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

This ebook is the first authorized digital version of Kernighan and Ritchie's 1988 classic, The C Programming Language (2nd Ed.). One of the best-selling programming books published in the last fifty years, "K&R" has been called everything from the "bible" to "a landmark in computer science" and it has influenced generations of programmers. Available now for all leading ebook platforms, this concise and beautifully written text is a "must-have" reference for every serious programmers digital library.

As modestly described by the authors in the Preface to the First Edition, this "is not an introductory programming manual; it assumes some familiarity with basic programming concepts like variables, assignment statements, loops, and functions. Nonetheless, a novice programmer should be able to read along and pick up the language, although access to a more knowledgeable colleague will help."

The C programming language

Adaptive beamforming algorithms can be extremely sensitive to slight errors in array characteristics. Errors which are uncorrelated from sensor to sensor pass through the beamformer like uncorrelated or spatially white noise. Hence, gain against white noise is a measure of robustness. A new algorithm is presented which includes a quadratic inequality constraint on the array gain against uncorrelated noise, while minimizing output power subject to multiple linear equality constraints. It is shown that a simple scaling of the projection of tentative weights, in the subspace orthogonal to the linear constraints, can be used to satisfy the quadratic inequality constraint. Moreover, this scaling is equivalent to a projection onto the quadratic constraint boundary so that the usual favorable properties of projection algorithms apply. This leads to a simple, effective, robust adaptive beamforming algorithm in which all constraints are satisfied exactly at each step and roundoff errors do not accumulate. The algorithm is then extended to the case of a more general quadratic constraint.

Robust adaptive beamforming

A constant false alarm rate (CFAR) detection algorithm (see J.Y. Chen and I.S. Reed, IEEE Trans. Aerosp. Electron. Syst., vol.AES-23, no.1, Jan. 1987) is generalized to a test which is able to detect the presence of known optical signal pattern which has nonnegligible unknown relative intensities in several signal-plus-noise bands or channels. This test and its statistics are analytically evaluated, and the signal-to-noise ratio (SNR) performance improvement is analyzed. Both theoretical and computer simulation results show that the SNR improvement factor of this algorithm using multiple band scenes over the single scene of maximum SNR can be substantial. The SNR gain of this detection algorithm is compared to the previously published one. It illustrates that the generalized SNR of the test using the full data array is always greater than that of using partial data array. The database used to simulate this adaptive CFAR test is obtained from actual image scenes. >

Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution

Hyperspectral remote sensing technology has advanced significantly in the past two decades. Current sensors onboard airborne and spaceborne platforms cover large areas of the Earth surface with unprecedented spectral, spatial, and temporal resolutions. These characteristics enable a myriad of applications requiring fine identification of materials or estimation of physical parameters. Very often, these applications rely on sophisticated and complex data analysis methods. The sources of difficulties are, namely, the high dimensionality and size of the hyperspectral data, the spectral mixing (linear and nonlinear), and the degradation mechanisms associated to the measurement process such as noise and atmospheric effects. This paper presents a tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing. In all topics, we describe the state-of-the-art, provide illustrative examples, and point to future challenges and research directions.

/pdf/hyperspectral-remote-sensing-data-analysis-and-future-1cxk3zgg98.pdf

Hyperspectral Remote Sensing Data Analysis and Future Challenges

A new type of perfect reconstruction filter bank is presented in this paper. This new class of filter bank has the following distict features at the same time: linear-phase, distortion-free, hR-FIR filter pair, closed-formexpression, orthogonal projection, and factorization-free. In contrast, the other existing filter banks do not possessall these properties simutaneously. For example, the classic Quadrature Mirror Filter (QMF) does not have theperfect reconstruction nature. Another example is the Conjugate Quadrature Filter (CQF), which lacks the linear-phase property. A qualitative comparison of different types of filter banks is also included. This newly developed perfect reconstruction hR-FIR filter bank is then extended to the 2-D case. Both separable and non-separable filters are considered. In addition, both rectangular dowusampling and non-rectangular downsampling schemesare discussed. Numerical optimization procedures are used to compute the optimal filter coefficients. The resultsare listed in tables for quick reference.

New class of 2D multirate filter bank for digital image coding

As it is discussed in Chapter 1, power and bandwidth are limited resources in modern communication systems, and the efficient exploitation of these resources invariably involves an increase in complexity. It has become apparent over the past few decades that when there are strict limits on the power and bandwidth resources, the complexity of communication systems still can be increased to obtain efficiencies that are even closer to the theoretical limits.

Trellis-Coded Modulation

A new probabilistic interpretation of ψψ∗ using nonhomogeneous poisson statistics

Correction to 'Convolutions over Residue Classes of Quadratic Integers'

Convolutional codes have many applications in various communication systems. Two important applications in mobile communications are introduced in this chapter.

Irving S. Reed

Papers

New class of 2D multirate filter bank for digital image coding

Trellis-Coded Modulation

A new probabilistic interpretation of ψψ∗ using nonhomogeneous poisson statistics

Correction to 'Convolutions over Residue Classes of Quadratic Integers'

Applications of Convolutional Codes in Mobile Communications