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Statistical Image Processing Techniques for Noisy Images [electronic resource] : An Application-Oriented Approach /
Statistical Processing Techniques for Noisy Images presents a statistical framework to design algorithms for target detection, tracking, segmentation and classification (identification). Its main goal is to provide the reader with efficient tools for developing algorithms that solve his/her own image processing applications. In particular, such topics as hypothesis test-based detection, fast active contour segmentation and algorithm design for non-conventional imaging systems are comprehensively treated, from theoretical foundations to practical implementations. With a large number of illustrations and practical examples, this book serves as an excellent textbook or reference book for senior or graduate level courses on statistical signal/image processing, as well as a reference for researchers in related fields.
| Main Authors: | , , |
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| Format: | Texto biblioteca |
| Language: | eng |
| Published: |
Boston, MA : Springer US : Imprint: Springer,
2004
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| Subjects: | Computer science., Computer graphics., Image processing., Remote sensing., Statistics., Computer Science., Image Processing and Computer Vision., Computer Imaging, Vision, Pattern Recognition and Graphics., Remote Sensing/Photogrammetry., Optics, Lasers, Photonics, Optical Devices., Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences., |
| Online Access: | http://dx.doi.org/10.1007/978-1-4419-8855-3 |
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