You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print format on
SPIE.org.
Abstract
Consider three commonplace imaging scenarios: (1) a document image is sent through various digital operations, say, scanning, printing, copying, and then faxing; (2) an image is compressed to reduce the number of bytes for storage or transmission; and (3) geometric features are computed from an image to characterize the degree to which an industrial process is in control. In each scenario, characterization of the image processing cannot be based on the effects of processing a single image, or on the effects of processing any finite number of images. For the document image, if one wishes to design a filter that will restore it, then that filter needs to be designed in accordance with how the various stages of image processing affect the class of images to be filtered, in particular, how the processing affects the probabilistic distribution of the image class. In the case of the compressed image, if one wishes to measure the degree of compression or to design a decompression filter, then both the compression and goodness of the restoration filter must be evaluated relative to the class of images to be compressed and decompressed. Any particular image will likely occur very rarely and the system must be designed and evaluated probabilistically. Finally, for feature generation, image observations will vary, features will be random variables, and classification accuracy will depend on the joint distribution of the features. At their root, image and signal processing are applied disciplines within the domain of random processes.
Online access to SPIE eBooks is limited to subscribing institutions.