Summary form only given. The volume conductor inverse problem is the problem of reconstructing a two-dimensional distributed voltage source from measurements of the electric field it produces at the surface of an intervening medium. The intervening medium may be homogeneous or horizontally layered, with differing conductivities in each layer. The problem is assumed to be quasistatic (a 'snapshot' in time); this is reasonable for the impedances encountered in biological tissues. The distributed source potential and the surface data are regarded as two-dimensional signals, and they are shown to be related by a linear two-dimensional filter. Implementation of the medium filter requires signal processing filtering techniques. This inverse problem is ill-conditioned for high-frequency signals and for large distances between source and measurements. The use of data-conditioning filters regularizes the inverse problem, with only minor effect on the reconstructed potential distribution. For typical signals under realistic signal-to-noise ratios, excellent numerical results have been obtained. In particular, a numerically stable recursive algorithm for computing the coefficients of the two-dimensional medium filter has been developed.<>
{"title":"A filtering approach to the two-dimensional volume conductor forward and inverse problems","authors":"T. G. Xydis, A. Yagle","doi":"10.1109/MDSP.1989.97099","DOIUrl":"https://doi.org/10.1109/MDSP.1989.97099","url":null,"abstract":"Summary form only given. The volume conductor inverse problem is the problem of reconstructing a two-dimensional distributed voltage source from measurements of the electric field it produces at the surface of an intervening medium. The intervening medium may be homogeneous or horizontally layered, with differing conductivities in each layer. The problem is assumed to be quasistatic (a 'snapshot' in time); this is reasonable for the impedances encountered in biological tissues. The distributed source potential and the surface data are regarded as two-dimensional signals, and they are shown to be related by a linear two-dimensional filter. Implementation of the medium filter requires signal processing filtering techniques. This inverse problem is ill-conditioned for high-frequency signals and for large distances between source and measurements. The use of data-conditioning filters regularizes the inverse problem, with only minor effect on the reconstructed potential distribution. For typical signals under realistic signal-to-noise ratios, excellent numerical results have been obtained. In particular, a numerically stable recursive algorithm for computing the coefficients of the two-dimensional medium filter has been developed.<<ETX>>","PeriodicalId":340681,"journal":{"name":"Sixth Multidimensional Signal Processing Workshop,","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125195239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summary form only given. An exact expression has been derived for the continuous ambiguity function of a time-limited waveform in terms of the discrete ambiguity function of the same waveform, and the resulting aliasing problem has been studied. The results have been used to solve the problem of least-squares synthesis of ambiguity functions for time-limited waveforms. The optimal waveform is specified by its Fourier transform samples taken at the Nyquist rate. Since the waveform is time limited, in general there are infinitely many nonzero frequency samples. Therefore, for practical purposes, all but a finite number of samples are specified to be zero. With this constraint, the design problem is solved by optimally choosing the nonzero Fourier-transform samples. This is accomplished by finding the eigenvector corresponding to the largest eigenvalue of a Hermitian matrix generated from the desired ambiguity function. The corresponding time waveform is then obtained by inverse transforming the optimal Fourier samples using the fast Fourier transform (FFT). Weighting of the Fourier samples prior to the FFT reduces the Gibbs' phenomenon.<>
{"title":"Time-limited waveform synthesis for range-Doppler radar","authors":"O. Arikan, D. Munson","doi":"10.1109/MDSP.1989.97024","DOIUrl":"https://doi.org/10.1109/MDSP.1989.97024","url":null,"abstract":"Summary form only given. An exact expression has been derived for the continuous ambiguity function of a time-limited waveform in terms of the discrete ambiguity function of the same waveform, and the resulting aliasing problem has been studied. The results have been used to solve the problem of least-squares synthesis of ambiguity functions for time-limited waveforms. The optimal waveform is specified by its Fourier transform samples taken at the Nyquist rate. Since the waveform is time limited, in general there are infinitely many nonzero frequency samples. Therefore, for practical purposes, all but a finite number of samples are specified to be zero. With this constraint, the design problem is solved by optimally choosing the nonzero Fourier-transform samples. This is accomplished by finding the eigenvector corresponding to the largest eigenvalue of a Hermitian matrix generated from the desired ambiguity function. The corresponding time waveform is then obtained by inverse transforming the optimal Fourier samples using the fast Fourier transform (FFT). Weighting of the Fourier samples prior to the FFT reduces the Gibbs' phenomenon.<<ETX>>","PeriodicalId":340681,"journal":{"name":"Sixth Multidimensional Signal Processing Workshop,","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1989-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126059392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summary form only given, as follows. The topic of multiprocessor computer architectures and parallel algorithms for computer vision is not new, but researchers are now addressing both a wider scope of issues and emphasizing system integration. Recently, a wide variety of new systems has been designed, built, and tested on a range of image analysis tasks. A critical question is how to achieve high performance in a complete, integrated set of component vision processes. A number of recent approaches to improving the performance of vision architectures are described. Comparisons are made relating the underlying model of parallel processing, the granularity of parallelism, and performance evaluation on various tasks covering several image representations and processing requirements.<>
{"title":"Parallel algorithms and architectures for image analysis and computer vision","authors":"C. R. Dyer","doi":"10.1109/MDSP.1989.96986","DOIUrl":"https://doi.org/10.1109/MDSP.1989.96986","url":null,"abstract":"Summary form only given, as follows. The topic of multiprocessor computer architectures and parallel algorithms for computer vision is not new, but researchers are now addressing both a wider scope of issues and emphasizing system integration. Recently, a wide variety of new systems has been designed, built, and tested on a range of image analysis tasks. A critical question is how to achieve high performance in a complete, integrated set of component vision processes. A number of recent approaches to improving the performance of vision architectures are described. Comparisons are made relating the underlying model of parallel processing, the granularity of parallelism, and performance evaluation on various tasks covering several image representations and processing requirements.<<ETX>>","PeriodicalId":340681,"journal":{"name":"Sixth Multidimensional Signal Processing Workshop,","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1989-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114477964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The performance of two related tasks, object detection and object amplitude estimation, is investigated. These tasks are related because the best amplitude estimate is the appropriate decision variable for the detection task. Different results have been observed for these two tasks as a function of lambda (a scalar which controls the strength of regularization) in a study restricted to images containing a mixture of high- and low-contrast nonoverlapping disks on a zero background. It has been found that in maximum a posteriori reconstructions the contrast of the low-contrast disks relative to the background decreases steadily as lambda increases. Thus the estimates for the amplitude of these disks deviate from their actual values. On the other hand, the detectability index does not change as quickly. The reason for this is that detectability is based on the separation of the estimate of the amplitude of the object relative to the estimate of the background value compared to their RMS deviations. The choice of lambda obviously becomes an important issue as it affects the bias in the estimated amplitude. It is postulated that the same behavior holds for many other types of Tikhonov regularization.<>
{"title":"Characterization of task performance based on maximum a posteriori reconstructions","authors":"K. Hanson","doi":"10.1109/MDSP.1989.97115","DOIUrl":"https://doi.org/10.1109/MDSP.1989.97115","url":null,"abstract":"The performance of two related tasks, object detection and object amplitude estimation, is investigated. These tasks are related because the best amplitude estimate is the appropriate decision variable for the detection task. Different results have been observed for these two tasks as a function of lambda (a scalar which controls the strength of regularization) in a study restricted to images containing a mixture of high- and low-contrast nonoverlapping disks on a zero background. It has been found that in maximum a posteriori reconstructions the contrast of the low-contrast disks relative to the background decreases steadily as lambda increases. Thus the estimates for the amplitude of these disks deviate from their actual values. On the other hand, the detectability index does not change as quickly. The reason for this is that detectability is based on the separation of the estimate of the amplitude of the object relative to the estimate of the background value compared to their RMS deviations. The choice of lambda obviously becomes an important issue as it affects the bias in the estimated amplitude. It is postulated that the same behavior holds for many other types of Tikhonov regularization.<<ETX>>","PeriodicalId":340681,"journal":{"name":"Sixth Multidimensional Signal Processing Workshop,","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1989-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129325277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summary form only given. A theoretical study of parametric morphological filters that best preserve the crucial topological structure of an input binary image from its noisy version is reported. The topological structure of the input binary image is given, and an arbitrary restoration filter is considered. A collection C of necessary and sufficient conditions for this filter to guarantee the restoration of a binary image from its noisy version, such that the input and restored images have identical topological structure, is derived. It is proved that each of the constraints in C generates a morphological filter. The approach used is to obtain a parametric filter that simultaneously satisfies as many of the constraints in C as possible.<>
{"title":"Parametric morphological filters for pattern restoration","authors":"D. Schonfeld, J. Goutsias","doi":"10.1109/MDSP.1989.97110","DOIUrl":"https://doi.org/10.1109/MDSP.1989.97110","url":null,"abstract":"Summary form only given. A theoretical study of parametric morphological filters that best preserve the crucial topological structure of an input binary image from its noisy version is reported. The topological structure of the input binary image is given, and an arbitrary restoration filter is considered. A collection C of necessary and sufficient conditions for this filter to guarantee the restoration of a binary image from its noisy version, such that the input and restored images have identical topological structure, is derived. It is proved that each of the constraints in C generates a morphological filter. The approach used is to obtain a parametric filter that simultaneously satisfies as many of the constraints in C as possible.<<ETX>>","PeriodicalId":340681,"journal":{"name":"Sixth Multidimensional Signal Processing Workshop,","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1989-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130356603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The environmental modeling aspect of intelligent robot development and research is addressed. It is argued that, at any given time, only a small portion of the robot's world model, called the environment, is used by the robot in its operation and that the environment model at a given instant should contain more detailed and explicit task-oriented information. The ultimate modeling scheme should consist of hierarchical decompositions on various scales such as a resolution scale and an abstraction scale. Issues in designing such an environment model, which will facilitate reasoning in uncertain environments for robots equipped with multiple sensors with disparate characteristics, have been investigated.<>
{"title":"Environment model for advanced robots","authors":"R. Jain","doi":"10.1109/MDSP.1989.97032","DOIUrl":"https://doi.org/10.1109/MDSP.1989.97032","url":null,"abstract":"The environmental modeling aspect of intelligent robot development and research is addressed. It is argued that, at any given time, only a small portion of the robot's world model, called the environment, is used by the robot in its operation and that the environment model at a given instant should contain more detailed and explicit task-oriented information. The ultimate modeling scheme should consist of hierarchical decompositions on various scales such as a resolution scale and an abstraction scale. Issues in designing such an environment model, which will facilitate reasoning in uncertain environments for robots equipped with multiple sensors with disparate characteristics, have been investigated.<<ETX>>","PeriodicalId":340681,"journal":{"name":"Sixth Multidimensional Signal Processing Workshop,","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1989-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121634459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summary form only given. A multiplicative Zac transform that plays the same role in analyzing affine group wavelets as the standard Zac transform plays in Heisenberg-Weyl wavelet theory has been defined in frequency space for causal signals. This construction is based on dilated complex exponentials that are eigenvectors of a sequence of dilation operators. Algorithms, based on the finite Fourier transform have been designed for analysis and synthesis of signals passing through the multiplicative Zac transform.<>
{"title":"A multiplicative Zac transform","authors":"R. Tolimieri","doi":"10.1109/MDSP.1989.97061","DOIUrl":"https://doi.org/10.1109/MDSP.1989.97061","url":null,"abstract":"Summary form only given. A multiplicative Zac transform that plays the same role in analyzing affine group wavelets as the standard Zac transform plays in Heisenberg-Weyl wavelet theory has been defined in frequency space for causal signals. This construction is based on dilated complex exponentials that are eigenvectors of a sequence of dilation operators. Algorithms, based on the finite Fourier transform have been designed for analysis and synthesis of signals passing through the multiplicative Zac transform.<<ETX>>","PeriodicalId":340681,"journal":{"name":"Sixth Multidimensional Signal Processing Workshop,","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1989-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121937234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jeffrey A. Fessler, Albert Macovski, Stanford University
Summary form only given, as follows. Several important estimation problems, in particular the quantification of blood vessel position and radius from projections, involve tracking of dynamics shift band shape parameters. The authors present an alternative algorithm for tracking shift and shape parameters that is based on nonparametric cubic-spline smoothing. Rather than requiring a known Gauss-Markov model, the algorithm assumes only that the shift and shape functions be smoothly varying in a sense defined. They discuss the physical motivation for their (global) optimality criterion, derive an efficient algorithm for computing the optimal estimates, and demonstrate the performance on angiographic data. The performance of the algorithm is demonstrated on simulated angiogram data.<>
{"title":"Non-parametric tracking of shift and shape functions in medical images","authors":"Jeffrey A. Fessler, Albert Macovski, Stanford University","doi":"10.1109/MDSP.1989.97015","DOIUrl":"https://doi.org/10.1109/MDSP.1989.97015","url":null,"abstract":"Summary form only given, as follows. Several important estimation problems, in particular the quantification of blood vessel position and radius from projections, involve tracking of dynamics shift band shape parameters. The authors present an alternative algorithm for tracking shift and shape parameters that is based on nonparametric cubic-spline smoothing. Rather than requiring a known Gauss-Markov model, the algorithm assumes only that the shift and shape functions be smoothly varying in a sense defined. They discuss the physical motivation for their (global) optimality criterion, derive an efficient algorithm for computing the optimal estimates, and demonstrate the performance on angiographic data. The performance of the algorithm is demonstrated on simulated angiogram data.<<ETX>>","PeriodicalId":340681,"journal":{"name":"Sixth Multidimensional Signal Processing Workshop,","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1989-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115896374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summary form only given, as follows. Similarities are found between spatial pattern analysis and other low-level cooperative image analysis tasks. Visual pattern analysis proceeds analogously via estimation of emergent 2D image frequencies. Unlike shape-from-X or optical flow paradigms, constraints are derived from the responses of multiple oriented spatial frequency channels rather than directly from the image irradiance measurements. By selecting channel filters that are sufficiently concentrated in both space and frequency, highly accurate spatial frequency estimates are computed on a local basis. Two related methods are proposed. In the first, a constrained estimate of the emergent image frequencies is obtained by resolving the responses of multiple channel filters in a process similar to photometric stereo. The second approach formulates the estimation of frequencies as an extremum problem regularized by a smoothing term. An iterative constraint propagation algorithm is developed analogous to those used in variational/relaxational approaches to shape-from-X (shading, texture) and optical flow. Examples illustrate both approaches using synthetic and natural images.<>
{"title":"Numerical analysis of visual patterns","authors":"A. Bovik, N. Gopal, T. Emmoth","doi":"10.1109/MDSP.1989.96993","DOIUrl":"https://doi.org/10.1109/MDSP.1989.96993","url":null,"abstract":"Summary form only given, as follows. Similarities are found between spatial pattern analysis and other low-level cooperative image analysis tasks. Visual pattern analysis proceeds analogously via estimation of emergent 2D image frequencies. Unlike shape-from-X or optical flow paradigms, constraints are derived from the responses of multiple oriented spatial frequency channels rather than directly from the image irradiance measurements. By selecting channel filters that are sufficiently concentrated in both space and frequency, highly accurate spatial frequency estimates are computed on a local basis. Two related methods are proposed. In the first, a constrained estimate of the emergent image frequencies is obtained by resolving the responses of multiple channel filters in a process similar to photometric stereo. The second approach formulates the estimation of frequencies as an extremum problem regularized by a smoothing term. An iterative constraint propagation algorithm is developed analogous to those used in variational/relaxational approaches to shape-from-X (shading, texture) and optical flow. Examples illustrate both approaches using synthetic and natural images.<<ETX>>","PeriodicalId":340681,"journal":{"name":"Sixth Multidimensional Signal Processing Workshop,","volume":"49 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1989-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130031745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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