Additive noise in images is estimated by the concept of geostatistics,
which then is implemented in the space-variant Wiener filter for adaptive image restoration. The algorithm is tested on several image versions degraded with Gaussian white noise. The present results are more favorable than those obtained by standard statistical methods.
This paper presents a new approach for extracting spatial features of images based on the theory of regionalized variables. These features can be effectively used for automatic recognition of logo images using neural networks. Experimental results on a public-domain logo database show the effectiveness of the proposed approach.
Spatial covariances based on geostatistics are extracted as representative features of logo or trademark images. These spatial covariances are different from other statistical features for image analysis in that the structural information of an image is independent of the pixel locations and represented in terms of spatial series. We then design a classifier in the sense of hidden Markov models to make use of these geostatistical sequential data to recognize the logos. High recognition rates are obtained from testing the method against a public-domain logo database.
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