Paper
5 December 2001 Automated transformation-invariant shape recognition through wavelet multiresolution
Patrice Brault, Hugues Mounier
Author Affiliations +
Abstract
We present here new results in Wavelet Multi-Resolution Analysis (W-MRA) applied to shape recognition in automatic vehicle driving applications. Different types of shapes have to be recognized in this framework. They pertain to most of the objects entering the sensors field of a car. These objects can be road signs, lane separation lines, moving or static obstacles, other automotive vehicles, or visual beacons. The recognition process must be invariant to global, affine or not, transformations which are : rotation, translation and scaling. It also has to be invariant to more local, elastic, deformations like the perspective (in particular with wide angle camera lenses), and also like deformations due to environmental conditions (weather : rain, mist, light reverberation) or optical and electrical signal noises. To demonstrate our method, an initial shape, with a known contour, is compared to the same contour altered by rotation, translation, scaling and perspective. The curvature computed for each contour point is used as a main criterion in the shape matching process. The original part of this work is to use wavelet descriptors, generated with a fast orthonormal W-MRA, rather than Fourier descriptors, in order to provide a multi-resolution description of the contour to be analyzed. In such way, the intrinsic spatial localization property of wavelet descriptors can be used and the recognition process can be speeded up. The most important part of this work is to demonstrate the potential performance of Wavelet-MRA in this application of shape recognition.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Patrice Brault and Hugues Mounier "Automated transformation-invariant shape recognition through wavelet multiresolution", Proc. SPIE 4478, Wavelets: Applications in Signal and Image Processing IX, (5 December 2001); https://doi.org/10.1117/12.449735
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Cited by 5 scholarly publications.
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KEYWORDS
Wavelets

Image processing

Image analysis

Computer programming

Pattern recognition

Image segmentation

Sensors

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