| 書目名稱 | Image Understanding using Sparse Representations | | 編輯 | Jayaraman J. Tiagarajan,Karthikeyan Natesan Ramamu | | 視頻video | http://file.papertrans.cn/462/461470/461470.mp4 | | 叢書名稱 | Synthesis Lectures on Image, Video, and Multimedia Processing | | 圖書封面 |  | | 描述 | Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the sparsity of natural signals has led to advances in several application areas including image compression, denoising, inpainting, compressed sensing, blind source separation, super-resolution, and classification. The primary goal of this book is to present the theory and algorithmic considerations in using sparse models for image understanding and computer vision applications. To this end, algorithms for obtaining sparse representations and their performance guarantees are discussed in the initial chapters. Furthermore, approaches for designing overcomplete, data-adapted dictionaries to model natural images are described. The development of theory behind dictionary learning involves exploring its connection to unsupervised clustering and analyz | | 出版日期 | Book 2014 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-02250-0 | | isbn_softcover | 978-3-031-01122-1 | | isbn_ebook | 978-3-031-02250-0Series ISSN 1559-8136 Series E-ISSN 1559-8144 | | issn_series | 1559-8136 | | copyright | Springer Nature Switzerland AG 2014 |
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