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26.06.2021 | History

1 edition of Advanced concepts for intelligent vision systems found in the catalog.

Advanced concepts for intelligent vision systems

10th International Conference, ACIVS 2008, Juan-les-Pins, France, October 20-24, 2008 ; proceedings

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      • International conference proceedings.Includes bibliographical references and index.

        StatementSpringer
        PublishersSpringer
        Classifications
        LC Classifications2008
        The Physical Object
        Paginationxvi, 74 p. :
        Number of Pages93
        ID Numbers
        ISBN 103540884572
        Series
        1
        2Lecture notes in computer science -- 5259
        3

        nodata File Size: 8MB.


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Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. com to load tweets curated by our Twitter account. These CNN models are then trained and tested on both original and data-augmented image datasets for three plant classification problems: Folio, AgrilPlant, and the Swedish leaf dataset.

Conference topics Papers must show original and high-quality work, from basic research to pre-industrial application. The papers are organized in topical sections on image and video coding; systems and applications; video processing; filtering and restoration; segmentation and feature extraction; tracking, scene understanding and computer vision; medical imaging; and biometrics and surveillance.

Venue The conference will take place in Auckland, New Zealand on Feb. The paper should consist of 8-12 pages in A4 format and should conform to the style guidelines outlined on the Acivs 2020 website.

The 48 Advanced concepts for intelligent vision systems presented in this volume were carefully reviewed and selected from a total of 78 submissions. This book constitutes the refereed proceedings of the 17th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2016, held in Lecce, Italy, in October 2016.

For this, we use two convolutional neural network CNN architectures, AlexNet and GoogleNet trained from scratch or using pre-trained weights.

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In this paper, we employ the deep learning framework and determine the effects of several data-augmentation DA techniques for plant classification problems. Want to get more out of the basic search box? We evaluate the utility of six individual DA techniques rotation, blur, contrast, scaling, illumination, and projective transformation and several combinations of these techniques, resulting in a total of 12 data-augmentation methods.

The results show that the CNN methods with particular data-augmented datasets yield the highest accuracies, which also surpass previous results on the three datasets. Synopsis This book constitutes the proceedings of the 20th INternational Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2020, held in Auckland, New Zealand, in Advanced concepts for intelligent vision systems 2020. For example, "World war II" with quotes will give more precise results than World war II without quotes.

Gonzalo Pajares Martinsanz, Universidad Complutense, Madrid, Spain. Data augmentation plays a crucial role in increasing the number of training images, which often aids to improve classification performances of deep learning techniques for computer vision problems. In this paper, we employ the deep learning framework and determine the effects of several data-augmentation DA techniques for plant classification problems. org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data.

Use ILLiad for articles and chapter scans. For web page which are no longer available, try to retrieve content from the of the Internet Archive if available.

Steering Committee Jacques Blanc-Talon, DGA, Paris, France.