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Decision Forests for Computer Vision and Medical Image Analysis (Record no. 356216)
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| 000 -Маркер записи | |
|---|---|
| Контрольное поле постоянной длины | 05237nam a22004935i 4500 |
| 001 - Контрольный номер | |
| Контрольное поле | vtls000483470 |
| 005 - Дата корректировки | |
| Контрольное поле | 20210922065620.0 |
| 007 - Кодируемые данные (физ. описан.) | |
| Контрольное поле постоянной длины | cr nn 008mamaa |
| 008 - Кодируемые данные | |
| Контрольное поле постоянной длины | 140715s2013 xxk| s |||| 0|eng d |
| 020 ## - Индекс ISBN | |
| ISBN | 9781447149293 |
| -- | 978-1-4471-4929-3 |
| 024 7# - Прочие стандартные номера | |
| Стандартный номер | 10.1007/978-1-4471-4929-3 |
| Источник номера | doi |
| 035 ## - Системный контрольный номер | |
| Системный контрольный номер | to000483470 |
| 040 ## - Источник каталогиз. | |
| Служба первич. каталог. | Springer |
| Служба, преобразующая запись | Springer |
| Организация, изменившая запись | RU-ToGU |
| 050 #4 - Расстановочный код библ. Конгресса | |
| Классификационный индекс | Q337.5 |
| 050 #4 - Расстановочный код библ. Конгресса | |
| Классификационный индекс | TK7882.P3 |
| 072 #7 - Код предметной/темат. категории | |
| Код предметной/темат. категории | UYQP |
| Источник кода | bicssc |
| 072 #7 - Код предметной/темат. категории | |
| Код предметной/темат. категории | COM016000 |
| Источник кода | bisacsh |
| 082 04 - Индекс Дьюи | |
| Индекс Дьюи | 006.4 |
| Номер издания | 23 |
| 100 1# - Автор | |
| Автор | Criminisi, A. |
| Роль лиц | editor. |
| 9 (RLIN) | 413570 |
| 245 10 - Заглавие | |
| Заглавие | Decision Forests for Computer Vision and Medical Image Analysis |
| Физический носитель | electronic resource |
| Ответственность | edited by A. Criminisi, J. Shotton. |
| 260 ## - Выходные данные | |
| Место издания | London : |
| Издательство | Springer London : |
| -- | Imprint: Springer, |
| Дата издания | 2013. |
| 300 ## - Физическое описание | |
| Объем | XIX, 368 p. 143 illus., 136 illus. in color. |
| Иллюстрации/тип воспроизводства | online resource. |
| 336 ## - Тип содержимого | |
| Тип содержимого | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - Средство доступа | |
| Средство доступа | computer |
| Media type code | c |
| Source | rdamedia |
| 338 ## - Тип носителя | |
| Тип носителя | online resource |
| Carrier type code | cr |
| Source | rdacarrier |
| 490 1# - Серия | |
| Заглавие серии | Advances in Computer Vision and Pattern Recognition, |
| ISSN серии | 2191-6586 |
| 505 0# - Примечание о содержании | |
| Содержание | Overview and Scope -- Notation and Terminology -- Part I: The Decision Forest Model -- Introduction -- Classification Forests -- Regression Forests -- Density Forests -- Manifold Forests -- Semi-Supervised Classification Forests -- Part II: Applications in Computer Vision and Medical Image Analysis -- Keypoint Recognition Using Random Forests and Random Ferns -- Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval -- Class-Specific Hough Forests for Object Detection -- Hough-Based Tracking of Deformable Objects -- Efficient Human Pose Estimation from Single Depth Images -- Anatomy Detection and Localization in 3D Medical Images -- Semantic Texton Forests for Image Categorization and Segmentation -- Semi-Supervised Video Segmentation Using Decision Forests -- Classification Forests for Semantic Segmentation of Brain Lesions in Multi-Channel MRI -- Manifold Forests for Multi-Modality Classification of Alzheimer’s Disease -- Entangled Forests and Differentiable Information Gain Maximization -- Decision Tree Fields -- Part III: Implementation and Conclusion -- Efficient Implementation of Decision Forests -- The Sherwood Software Library -- Conclusions. |
| 520 ## - Аннотация | |
| Аннотация | Decision forests (also known as random forests) are an indispensable tool for automatic image analysis. This practical and easy-to-follow text explores the theoretical underpinnings of decision forests, organizing the vast existing literature on the field within a new, general-purpose forest model. A number of exercises encourage the reader to practice their skills with the aid of the provided free software library. An international selection of leading researchers from both academia and industry then contribute their own perspectives on the use of decision forests in real-world applications such as pedestrian tracking, human body pose estimation, pixel-wise semantic segmentation of images and videos, automatic parsing of medical 3D scans, and detection of tumors. The book concludes with a detailed discussion on the efficient implementation of decision forests. Topics and features: With a foreword by Prof. Yali Amit and Prof. Donald Geman, recounting their participation in the development of decision forests Introduces a flexible decision forest model, capable of addressing a large and diverse set of image and video analysis tasks Investigates both the theoretical foundations and the practical implementation of decision forests Discusses the use of decision forests for such tasks as classification, regression, density estimation, manifold learning, active learning and semi-supervised classification Includes exercises and experiments throughout the text, with solutions, slides, demo videos and other supplementary material provided at an associated website Provides a free, user-friendly software library, enabling the reader to experiment with forests in a hands-on manner With its clear, tutorial structure and supporting exercises, this text will be of great value to students wishing to learn the basics of decision forests, researchers wanting to become more familiar with forest-based learning, and practitioners interested in exploring modern and efficient image analysis techniques. Dr. A. Criminisi and Dr. J. Shotton are Senior Researchers in the Computer Vision Group at Microsoft Research Cambridge, UK. |
| 650 #0 - Тематические рубрики | |
| Основная рубрика | Computer Science. |
| 9 (RLIN) | 155490 |
| 650 #0 - Тематические рубрики | |
| Основная рубрика | Artificial intelligence. |
| 9 (RLIN) | 274099 |
| 650 #0 - Тематические рубрики | |
| Основная рубрика | Optical pattern recognition. |
| 9 (RLIN) | 304126 |
| 650 14 - Тематические рубрики | |
| Основная рубрика | Computer Science. |
| 9 (RLIN) | 155490 |
| 650 24 - Тематические рубрики | |
| Основная рубрика | Pattern Recognition. |
| 9 (RLIN) | 304129 |
| 650 24 - Тематические рубрики | |
| Основная рубрика | Artificial Intelligence (incl. Robotics). |
| 9 (RLIN) | 274102 |
| 700 1# - Другие авторы | |
| Другие авторы | Shotton, J. |
| Роль лиц | editor. |
| 9 (RLIN) | 413571 |
| 710 2# - Другие организации | |
| Организация/юрисдикция | SpringerLink (Online service) |
| 9 (RLIN) | 143950 |
| 773 0# - Источник информации | |
| Название источника | Springer eBooks |
| 830 #0 - Заголовок добавочной библ.записи на серию — унифицированное заглавие | |
| Унифицированное заглавие | Advances in Computer Vision and Pattern Recognition, |
| 9 (RLIN) | 413327 |
| 856 40 - Электронный адрес документа | |
| URL | <a href="http://dx.doi.org/10.1007/978-1-4471-4929-3">http://dx.doi.org/10.1007/978-1-4471-4929-3</a> |
| 912 ## - Coursera for Campus: онлайн курсы для ТГУ | |
| Coursera for Campus: онлайн курсы для ТГУ | ZDB-2-SCS |
| 999 ## - Системные контрольные номера (Koha) | |
| biblionumber (Koha) | 356216 |
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