Scale space analysis by stabilized inverse diffusion equations

Caption title.Includes bibliographical references (p. 11).Supported by AFSOR. F49620-95-1-0083 Supported by ONR. N00014-91-J-1004 Supported in part by Boston University under the AFOSR Multidisciplinary Research Program on Reduced Signature Target Recognition. GC123919NGDIlya Pollak, Alan S. Willsky, Hamid Krim

A Framework For TV Logos Learning Using Linear Inverse Diffusion Filters For Noise Removal

Different logotypes represent significant cues for video annotations. A combination of temporal and spatial segmentation methods can be used for logo extraction from various video contents. To achieve this segmentation, pixels with low variation of intensity over time are detected. Static backgrounds can become spurious parts of these logos. This paper offers a new way to use several segmentations of logos to learn new logo models from which noise has been removed. First, we group segmented logos of similar appearances into different clusters. Then, a model is learned for each cluster that has a minimum number of members. This is done by applying a linear inverse diffusion filter to all logos in each cluster. Our experiments demonstrate that this filter removes most of the noise that was added to the logo during segmentation and it successfully copes with misclassified logos that have been wrongly added to a cluster

Scale Space Analysis by Stabilized Inverse Diffusion Equations

. We introduce a family of first-order multi-dimensional ordinary differential equations (ODEs) with discontinuous right-hand sides and demonstrate their applicability in image processing. An equation belonging to this family is an inverse diffusion everywhere except at local extrema, where some stabilization is introduced. For this reason, we call these equations "stabilized inverse diffusion equations" ("SIDEs"). A SIDE in one spatial dimension may be interpreted as a limiting case of a semi-discretized Perona-Malik equation [3, 4]. In an experimental section, SIDEs are shown to suppress noise while sharpening edges present in the input signal. Their application to image segmentation is demonstrated. 1 Introduction In this paper we introduce, analyze, and apply a new class of nonlinear image processing algorithms. These algorithms are motivated by the great recent interest in using evolutions specified by partial differential equations (PDE's) as image processing procedures for tas..