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About 1,863 papers for “Multidimensional signal processing”
Bruce W. Suter, Meir Pachter · Wiley Encyclopedia of Electrical and Electronics Engineering · 1999
Abstract The sections in this article are Sampling Lattices Multidimensional Sampled Signals System Identification Problem System Identification Examples Conclusion
Cited by 8
Sankar Basu · 1988
Synthetic Aperture Radar (SAR) is an active remote sensing system which has applications in agriculture, ecology, geology, oceanography, hydrology, military, etc. [1]. SAR systems are mounted on an airplane or satellite which moves in a particular direction with a particular speed. The movement of the airplane or satellite is used to increase the aperture of the SAR
Cited by 8
2018
Cited by 6
M. Ekstrom, Wei Su, Chih-Ming Chen et al. · 2018
This chapter addresses the necessity as well as the feasibility of image and video data compression. Multidimensional signal processing tools apply to aperture and sensor array processing. Planar sensor arrays can be considered to be sampled apertures. In a broader context, beamforming can be used to separate signals received by sensor arrays based on frequency, wavenumber, and velocity of propagation. The counterpart in aperture and sensor array processing of the use of window functions in spectral analysis for reduction of sidelobes is the use of aperture shading. The combination of individual sensor outputs in a more sophisticated way than the delay-and-sum technique leads to the design of multichannel velocity filters for linear and planar as well as spatial arrays. Line arrays, planar arrays, and volumetric arrays could be either uniformly spaced or nonuniformly spaced, including the possibility of placing sensors randomly according to some probability distribution function.
Cited by 19
S. Dimolitsas · IEEE Transactions on Acoustics Speech and Signal Processing · 1985
Cited by 958
Er-hsien Fu · 2008
This paper conducts a literature survey of digital watermarks used for images. It describes the previous work done on digital watermarks, including the analysis of various watermarking schemes and their results. Potential applications are discussed, and an implementation plan of the project is presented. 1.
Cited by 0
Borga, Magnus · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 1998
The subject of this dissertation is to show how learning can be used for multidimensional signal processing, in particular computer vision. Learning is a wide concept, but it can generally be defined as a system’s change of behaviour in order to improve its performance in some sense. Learning systems can be divided into three classes: supervised learning, reinforcement learning and unsupervised learning. Supervised learning requires a set of training data with correct answers and can be seen as a kind of function approximation. A reinforcement learning system does not require a set of answers. It learns by maximizing a scalar feedback signal indicating the system’s performance. Unsupervised learning can be seen as a way of finding a good representation of the input signals according to a given criterion. In learning and signal processing, the choice of signal representation is a central issue. For high-dimensional signals, dimensionality reduction is often necessary. It is then important not to discard useful information. For this reason, learning methods based on maximizing mutual information are particularly interesting. A properly chosen data representation allows local linear models to be used in learning systems. Such models have the advantage of having a small number of parameters and can for this reason be estimated by using relatively few samples. An interesting method that can be used to estimate local linear models is canonical correlation analysis (CCA). CCA is strongly related to mutual information. The relation between CCA and three other linear methods is discussed. These methods are principal component analysis (PCA), partial least squares (PLS) and multivariate linear regression (MLR). An iterative method for CCA, PCA, PLS and MLR, in particular low-rank versions of these methods, is presented. A novel method for learning filters for multidimensional signal processing using CCA is presented. By showing the system signals in pairs, the filters can be adapted to detect certain features and to be invariant to others. A new method for local orientation estimation has been developed using this principle. This method is significantly less sensitive to noise than previously used methods. Finally, a novel stereo algorithm is presented. This algorithm uses CCA and phase analysis to detect the disparity in stereo images. The algorithm adapts filters in each local neighbourhood of the image in a way which maximizes the correlation between the filtered images. The adapted filters are then analysed to find the disparity. This is done by a simple phase analysis of the scalar product of the filters. The algorithm can even handle cases where the images have different scales. The algorithm can also handle depth discontinuities and give multiple depth estimates for semi-transparent images.
Cited by 33Open access
Wavelet analysis and its applications · 1998
Cited by 0
Iskender Agi · 1995
Multidimensional signal processing applications have a great deal of inherent parallelism. Two examples of these applications---MPEG encoding and computed tomography---can be implemented in parallel. With the described methodology these applications were converted into a form usable in GLU and implemented on a network of workstations. Improvements to the GLU programming environment can benefit other inherently parallel applications. 1. Introduction Multidimensional signal processing applications have a great deal of inherent parallelism. Many spatial signal processing applications are also noncausal and shiftinvariant. These applications use various mathematical transformations performed on well-defined data and have well-structured local and global communication. These properties of multidimensional signal processing applications allow most of these transformations to be calculated concurrently. We describe the parallel implementation of two multidimensional signal processing applic...
Cited by 0
Quming Zhou · 2003
Finding object contours in noisy images is a challenging task because of the amorphous nature of the object and the lack of sharp boundaries. Classical edge-based segmentation methods have the drawback of not connecting edge segments to form a distinct and meaningful boundary. Many level set approaches, which can deal with changes of topology and the presence of corners, have been developed to extract object boundaries. Previous researchers have used image gradient, edge strength, area minimization and region intensity to define the speed function. However, no paper mentions the edge/gradient direction. Our approach will incorporate direction and magnitude in the speed function. 1.
Cited by 0
1989
Cited by 0
Juan Carlos Reyes-Martínez1, Ana María Vargas-González2, Carlos Alberto Sánchez-Ramírez1* · Zenodo (CERN European Organization for Nuclear Research) · 2019
ABSTRACT: Multidimensional signals contain information of an object in more than one dimension, and usually their processing relies on complex methods in comparison with their unidimensional counterparts. In signal processing, finding a sparse representation of a signal is of great importance for compression purposes. Analytical multidimensional bases such as the Fourier, Cosine, or Wavelet Transform have been conventionally used. Recently, the use of learned dictionaries that directly adapt to the given signal are becoming popular in tasks such as image classification, image denoising, spectral unmixing, and medical image reconstruction. This paper presents an algorithm to learn transformation bases for the sparse representation of multidimensional signals. The proposed algorithm alternates between a sparse coding step solved by hard or soft thresholding strategies, and an updating dictionary step solved by a conjugate gradient method. Furthermore, the algorithm is tested using both: two-dimensional and three-dimensional patches, which are compared in terms of the sparsity performance for different types of multidimensional signals such as hyperspectral images, computerized axial tomography images and, magnetic resonance images. The attained results are compared against traditional analytical transforms and the state-of-the-art dictionary learning method: K-SVD RESUMEN: Las señales multidimensionales contienen información de un objeto en más de una dimensión y, comúnmente, su procesamiento requiere métodos de mayor complejidad que las señales unidimensionales. En procesamiento de señales, la representación escasa de una señal es de gran importancia para fines de compresión. Convencionalmente, transformaciones analíticas como las transformadas de Fourier, Coseno o Wavelet, han sido utilizadas. Recientemente, se ha popularizado el uso de diccionarios entrenados, que se adaptan a una señal dada, en aplicaciones como clasificación de imágenes, eliminación de ruido, separación espectral, y reconstrucción de imágenes médicas. Este artículo presenta un algoritmo para entrenar bases de transformación para representación escasa de señales multidimensionales. El algoritmo propuesto alterna entre una codificación escasa que se resuelve por umbralización, y la actualización del diccionario que se resuelve mediante el método de gradiente conjugado. Además, el artículo incluye una comparación entre parches bidimensionales y tridimensionales en términos del nivel de escasez que ofrecen en diferentes tipos de señales multidimensionales como: imágenes hiperespectrales, imágenes de tomografía computarizada, e imágenes de resonancia magnética. Los resultados obtenidos son comparados contra transformaciones analíticas tradicionales y contra el método de entrenamiento de diccionarios más conocido en el estado del arte: K-SVD.
Cited by 0Open access
Juan Carlos Reyes-Martínez1, Ana María Vargas-González2, Carlos Alberto Sánchez-Ramírez1* · Zenodo (CERN European Organization for Nuclear Research) · 2019
ABSTRACT: Multidimensional signals contain information of an object in more than one dimension, and usually their processing relies on complex methods in comparison with their unidimensional counterparts. In signal processing, finding a sparse representation of a signal is of great importance for compression purposes. Analytical multidimensional bases such as the Fourier, Cosine, or Wavelet Transform have been conventionally used. Recently, the use of learned dictionaries that directly adapt to the given signal are becoming popular in tasks such as image classification, image denoising, spectral unmixing, and medical image reconstruction. This paper presents an algorithm to learn transformation bases for the sparse representation of multidimensional signals. The proposed algorithm alternates between a sparse coding step solved by hard or soft thresholding strategies, and an updating dictionary step solved by a conjugate gradient method. Furthermore, the algorithm is tested using both: two-dimensional and three-dimensional patches, which are compared in terms of the sparsity performance for different types of multidimensional signals such as hyperspectral images, computerized axial tomography images and, magnetic resonance images. The attained results are compared against traditional analytical transforms and the state-of-the-art dictionary learning method: K-SVD RESUMEN: Las señales multidimensionales contienen información de un objeto en más de una dimensión y, comúnmente, su procesamiento requiere métodos de mayor complejidad que las señales unidimensionales. En procesamiento de señales, la representación escasa de una señal es de gran importancia para fines de compresión. Convencionalmente, transformaciones analíticas como las transformadas de Fourier, Coseno o Wavelet, han sido utilizadas. Recientemente, se ha popularizado el uso de diccionarios entrenados, que se adaptan a una señal dada, en aplicaciones como clasificación de imágenes, eliminación de ruido, separación espectral, y reconstrucción de imágenes médicas. Este artículo presenta un algoritmo para entrenar bases de transformación para representación escasa de señales multidimensionales. El algoritmo propuesto alterna entre una codificación escasa que se resuelve por umbralización, y la actualización del diccionario que se resuelve mediante el método de gradiente conjugado. Además, el artículo incluye una comparación entre parches bidimensionales y tridimensionales en términos del nivel de escasez que ofrecen en diferentes tipos de señales multidimensionales como: imágenes hiperespectrales, imágenes de tomografía computarizada, e imágenes de resonancia magnética. Los resultados obtenidos son comparados contra transformaciones analíticas tradicionales y contra el método de entrenamiento de diccionarios más conocido en el estado del arte: K-SVD.
Cited by 0Open access
Petros Maragos, Ronald W. Schafer · Proceedings of the IEEE · 1990
The basic theory and applications of a set-theoretic approach to image analysis called mathematical morphology are reviewed. The goals are to show how the concepts of mathematical morphology geometrical structure in signals to illuminate the ways that morphological systems can enrich the theory and applications of multidimensional signal processing. The topics covered include: applications to nonlinear filtering (morphological and rank-order filters, multiscale smoothing, morphological sampling, and morphological correlation); applications to image analysis (feature extraction, shape representation and description, size distributions, and fractals); and representation theorems, which shows how a large class of nonlinear and linear signal operators can be realized as a combination of simple morphological operations.
Cited by 298Open access
N.K. Bose · Multidimensional Systems and Signal Processing · 2009
Cited by 0Open access
Smart innovation, systems and technologies · 2024
Cited by 0
Roumen Kountchev, Rumen Mironov, Kazumi Nakamatsu · Smart innovation, systems and technologies · 2022
Cited by 0
Yu Zhongquan · 2002
This paper describes the method of multidimensional signal processing on ionitriding techniques. It focuses on the relations among the multidimensional parameters, the adjustment of these parameters, the adjusting rules, and the composition of the computer system.
Cited by 0
Jain, Lakhmi C., Mironov, Rumen P., Kountcheva, Roumiana A. et al. · Smart innovation, systems and technologies · 2025
Cited by 0Open access
Zhiping Lin, Raimund J. Ober, E.S. Ward · DR-NTU (Nanyang Technological University) · 2008
Although significant progress has been achieved in sequencing the human genome in recent years, the molecular basis for many diseases can only be understood if the three-dimensional (3D) structures of the proteins encoded by genes are available. Sequencing a gene is nowadays essentially a routine task, whereas the determination of a 3D structure of a protein is still a major task that will typically take years to complete. Currently, nuclear magnetic resonance (NMR) spectroscopy is still one of the two major methods for the determination of 3D protein structures as other methods are still not very accurate The processing and analysis of multidimensional (nD) NMR data is an important step in NMR spectroscopy. There are several methods for processing nD NMR data, but they are mainly based on the one-dimensional (1D) techniques. Moreover, there does not exist any rigorous measure for assessing how good the existing methods for estimating the NMR parameters. In this project, we analyze and process nD NMR data using nD system theory and signal processing techniques and global optimization methods. An important step in our investigation is to interprete nD NMR data as the impulse response of a separable nD state space system.
Cited by 0