Recognition of Planar Objects Using Multiresolution Analysis
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Research Article Recognition of Planar Objects Using Multiresolution Analysis ¨ ¨ un ¨ Nazlı Guney and Ays¸ın Ertuz Department of Electrical and Electronics Engineering, Bo¯gazic¸i University, 34342 Bebek, Istanbul, Turkey Received 29 August 2005; Revised 29 May 2006; Accepted 16 July 2006 Recommended by Antonio Ortega By using affine-invariant shape descriptors, it is possible to recognize an unknown planar object from an image taken from an arbitrary view when standard view images of candidate objects exist in a database. In a previous study, an affine-invariant function calculated from the wavelet coefficients of the object boundary has been proposed. In this work, the invariant is constructed from the multiwavelet and (multi)scaling function coefficients of the boundary. Multiwavelets are known to have superior performance compared to scalar wavelets in many areas of signal processing due to their simultaneous orthogonality, symmetry, and short support properties. Going from scalar wavelets to multiwavelets is challenging due to the increased dimensionality of multiwavelets. This increased dimensionality is exploited to construct invariants with better performance when the multiwavelet “detail” coefficients are available. However, with (multi)scaling function coefficients, which are more stable in the presence of noise, scalar wavelets cannot be defeated. Copyright © 2007 Hindawi Publishing Corporation. All rights reserved.
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INTRODUCTION
Object recognition is one of the most difficult problems in computer vision. However, if the problem definition includes only planar objects, which are to be viewed from arbitrary directions, it is possible to design recognition systems that have satisfactory performances. When the depth of an object along the line of sight is small compared to the viewing distance of the camera, as its images are produced from different viewpoints, it seems to be going through an affine transformation. Thus, for recognition of planar objects, it suffices to find suitable affine invariants. These invariants are shape descriptors that remain unchanged even when the viewing point of the camera changes. Therefore, it may be said that object recognition is a search for invariants [1]. Recognition techniques are classified according to how the shape descriptors are calculated from the images of objects. One such classification is based on whether the boundary or the region of the object is required. Region-based techniques take into account the whole region in the image corresponding to the object, whereas boundary-based techniques analyze the object boundary. Analyzing only the boundary is advantageous compared to the region-based techniques in terms of computational complexity, since the amount of data to be processed substantially diminishes. Yet another classification to discriminate between the shape descriptors is whether they are local or global. Local techniques, which
usually resort to higher-order derivatives, are very much affected by the presence of noise [1]. Global techniques, on the other hand, co
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