Summary form only given, as follows. Multilevel image coding methods for compression and related image processing applications are described. These methods first transform an image, using the specific orthonormal bases of compactly supported wavelets invented by Daubechies (1988), to obtain a multiresolution representation of the image in terms of its transform coefficients. Subsequent multilevel processing is performed directly on these coefficients using techniques such as vector quantization, correlation, and prediction to achieve typical compression, stereoscopic matching, enhancement, or pattern recognition goals. Computational complexity issues and numerical results are discussed.<>
{"title":"Application of wavelets to image coding","authors":"W. Lawton","doi":"10.1109/MDSP.1989.97054","DOIUrl":"https://doi.org/10.1109/MDSP.1989.97054","url":null,"abstract":"Summary form only given, as follows. Multilevel image coding methods for compression and related image processing applications are described. These methods first transform an image, using the specific orthonormal bases of compactly supported wavelets invented by Daubechies (1988), to obtain a multiresolution representation of the image in terms of its transform coefficients. Subsequent multilevel processing is performed directly on these coefficients using techniques such as vector quantization, correlation, and prediction to achieve typical compression, stereoscopic matching, enhancement, or pattern recognition goals. Computational complexity issues and numerical results are discussed.<<ETX>>","PeriodicalId":340681,"journal":{"name":"Sixth Multidimensional Signal Processing Workshop,","volume":"19 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":"121167411","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. Optimal or adaptive array processing has several advantages for applications to acoustic array processing in the ocean. These advantages over conventional methods include better sidelobe control and better noise rejection. The disadvantages include sensitivity to modeling errors directly proportional to the maximum array output signal-to-noise ratio (SNR), and the requirement to know the cross spectral covariance matrix which must be estimated from the data for any real-world applications in the ocean. The authors investigate the robustness of the minimum variance distortionless constraint (MVDC) estimator and so-called robust variants to this algorithm to modeling errors and errors in estimation of the covariance matrix. In particular, they study the sidelobe and nulling behavior and resulting array output SNR versus various levels of these errors.<>
{"title":"Robustness of adaptive array processing","authors":"P.N. Mikhalevsky, A. Baggeroer","doi":"10.1109/MDSP.1989.97040","DOIUrl":"https://doi.org/10.1109/MDSP.1989.97040","url":null,"abstract":"Summary form only given, as follows. Optimal or adaptive array processing has several advantages for applications to acoustic array processing in the ocean. These advantages over conventional methods include better sidelobe control and better noise rejection. The disadvantages include sensitivity to modeling errors directly proportional to the maximum array output signal-to-noise ratio (SNR), and the requirement to know the cross spectral covariance matrix which must be estimated from the data for any real-world applications in the ocean. The authors investigate the robustness of the minimum variance distortionless constraint (MVDC) estimator and so-called robust variants to this algorithm to modeling errors and errors in estimation of the covariance matrix. In particular, they study the sidelobe and nulling behavior and resulting array output SNR versus various levels of these errors.<<ETX>>","PeriodicalId":340681,"journal":{"name":"Sixth Multidimensional Signal Processing Workshop,","volume":"50 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":"126573889","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. One of the determining factors in parametric modeling of a stationary image source is its marginal probability distribution. There have been several different assumptions about this distribution, based on either histogram measurement with an ergodicity assumption or the physics of the image-generating process. Gaussian, Rayleigh, exponential, and some other distributions have been reported to model the source. It is shown that the probability density function of the differential image can be very well modeled as a generalized Gaussian distribution. A Peano-type differential operation, which has been shown to be the optimal scanning method and essentially achieves the entropy of the image asymptotically, has been implemented. The Kolmogorov-Smirnov test for goodness of fit has been used for 20 normal chest X-ray images. On the basis of the test results a first-order generalized Gaussian autoregressive model for the image source has been proposed and its properties and applications studied.<>
{"title":"On modeling the distribution of chest X-ray images","authors":"Y.-Q. Zhang, M. Loew, R. Pickholtz","doi":"10.1109/MDSP.1989.97011","DOIUrl":"https://doi.org/10.1109/MDSP.1989.97011","url":null,"abstract":"Summary form only given. One of the determining factors in parametric modeling of a stationary image source is its marginal probability distribution. There have been several different assumptions about this distribution, based on either histogram measurement with an ergodicity assumption or the physics of the image-generating process. Gaussian, Rayleigh, exponential, and some other distributions have been reported to model the source. It is shown that the probability density function of the differential image can be very well modeled as a generalized Gaussian distribution. A Peano-type differential operation, which has been shown to be the optimal scanning method and essentially achieves the entropy of the image asymptotically, has been implemented. The Kolmogorov-Smirnov test for goodness of fit has been used for 20 normal chest X-ray images. On the basis of the test results a first-order generalized Gaussian autoregressive model for the image source has been proposed and its properties and applications studied.<<ETX>>","PeriodicalId":340681,"journal":{"name":"Sixth Multidimensional Signal Processing Workshop,","volume":"129 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":"134430548","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 recent technique based on maximum likelihood (ML) arguments has been shown to perform quite well in the presence of fully correlated sources and does so in a computationally efficient manner compared to competing techniques such as exhaustive-search ML or vector-space MUSIC. However, it still suffers from a lack of signal-selectivity which can be disadvantageous in some applications, and it requires that the noise be Gaussian and independent and identically distributed from sensor to sensor for the method to be a true maximum-likelihood technique. An algorithm that effectively addresses the above drawbacks by exploiting the known spectral coherence properties of the desired signals as well as their spatial coherence properties has been developed.<>
{"title":"Signal-selective direction finding for fully correlated signals","authors":"S. V. Schell, W. Gardner","doi":"10.1109/MDSP.1989.97080","DOIUrl":"https://doi.org/10.1109/MDSP.1989.97080","url":null,"abstract":"Summary form only given. A recent technique based on maximum likelihood (ML) arguments has been shown to perform quite well in the presence of fully correlated sources and does so in a computationally efficient manner compared to competing techniques such as exhaustive-search ML or vector-space MUSIC. However, it still suffers from a lack of signal-selectivity which can be disadvantageous in some applications, and it requires that the noise be Gaussian and independent and identically distributed from sensor to sensor for the method to be a true maximum-likelihood technique. An algorithm that effectively addresses the above drawbacks by exploiting the known spectral coherence properties of the desired signals as well as their spatial coherence properties has been developed.<<ETX>>","PeriodicalId":340681,"journal":{"name":"Sixth Multidimensional Signal Processing Workshop,","volume":"19 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":"133478454","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. Seismic data are collected, displayed, and interpreted in the time-distance domain (t-x). Local attributes of seismic data can be grouped into conventional single trace attributes (x=constant). The extraction of multitrace attributes is based on a computer-efficient implementation of localized slant stacking (beamforming) and median filtering. Image processing techniques are then applied to support the interpretation of migrated reflection seismic data whereby a seismic section is treated as a two-dimensional image. Local multitrace attributes have been used in a fast and robust coherency enhancement process for noisy seismic data. In a related application, multitrace attributes have provided the required independent data for successful multispectral image enhancement of seismic data. Multiattribute displays are well suited for the structural interpretation of migrated seismic data: this technique can be used for imaging of steeply dipping structures, analyzing uniformities and possible lithological boundaries, and highlighting focusing of diffracted energy and basin bounding faults.<>
{"title":"Multi-attribute processing techniques for the enhancement and interpretation of seismic data","authors":"B. Milkereit, C. Spencer","doi":"10.1109/MDSP.1989.97009","DOIUrl":"https://doi.org/10.1109/MDSP.1989.97009","url":null,"abstract":"Summary form only given. Seismic data are collected, displayed, and interpreted in the time-distance domain (t-x). Local attributes of seismic data can be grouped into conventional single trace attributes (x=constant). The extraction of multitrace attributes is based on a computer-efficient implementation of localized slant stacking (beamforming) and median filtering. Image processing techniques are then applied to support the interpretation of migrated reflection seismic data whereby a seismic section is treated as a two-dimensional image. Local multitrace attributes have been used in a fast and robust coherency enhancement process for noisy seismic data. In a related application, multitrace attributes have provided the required independent data for successful multispectral image enhancement of seismic data. Multiattribute displays are well suited for the structural interpretation of migrated seismic data: this technique can be used for imaging of steeply dipping structures, analyzing uniformities and possible lithological boundaries, and highlighting focusing of diffracted energy and basin bounding faults.<<ETX>>","PeriodicalId":340681,"journal":{"name":"Sixth Multidimensional Signal Processing Workshop,","volume":"55 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":"130595761","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. The 4-D approach to real-time machine vision, which is based on integral spatio-temporal world models, is discussed. The method combines dynamical models for motion of and around the center of gravity with 3-D models for shape representation and the laws of perspective projection in order to arrive at recursive state estimation for objects in high-frequency image sequences. It does not require storing past frames and therefore is well suited for TV signal processing. It is an extension to image sequence understanding of well-known state estimation techniques in modern control theory. The nonunique inversion of the perspective projection is bypassed by recursive least-squares approximation exploiting a local linear relationship between image features and the internal representations of the physical 3-D state variables. The method is especially well suited for the autonomous visual guidance of vehicles, since it integrates control actuation and measurement interpretation.<>
{"title":"Dynamic scene analysis and applications","authors":"E. Dickmanns","doi":"10.1109/MDSP.1989.97049","DOIUrl":"https://doi.org/10.1109/MDSP.1989.97049","url":null,"abstract":"Summary form only given. The 4-D approach to real-time machine vision, which is based on integral spatio-temporal world models, is discussed. The method combines dynamical models for motion of and around the center of gravity with 3-D models for shape representation and the laws of perspective projection in order to arrive at recursive state estimation for objects in high-frequency image sequences. It does not require storing past frames and therefore is well suited for TV signal processing. It is an extension to image sequence understanding of well-known state estimation techniques in modern control theory. The nonunique inversion of the perspective projection is bypassed by recursive least-squares approximation exploiting a local linear relationship between image features and the internal representations of the physical 3-D state variables. The method is especially well suited for the autonomous visual guidance of vehicles, since it integrates control actuation and measurement interpretation.<<ETX>>","PeriodicalId":340681,"journal":{"name":"Sixth Multidimensional Signal Processing Workshop,","volume":"315 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":"115147821","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 major clinical potentials of MR imaging, as determined by research done in industry and universities, are identified and discussed. These are cardiovascular imaging, fast scan imaging, motion compensation, and spectroscopy. The problems of RF pulse design and image processing are considered.<>
{"title":"Technical challenges of magnetic resonance (MR) imaging","authors":"M. Buonocore","doi":"10.1109/MDSP.1989.96985","DOIUrl":"https://doi.org/10.1109/MDSP.1989.96985","url":null,"abstract":"The major clinical potentials of MR imaging, as determined by research done in industry and universities, are identified and discussed. These are cardiovascular imaging, fast scan imaging, motion compensation, and spectroscopy. The problems of RF pulse design and image processing are considered.<<ETX>>","PeriodicalId":340681,"journal":{"name":"Sixth Multidimensional Signal Processing Workshop,","volume":"120 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":"115719973","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 fast edge detector architecture and IC, based on a new edge follower algorithm, have been designed. The chip offers real-time processing with a limited amount of hardware due to the optimization of the critical path in the architecture. In this way, a complete frame (512*512) can be processed in about 400000 clock cycles, and a clock rate of up to 10 MHz has been achieved in a 3- mu m double-metal CMOS technology. This chip offers online information such as edge location and orientation, which can be used for feature extraction and pattern recognition in the robot vision system. A novel architectural model, the multiplexed cooperating datapath architecture, has been adopted to obtain an efficient design with a minimal set of functional building blocks. The methodology is especially suited for recursive types of algorithms such as the edge follower. High throughput is achieved by optimizing the memory storage and by eliminating the communication bottlenecks with dedicated buses.<>
{"title":"A fast edge detection chip for robot vision systems","authors":"C.Y. Lee, F. Catthoor, H. de Man","doi":"10.1109/MDSP.1989.97027","DOIUrl":"https://doi.org/10.1109/MDSP.1989.97027","url":null,"abstract":"Summary form only given. A fast edge detector architecture and IC, based on a new edge follower algorithm, have been designed. The chip offers real-time processing with a limited amount of hardware due to the optimization of the critical path in the architecture. In this way, a complete frame (512*512) can be processed in about 400000 clock cycles, and a clock rate of up to 10 MHz has been achieved in a 3- mu m double-metal CMOS technology. This chip offers online information such as edge location and orientation, which can be used for feature extraction and pattern recognition in the robot vision system. A novel architectural model, the multiplexed cooperating datapath architecture, has been adopted to obtain an efficient design with a minimal set of functional building blocks. The methodology is especially suited for recursive types of algorithms such as the edge follower. High throughput is achieved by optimizing the memory storage and by eliminating the communication bottlenecks with dedicated buses.<<ETX>>","PeriodicalId":340681,"journal":{"name":"Sixth Multidimensional Signal Processing Workshop,","volume":"59 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":"116314270","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. The Frazier-Jawerth transform (FJT), originally the phi-transform, is similar to the wavelet transform and is distinguished by the fact that the analyzing functions form an overcomplete basis for he signal space and may be nonorthogonal. This added flexibility makes possible the definition of optimal analyzing functions, which are the focus of this study. For continuous-time and infinite discrete-time signals, the optimally localized functions are the prolate spheroidal wave functions and their discrete versions. For finite discrete-time signals and images, generalizations of these functions that are applicable for use in the FJT have been identified by the authors.<>
{"title":"Compact functions and the Frazier-Jawerth transform","authors":"D. Fuhrmann, A. Kumar, J. R. Cox","doi":"10.1109/MDSP.1989.97063","DOIUrl":"https://doi.org/10.1109/MDSP.1989.97063","url":null,"abstract":"Summary form only given. The Frazier-Jawerth transform (FJT), originally the phi-transform, is similar to the wavelet transform and is distinguished by the fact that the analyzing functions form an overcomplete basis for he signal space and may be nonorthogonal. This added flexibility makes possible the definition of optimal analyzing functions, which are the focus of this study. For continuous-time and infinite discrete-time signals, the optimally localized functions are the prolate spheroidal wave functions and their discrete versions. For finite discrete-time signals and images, generalizations of these functions that are applicable for use in the FJT have been identified by the authors.<<ETX>>","PeriodicalId":340681,"journal":{"name":"Sixth Multidimensional Signal Processing Workshop,","volume":"172 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":"121034439","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 specific problem that was addressed is one in which there is limited data in both the temporal and spatial dimensions, so that one cannot assume the use of ordinary Fourier transforms on the time domain outputs of each sensor. Rather, zero-mean Gaussian statistics were assumed, and the likelihood of the observed data was directly maximized with respect to the parameters which enter into the covariance matrix of the multivariate distribution. Two models were pursued. The first is a parametric model in which it is assumed that there are a fixed number of independent, wide-sense-stationary, plane-wave signals. The second model is one in which there is energy impinging upon the array from a spatial continuum. EM (expectation-maximization) algorithms appropriate for these two problems were derived.<>
{"title":"Maximum-likelihood wideband direction-of-arrival estimation","authors":"D. Fuhrmann, M. Miller","doi":"10.1109/MDSP.1989.97076","DOIUrl":"https://doi.org/10.1109/MDSP.1989.97076","url":null,"abstract":"The specific problem that was addressed is one in which there is limited data in both the temporal and spatial dimensions, so that one cannot assume the use of ordinary Fourier transforms on the time domain outputs of each sensor. Rather, zero-mean Gaussian statistics were assumed, and the likelihood of the observed data was directly maximized with respect to the parameters which enter into the covariance matrix of the multivariate distribution. Two models were pursued. The first is a parametric model in which it is assumed that there are a fixed number of independent, wide-sense-stationary, plane-wave signals. The second model is one in which there is energy impinging upon the array from a spatial continuum. EM (expectation-maximization) algorithms appropriate for these two problems were derived.<<ETX>>","PeriodicalId":340681,"journal":{"name":"Sixth Multidimensional Signal Processing Workshop,","volume":"67 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":"122524838","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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