15 pages, 9 figure

Hot X-ray coronae around massive spiral galaxies: a unique probe of structure formation models

Luminous X-ray gas coronae in the dark matter halos of massive spiral galaxies are a fundamental prediction of structure formation models, yet only a few such coronae have been detected so far. In this paper, we study the hot X-ray coronae beyond the optical disks of two normal massive spirals, NGC1961 and NGC6753. Based on XMM-Newton X-ray observations, hot gaseous emission is detected to ~60 kpc - well beyond their optical radii. The hot gas has a best-fit temperature of kT~0.6 keV and an abundance of ~0.1 Solar, and exhibits a fairly uniform distribution, suggesting that the quasi-static gas resides in hydrostatic equilibrium in the potential well of the galaxies. The bolometric luminosity of the gas in the (0.05-0.15)r_200 region (r_200 is the virial radius) is ~6e40 erg/s for both galaxies. The baryon mass fractions of NGC1961 and NGC6753 are f_b~0.1, which fall short of the cosmic baryon fraction. The hot coronae around NGC1961 and NGC6753 offer an excellent basis to probe structure formation simulations. To this end, the observations are confronted with the moving mesh code Arepo and the smoothed particle hydrodynamics code Gadget. Although neither model gives a perfect description, the observed luminosities, gas masses, and abundances favor the Arepo code. Moreover, the shape and the normalization of the observed density profiles are better reproduced by Arepo within ~0.5r_200. However, neither model incorporates efficient feedback from supermassive black holes or supernovae, which could alter the simulated properties of the X-ray coronae. With the further advance of numerical models, the present observations will be essential in constraining the feedback effects in structure formation simulations.Comment: 19 pages, 13 figures, 6 tables, accepted for publication in Ap