Vasileios Argyriou

Robust FFT-Based Scale-Invariant Image Registration with Image Gradients

We present a robust FFT-based approach to scale-invariant image registration. Our method relies on FFT-based correlation twice: once in the log-polar Fourier domain to estimate the scaling and rotation and once in the spatial domain to recover the residual translation. Previous methods based on the same principles are not robust. To equip our scheme with robustness and accuracy, we introduce modifications which tailor the method to the nature of images. First, we derive efficient log-polar Fourier representations by replacing image functions with complex gray-level edge maps. We show that this representation both captures the structure of salient image features and circumvents problems related to the low-pass nature of images, interpolation errors, border effects, and aliasing. Second, to recover the unknown parameters, we introduce the normalized gradient correlation. We show that, using image gradients to perform correlation, the errors induced by outliers are mapped to a uniform distribution for which our normalized gradient correlation features robust performance. Exhaustive experimentation with real images showed that, unlike any other Fourier-based correlation techniques, the proposed method was able to estimate translations, arbitrary rotations, and scale factors up to 6.

G3D: A gaming action dataset and real time action recognition evaluation framework

In this paper a novel evaluation framework for measuring the performance of real-time action recognition methods is presented. The evaluation framework will extend the time-based event detection metric to model multiple distinct action classes. The proposed metric provides more accurate indications of the performance of action recognition algorithms for games and other similar applications since it takes into consideration restrictions related to time and consecutive repetitions. Furthermore, a new dataset, G3D for real-time action recognition in gaming containing synchronised video, depth and skeleton data is provided. Our results indicate the need of an advanced metric especially designed for games and other similar real-time applications.

A study of sub-pixel motion estimation using phase correlation

We propose a method for obtaining high-accuracy sub-pixel motion estimates using phase correlation. Our method is motivated by recently published analysis according to which the Fourier inverse of the normalized cross-power spectrum of pairs of images which have been mutually shifted by a fractional amount can be approximated by a two-dimensional sinc function. We use a modified version of such a function to obtain a sub-pixel estimate of motion by means of variable-separable fitting in the vicinity of the maximum peak of the phase correlation surface. We demonstrate that our method outperforms, in terms of sub-pixel accuracy, not only other surface fitting techniques but also the state-of-the-art in motion estimation using phase correlation including the technique that motivated our work in the first place. Furthermore our method performs particularly well in the presence of artificially induced additive white Gaussian noise and also offers better motion vector coherence in terms of zero-order entropy.

Recursive photometric stereo when multiple shadows and highlights are present

We present a recursive algorithm for 3D surface reconstruction based on photometric stereo in the presence of highlights, and self and cast shadows. We assume that the surface reflectance outside the highlights can be approximated by the Lambertian model. The algorithm works with as few as three light sources, and it can be generalised for N without any difficulties. Furthermore, this reconstruction method is able to identify areas where the majority of the lighting directions result in unreliable pixel intensities, providing the capability to adjust a reconstruction algorithm and improve its performance avoiding the unreliable sources. We report results for both artificial and real images and compare them with the results of other state of the art photometric stereo algorithms.

Face Recognition and Verification Using Photometric Stereo: The Photoface Database and a Comprehensive Evaluation

This paper presents a new database suitable for both 2-D and 3-D face recognition based on photometric stereo (PS): the Photoface database. The database was collected using a custom-made four-source PS device designed to enable data capture with minimal interaction necessary from the subjects. The device, which automatically detects the presence of a subject using ultrasound, was placed at the entrance to a busy workplace and captured 1839 sessions of face images with natural pose and expression. This meant that the acquired data is more realistic for everyday use than existing databases and is, therefore, an invaluable test bed for state-of-the-art recognition algorithms. The paper also presents experiments of various face recognition and verification algorithms using the albedo, surface normals, and recovered depth maps. Finally, we have conducted experiments in order to demonstrate how different methods in the pipeline of PS (i.e., normal field computation and depth map reconstruction) affect recognition and verification performance. These experiments help to 1) demonstrate the usefulness of PS, and our device in particular, for minimal-interaction face recognition, and 2) highlight the optimal reconstruction and recognition algorithms for use with natural-expression PS data. The database can be downloaded from http://www.uwe.ac.uk/research/Photoface.

Dynamic Feature Selection for Online Action Recognition

The ability to recognize human actions in real-time is fundamental in a wide range of applications from home entertainment to medical systems. Previous work on online action recognition has shown a tradeoff between accuracy and latency. In this paper we present a novel algorithm for online action recognition that combines the discriminative power of Random Forests for feature selection and a new dynamic variation of AdaBoost for online classification. The proposed method has been evaluated using datasets and performance metrics specifically designed for real time action recognition. Our results show that the presented algorithm is able to improve recognition rates at low latency.

Using gradient correlation for sub-pixel motion estimation of video sequences

A highly accurate and computationally efficient method is presented suitable for the estimation of motion in video sequences. The method is based on the maximization of the spatial gradient cross-correlation function, which is computed in the frequency domain and therefore can be implemented by fast transformation algorithms. We present enhancements to the baseline gradient-correlation algorithm, which further improve performance, especially in the presence of manually induced additive Gaussian noise. We also present a comparative performance analysis, which demonstrates that the proposed method outperforms the state-of-the-art in frequency-domain motion estimation, in the shape of phase correlation.

The Photoface database

In this paper we present a new database suitable for both 2D and 3D face recognition based on photometric stereo, the so-called Photoface database. The Photoface database was collected using a custom-made four-source photometric stereo device that could be easily deployed in commercial settings. Unlike other publicly available databases the level of cooperation between subjects and the capture mechanism was minimal. The proposed device may also be used, to capture 3D expressive faces. Apart from the description of the device and the Photoface database, we present experiments from baseline face recognition and verification algorithms using albedo, normals and the recovered depth maps. Finally, we have conducted experiments in order to demonstrate how different methods in the pipeline of photometric stereo (i.e. normal field computation and depth map reconstruction methods) affect recognition/verification performance.

On the estimation of subpixel motion using phase correlation

We propose a method for obtaining high-accuracy subpixel motion estimates using phase correlation. Our method is motivated by recently published analysis according to which the Fourier inverse of the normalized cross-power spectrum of pairs of images that have been mutually shifted by a fractional amount can be approximated by a two-dimensional sinc function. We propose a modified version of such a function to obtain a subpixel estimate of motion by means of variable-separable fitting in the vicinity of the maximum peak of the phase correlation surface. We demonstrate that our method outperforms, in terms of subpixel accuracy, not only other surface-fitting techniques but also the state-of-the-art in motion estimation using phase correlation including the technique that motivated our work in the first place. Furthermore, our method performs particularly well in the presence of artificially induced additive white Gaussian noise and also offers better motion vector coherence in terms of zero-order entropy.

Subpixel Registration With Gradient Correlation

We address the problem of subpixel registration of images assumed to be related by a pure translation. We present a method which extends gradient correlation to achieve subpixel accuracy. Our scheme is based on modeling the dominant singular vectors of the 2-D gradient correlation matrix with a generic kernel which we derive by studying the structure of gradient correlation assuming natural image statistics. Our kernel has a parametric form which offers flexibility in modeling the functions obtained from various types of image data. We estimate the kernel parameters, including the unknown subpixel shifts, using the Levenberg-Marquardt algorithm. Experiments with LANDSAT and MRI data show that our scheme outperforms recently proposed state-of-the-art phase correlation methods.