MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

We present a nonparametric algorithm for finding localized energy solutions from limited data. The problem we address is underdetermined, and no prior knowledge of the shape of the region on which the solution is nonzero is assumed. Termed the FOcal Underdetermined System Solver (FOCUSS), the algorithm has two integral parts: a low-resolution initial estimate of the real signal and the iteration process that refines the initial estimate to the final localized energy solution. The iterations are based on weighted norm minimization of the dependent variable with the weights being a function of the preceding iterative solutions. The algorithm is presented as a general estimation tool usable across different applications. A detailed analysis laying the theoretical foundation for the algorithm is given and includes proofs of global and local convergence and a derivation of the rate of convergence. A view of the algorithm as a novel optimization method which combines desirable characteristics of both classical optimization and learning-based algorithms is provided. Mathematical results on conditions for uniqueness of sparse solutions are also given. Applications of the algorithm are illustrated on problems in direction-of-arrival (DOA) estimation and neuromagnetic imaging.

Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm

The problem of error control and concealment in video communication is becoming increasingly important because of the growing interest in video delivery over unreliable channels such as wireless networks and the Internet. This paper reviews the techniques that have been developed for error control and concealment. These techniques are described in three categories according to the roles that the encoder and decoder play in the underlying approaches. Forward error concealment includes methods that add redundancy at the source end to enhance error resilience of the coded bit streams. Error concealment by postprocessing refers to operations at the decoder to recover the damaged areas based on characteristics of image and video signals. Last, interactive error concealment covers techniques that are dependent on a dialogue between the source and destination. Both current research activities and practice in international standards are covered.

/pdf/error-control-and-concealment-for-video-communication-a-3tm7c48xjc.pdf

Error control and concealment for video communication: a review

Dynamics of structures

This paper presents an account of the current state of sampling, 50 years after Shannon's formulation of the sampling theorem. The emphasis is on regular sampling, where the grid is uniform. This topic has benefitted from a strong research revival during the past few years, thanks in part to the mathematical connections that were made with wavelet theory. To introduce the reader to the modern, Hilbert-space formulation, we reinterpret Shannon's sampling procedure as an orthogonal projection onto the subspace of band-limited functions. We then extend the standard sampling paradigm for a presentation of functions in the more general class of "shift-in-variant" function spaces, including splines and wavelets. Practically, this allows for simpler-and possibly more realistic-interpolation models, which can be used in conjunction with a much wider class of (anti-aliasing) prefilters that are not necessarily ideal low-pass. We summarize and discuss the results available for the determination of the approximation error and of the sampling rate when the input of the system is essentially arbitrary; e.g., nonbandlimited. We also review variations of sampling that can be understood from the same unifying perspective. These include wavelets, multiwavelets, Papoulis generalized sampling, finite elements, and frames. Irregular sampling and radial basis functions are briefly mentioned.

/pdf/sampling-50-years-after-shannon-1i3rgczukl.pdf

Sampling-50 years after Shannon

IEEE International Conference on Fuzzy Systems

An algorithm is developed to define, from the data samples themselves, a frequency-weighted norm to use in minimum-weighted-norm extrapolation. The iterative procedure developed consists of using a periodogram spectrum estimate obtained from some samples of the signal estimate/extrapolation found at one iteration to define the weight that is used to estimate at the next iteration. This algorithm usually converges in less than 10 iterations to an extrapolation which is characterized as a nonparametric frequency-stationary extension of the data. The frequency resolution and extrapolation length are controlled by the length of a time-domain window used to obtain smooth spectral estimates between iterations. Examples are provided to illustrate the use of the algorithm for interpolation/extrapolation. The examples give comparable results to nonadaptive extrapolation methods without the need for a priori knowledge. For a certain spectral estimation example, the algorithm provides comparable resolution to the parametric methods with more accurate values of the relative strengths of the narrowband components. >

Extrapolation and spectral estimation with iterative weighted norm modification

The problem of recovering a finite-length sequence from the Fourier transform magnitude and the exact value of the first time sample, which has been recently shown to be almost always possible, is shown to be NP-complete. It is shown that recovery to within a unit magnitude complex constant is possible if the magnitude of the end point is within an uncertainty interval. Larger uncertainty leads to the recovery of a set of feasible solutions that can be used to recover the sequence if additional information is available. A branch-and-bound algorithm to find all solutions which offers significant improvements over exhaustive search is presented. This algorithm can be modified to incorporate the knowledge of additional time samples or knowledge of the fact that the sequence is real to help make a unique recovery. >

On phase retrieval of finite-length sequences using the initial time sample

Heart Rate Variability (HRV) signal analysis provides a quantitative marker of the Autonomic Nervous System (ANS) function. A wristband-type wireless photoplethysmographic (PPG) device was custom-designed to collect and analyze the arterial pulse in the wrist. The proposed device is comprised of an optical sensor to monitor arterial pulse, a signal conditioning unit to filter and amplify the analog PPG signal, a microcontroller to digitize the analog PPG signal, and a Bluetooth module to transfer the data to a smart device. This paper proposes a novel model to represent the PPG signal as the summation of two Gaussian functions. The paper concludes with a verification procedure for HRV signal analysis during sedentary activities.

/pdf/design-and-prototyping-of-a-wristband-type-wireless-37cyfib85b.pdf

Design and prototyping of a wristband-type wireless photoplethysmographic device for heart rate variability signal analysis

In this paper, we present a detailed description of a non-parametric two-dimensional (2-D) procedure to extrapolate a signal, denoted Adaptive Weighted Norm Extrapolation (AWNE), and we propose its application for SAR image formation. The benefits of the AWNE procedure are shown when it is applied to the MSTAR targets database of images. Once the phase history is recovered, the AWNE method is applied to a subaperture or to the full set of frequency samples to extrapolate them to a larger aperture. Then, the Inverse DFT is applied to obtain the new complex SAR image. Use of the 2-D AWNE procedure proves to be superior to its one-dimensional version by reducing undesirable effects such as sidelobe interference, and variability in energy of the extrapolated data from row to row and from column to column. To assess the performance of AWNE in enhancing prominent scatterers, reducing speckle, and suppressing clutter, we compare the super-resolved images to the images formed with the traditional Fourier technique starting from the same frequency samples. Both images are also compared with images formed starting from less data to assess the quality of the extrapolation and to quantify the ability to recover from lost resolution. We quantify the performance with the help of a target mask produced by a CFAR detector using metrics such as peak location blob matching count and a mean minimum peak distance. Another focus of our experiments is the illustration of the potential advantages of going beyond the traditional limits of resolution by extrapolating the full aperture of phase history to a larger size. We quantify performance by visual comparison and by the use of a geometric constellation of prominent point scatterers of the targets extracted from the images.

Sergio D. Cabrera

Papers

IEEE International Conference on Fuzzy Systems

Extrapolation and spectral estimation with iterative weighted norm modification

On phase retrieval of finite-length sequences using the initial time sample

Design and prototyping of a wristband-type wireless photoplethysmographic device for heart rate variability signal analysis

SAR image formation using 2-D re-weighted minimum norm extrapolation