Publications
Object detection by two-dimensional linear prediction
Summary
Summary
An important component of any automated image analysis system is the detection and classification of objects. In this report, we consider the first of these problems where the specific goal is to detect anomalous areas (e.g., man-made objects) in textured backgrounds such as trees, grass, and fields of aerial photographs...
Implementation of 2-D digital filters by iterative methods
Summary
Summary
A two-dimensional (2-D) rational filter can be implemented by an iterative computation involving only finite-extent impulse response (FIR) filtering operations, provided a certain convergence criterion is met. In this paper, we generalize this procedure so that the convergence criterion is satisfied for any stable 2-D rational transfer function. One formulation...
Signal reconstruction from the short-time Fourier transform magnitude
Summary
Summary
In this paper, a signal is shown to be uniquely represented by the magnitude of its short-time Fourier transform (STFT) under mild restrictions on the signal and the analysis window of the STFT. Furthermore, various algorithms are developed which reconstruct signal from appropriate samples of the STFT magnitude. Several of...
Iterative techniques for minimum phase signal reconstruction from phase or magnitude
Summary
Summary
In this paper, we develop iterative algorithms for reconstructing a minimum phase sequence from pthhea se or magnitude of its Fourier transform. These iterative solutions involve repeatedly imposing a causality constraint in the time domain and incorporating the known phase or magnitude function in the frequency domain. This approach is...
Recursive two-dimensional signal reconstruction from linear system input and output magnitudes
Summary
Summary
A recursive algorithm is presented for reconstructing a two-dimensional complex signal from its magnitude and the magnitude of the output of a known linear shift-invariant system whose input is the desired signal. The recursion has a simple geometric interpretation, and is easily extended to causal, shift-varying systems.
Convergence of iterative nonexpansive signal reconstruction algorithms
Summary
Summary
Iterative algorithms for signal reconstruction from partial time- and frequency-domain knowledge have proven useful in a number of application areas. In this paper, a general convergence proof, applicable to a general class of such iterative reconstruction algorithms, is presented. The proof relies on the concept of a nonexpansive mapping in...