Pattern Based Compression Multi Band Image by Myers Wayne (26 results)

Condition: New. pp. 208.

Condition: New. pp. 208 69 Illus.

Condition: New. pp. 208.

2006. 16 x 24 cm. XVIII, 190 S. XVIII, 190 p. 69 illus. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. (Environmental and Ecological Statistics). Sprache: Englisch.

Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Paperback. Condition: new. Paperback. We offer here a non-conventional approach to muhivariate ima- structured data for which the basis is well tested but the analytical ramifi cations are still unfolding. Although we do not formally pursue them, there are several parallels with the nature of neural networks. We employ a systematic set of statistical heuristics for modeling multivariate image data in a quasi-perceptual manner. When the human eye perceives a scene, the elements of the scene are segregated heuristically into compo nents according to similarity and dissimilarity, and then the relationships among the components are interpreted. Similarly, we segregate or seg ment the scene into hierarchically organized components that are subject to subsequent statistical analysis in many modes for interpretive purposes. We refer to the segregated scene segments as patterns, since they provide a basis for perception of pattern. Since they are also hierarchically organ ized, we refer to them further as polypatterns. This leads us to our acro nym of Progressively Segmented Image Modeling As Poly-Patterns (PSIMAPP). Likewise, we formalize our approach in terms of pattern processes and segmentation sequences. In alignment with the terminology of image analysis, we refer to our multivariate measures as being signal bands. When the human eye perceives a scene, the elements of the scene are segregated heuristically into compo nents according to similarity and dissimilarity, and then the relationships among the components are interpreted. We refer to the segregated scene segments as patterns, since they provide a basis for perception of pattern. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condition: New.

Condition: New.

Condition: New.

Condition: New.

Hardcover. Condition: new. Hardcover. We offer here a non-conventional approach to muhivariate ima- structured data for which the basis is well tested but the analytical ramifi cations are still unfolding. Although we do not formally pursue them, there are several parallels with the nature of neural networks. We employ a systematic set of statistical heuristics for modeling multivariate image data in a quasi-perceptual manner. When the human eye perceives a scene, the elements of the scene are segregated heuristically into compo nents according to similarity and dissimilarity, and then the relationships among the components are interpreted. Similarly, we segregate or seg ment the scene into hierarchically organized components that are subject to subsequent statistical analysis in many modes for interpretive purposes. We refer to the segregated scene segments as patterns, since they provide a basis for perception of pattern. Since they are also hierarchically organ ized, we refer to them further as polypatterns. This leads us to our acro nym of Progressively Segmented Image Modeling As Poly-Patterns (PSIMAPP). Likewise, we formalize our approach in terms of pattern processes and segmentation sequences. In alignment with the terminology of image analysis, we refer to our multivariate measures as being signal bands. When the human eye perceives a scene, the elements of the scene are segregated heuristically into compo nents according to similarity and dissimilarity, and then the relationships among the components are interpreted. We refer to the segregated scene segments as patterns, since they provide a basis for perception of pattern. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condition: As New. Unread book in perfect condition.

Condition: New. pp. 208.

Condition: New.

Condition: As New. Unread book in perfect condition.

Paperback. Condition: Brand New. 190 pages. 9.00x6.00x0.47 inches. In Stock.

Gebunden. Condition: New. Presents a non-conventional approach to multivariate image-structured dataPresents a non-conventional approach to multivariate image-structured dataIncludes supplementary material: sn.pub/extrasDr. Wayne L. Myers earned M.F. .

Condition: New. In.

Paperback. Condition: Like New. Like New. book.

Paperback. Condition: new. Paperback. We offer here a non-conventional approach to muhivariate ima- structured data for which the basis is well tested but the analytical ramifi cations are still unfolding. Although we do not formally pursue them, there are several parallels with the nature of neural networks. We employ a systematic set of statistical heuristics for modeling multivariate image data in a quasi-perceptual manner. When the human eye perceives a scene, the elements of the scene are segregated heuristically into compo nents according to similarity and dissimilarity, and then the relationships among the components are interpreted. Similarly, we segregate or seg ment the scene into hierarchically organized components that are subject to subsequent statistical analysis in many modes for interpretive purposes. We refer to the segregated scene segments as patterns, since they provide a basis for perception of pattern. Since they are also hierarchically organ ized, we refer to them further as polypatterns. This leads us to our acro nym of Progressively Segmented Image Modeling As Poly-Patterns (PSIMAPP). Likewise, we formalize our approach in terms of pattern processes and segmentation sequences. In alignment with the terminology of image analysis, we refer to our multivariate measures as being signal bands. When the human eye perceives a scene, the elements of the scene are segregated heuristically into compo nents according to similarity and dissimilarity, and then the relationships among the components are interpreted. We refer to the segregated scene segments as patterns, since they provide a basis for perception of pattern. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Hardcover. Condition: new. Hardcover. We offer here a non-conventional approach to muhivariate ima- structured data for which the basis is well tested but the analytical ramifi cations are still unfolding. Although we do not formally pursue them, there are several parallels with the nature of neural networks. We employ a systematic set of statistical heuristics for modeling multivariate image data in a quasi-perceptual manner. When the human eye perceives a scene, the elements of the scene are segregated heuristically into compo nents according to similarity and dissimilarity, and then the relationships among the components are interpreted. Similarly, we segregate or seg ment the scene into hierarchically organized components that are subject to subsequent statistical analysis in many modes for interpretive purposes. We refer to the segregated scene segments as patterns, since they provide a basis for perception of pattern. Since they are also hierarchically organ ized, we refer to them further as polypatterns. This leads us to our acro nym of Progressively Segmented Image Modeling As Poly-Patterns (PSIMAPP). Likewise, we formalize our approach in terms of pattern processes and segmentation sequences. In alignment with the terminology of image analysis, we refer to our multivariate measures as being signal bands. When the human eye perceives a scene, the elements of the scene are segregated heuristically into compo nents according to similarity and dissimilarity, and then the relationships among the components are interpreted. We refer to the segregated scene segments as patterns, since they provide a basis for perception of pattern. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Buch. Condition: Neu. Neuware - We offer here a non-conventional approach to muhivariate ima- structured data for which the basis is well tested but the analytical ramifi cations are still unfolding. Although we do not formally pursue them, there are several parallels with the nature of neural networks. We employ a systematic set of statistical heuristics for modeling multivariate image data in a quasi-perceptual manner. When the human eye perceives a scene, the elements of the scene are segregated heuristically into compo nents according to similarity and dissimilarity, and then the relationships among the components are interpreted. Similarly, we segregate or seg ment the scene into hierarchically organized components that are subject to subsequent statistical analysis in many modes for interpretive purposes. We refer to the segregated scene segments as patterns, since they provide a basis for perception of pattern. Since they are also hierarchically organ ized, we refer to them further as polypatterns. This leads us to our acro nym of Progressively Segmented Image Modeling As Poly-Patterns (PSIMAPP). Likewise, we formalize our approach in terms of pattern processes and segmentation sequences. In alignment with the terminology of image analysis, we refer to our multivariate measures as being signal bands.

Condition: New. Print on Demand pp. 208 69 Illus.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents a non-conventional approach to multivariate image-structured dataDr. Wayne L. Myers earned M.F. and Ph.D. degrees in forest ecology and forest entomology at the University of Michigan. He began his professional career in Canada as a r.

Condition: New. PRINT ON DEMAND pp. 208.

Hardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 506.