Mr. Andrew W. Haddad Profile

Andrew W. Haddad

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Proceedings Article | 10 February 2012 Paper

Detection of symmetric shapes on a mobile device with applications to automatic sign interpretation

Andrew Haddad, Shanshan Huange, Mireille Boutin, Edward Delp

Proceedings Volume 8304, 83040G (2012) https://doi.org/10.1117/12.908815

KEYWORDS: Image segmentation, Radon transform, Mobile devices, Databases, Cameras, RGB color model, Image processing, Diamond, Zoom lenses, Digital cameras

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We present a light-weight method for automatically detecting shapes that have an approximate rotational symmetry (e.g., a square or equilateral triangle) on discrete-space images. Our motivation is the problem of automatically detecting and recognizing hazardous material placards on a mobile platform (e.g., a mobile telephone) equipped with a camera. The proposed method is well-suited for mobile device applications, which are characterized by limited memory, processing power and battery life. It is based on comparing the magnitude of the coefficients of the Fourier series of the centralized moments of the Radon transform of the image after segmentation. However, in our approach, the computation of the Radon transform is bypassed as we obtain these coefficients directly from the rows of the Pascal Triangle of the segmented image. The Pascal Triangle of an image is composed of complex moments arranged in a pyramidal fashion similar to the binomial coefficients. These complex moments are obtained from a coarse segmentation of the shape represented by a gray-scale image. In particular, the contours of the object do not need to be precisely defined, and the shape needs not be connected. Moreover, our approach is invariant under translation, rotation, and scaling. We tested our method on images from the MPEG-7 shape database as well as images from our own database of hazardous material placards.

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