Robert Koo (24 results)

Soft cover. Condition: Fine. 1st Edition. ISSN # 0440-2316. From the collection of Robert Hershon.

Hardcover. Condition: Fine. Dust Jacket Condition: Fine. 341 pages.

Abstracts of Articles 1 à 9 in : University of Washington Publications in Fisheries, New Series, Volume I, "Studies of Alaska Red Salmon", University of Washington Press, 1962, Contributions No. 105 à 113, College of Fisheries, University of Washington, 18 pages de 15,50x23,50 cm Bon état - Pour les envois hors de France, la tafication Ğlivre & brochureğ pour les frais de port a disparue.Les frais de port annoncés correspondent à une moyenne. Ils seront calculés au plus juste en fonction du poids de votre article.

Paperback. Condition: Near Fine. Chuang, Che; Chen, Cheng-Hsiung [Robert], Yang, Hsing-Shen, Koo, Chung-Kuang [in] (illustrator). First Edition. Exhibition Catalog With Color And B/W Reproductions. Inscribed "To My Dear Friend Mr. ----- H. S. Yang 1973.8.6". Near Fine. Signed by Illustrator(s).

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

paperback. Condition: New. In shrink wrap. Looks like an interesting title!

Buch. Condition: Neu. Neuware - Hyperspectral imagery (HSI) analysis is frequently employed by the Department of Defense for the purpose of classifying objects within an image as a form of target detection. In this research a robust Two-Phase Filtering Independent Component Analysis (ICA) Target Detection Method is proposed and validated. This new method resolves two main challenges encountered when implementing target detection methods using ICA, a high order statistics feature extraction (FE) method. The first challenge is the high computational demand imposed by the large volume of data associated with HSI during the FE process. To alleviate the effort required for ICA data processing, principal component analysis (PCA), a classical second order statistics method, is used for data reduction. Furthermore, the performance of using PCA under classification is compared against recently developed supervised FE techniques. The second challenge arises during the feature selection (FS) phase after the statistically independent components have been extracted. Current ICA target FS techniques have shown to be either unreliable or require significant user-intervention. A reliable FS process is essential in automating the target detection process. This proposed method uses ICA to extract independent features from the retained principal components, and is followed by an unsupervised target FS with a two-phase filtering process using kurtosis and mean silhouette values. This method achieved promising results when tested against a wide range of benchmark images.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

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

in-12, relié sous rodhoïd et étui illustré, 330 pages. A l'état de neuf.

Condition: New. Print on Demand.

HRD. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condition: New. Print on Demand.

LeatherBound. Condition: New. BOOKS ARE EXEMPT FROM IMPORT DUTIES AND TARIFFS; NO EXTRA CHARGES APPLY. Leatherbound edition. Condition: New. Leather Binding on Spine and Corners with Golden leaf printing on spine. Bound in genuine leather with Satin ribbon page markers and Spine with raised gilt bands. Pages: 160. A perfect gift for your loved ones. Reprinted from 1974 edition. NO changes have been made to the original text. This is NOT a retyped or an ocr'd reprint. Illustrations, Index, if any, are included in black and white. Each page is checked manually before printing. As this print on demand book is reprinted from a very old book, there could be some missing or flawed pages, but we always try to make the book as complete as possible. Fold-outs, if any, are not part of the book. If the original book was published in multiple volumes then this reprint is of only one volume, not the whole set. IF YOU WISH TO ORDER PARTICULAR VOLUME OR ALL THE VOLUMES YOU CAN CONTACT US. Resized as per current standards. Sewing binding for longer life, where the book block is actually sewn (smythe sewn/section sewn) with thread before binding which results in a more durable type of binding. Language: English Pages: 160.

Condition: Bueno. 1st ed. 22x33. 276pp. Publisher's boards. Tela editorial. Publisher's boards. Tela editorial.

HRD. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condition: New. PRINT ON DEMAND.

Condition: New. Print on Demand.

Condition: New. Print on Demand.

Condition: New. PRINT ON DEMAND.

LeatherBound. Condition: New. BOOKS ARE EXEMPT FROM IMPORT DUTIES AND TARIFFS; NO EXTRA CHARGES APPLY. LeatherBound edition. Condition: New. Reprinted from 1953 edition. Leather Binding on Spine and Corners with Golden leaf printing on spine. Bound in genuine leather with Satin ribbon page markers and Spine with raised gilt bands. A perfect gift for your loved ones. Pages: 166 NO changes have been made to the original text. This is NOT a retyped or an ocr'd reprint. Illustrations, Index, if any, are included in black and white. Each page is checked manually before printing. As this print on demand book is reprinted from a very old book, there could be some missing or flawed pages, but we always try to make the book as complete as possible. Fold-outs, if any, are not part of the book. If the original book was published in multiple volumes then this reprint is of only one volume, not the whole set. Sewing binding for longer life, where the book block is actually sewn (smythe sewn/section sewn) with thread before binding which results in a more durable type of binding. Pages: 166.

Paperback. Condition: new. Paperback. Hyperspectral imagery (HSI) analysis is frequently employed by the Department of Defense for the purpose of classifying objects within an image as a form of target detection. In this research a robust Two-Phase Filtering Independent Component Analysis (ICA) Target Detection Method is proposed and validated. This new method resolves two main challenges encountered when implementing target detection methods using ICA, a high order statistics feature extraction (FE) method. The first challenge is the high computational demand imposed by the large volume of data associated with HSI during the FE process. To alleviate the effort required for ICA data processing, principal component analysis (PCA), a classical second order statistics method, is used for data reduction. Furthermore, the performance of using PCA under classification is compared against recently developed supervised FE techniques. The second challenge arises during the feature selection (FS) phase after the statistically independent components have been extracted. Current ICA target FS techniques have shown to be either unreliable or require significant user-intervention. A reliable FS process is essential in automating the target detection process. This proposed method uses ICA to extract independent features from the retained principal components, and is followed by an unsupervised target FS with a two-phase filtering process using kurtosis and mean silhouette values. This method achieved promising results when tested against a wide range of benchmark images.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Condition: New. Print on Demand pp. 140.

Hardcover. Condition: new. Hardcover. Hyperspectral imagery (HSI) analysis is frequently employed by the Department of Defense for the purpose of classifying objects within an image as a form of target detection. In this research a robust Two-Phase Filtering Independent Component Analysis (ICA) Target Detection Method is proposed and validated. This new method resolves two main challenges encountered when implementing target detection methods using ICA, a high order statistics feature extraction (FE) method. The first challenge is the high computational demand imposed by the large volume of data associated with HSI during the FE process. To alleviate the effort required for ICA data processing, principal component analysis (PCA), a classical second order statistics method, is used for data reduction. Furthermore, the performance of using PCA under classification is compared against recently developed supervised FE techniques. The second challenge arises during the feature selection (FS) phase after the statistically independent components have been extracted. Current ICA target FS techniques have shown to be either unreliable or require significant user-intervention. A reliable FS process is essential in automating the target detection process. This proposed method uses ICA to extract independent features from the retained principal components, and is followed by an unsupervised target FS with a two-phase filtering process using kurtosis and mean silhouette values. This method achieved promising results when tested against a wide range of benchmark images.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.