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Recognition of Objects Represented in Different Color Spaces

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In this article we present a statistical framework for automatic classification and localization of 3D objects in 2D images. The new functionality of the framework allows us to use objects represented in different color spaces including gray level, RGB, and Lab formats. First, the objects are preprocessed and described by local wavelet features. Second, statistical modeling of these features under the assumption of their normal distribution is performed in a supervised way. The resulting probability density functions are determined by the maximum likelihood estimation. The density functions describe a particular object class from a particular training viewpoint. In the recognition phase, local feature vectors are computed from an image with an unknown object in an unknown pose. Those features are then evaluated against the trained density functions which yields the classes and the poses of objects found in the scene. Experiments performed for more than 40.000 images with real heterogeneous backgrounds have delivered very good classification and localization rates for all investigated object representations. Moreover, they brought us to interesting conclusions considering the general performance of statistical recognition systems for different image representations.
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Document Type: Research Article

Publication date: January 1, 2010

More about this publication?
  • Started in 2002 and merged with the Color and Imaging Conference (CIC) in 2014, CGIV covered a wide range of topics related to colour and visual information, including color science, computational color, color in computer graphics, color reproduction, volor vision/psychophysics, color image quality, color image processing, and multispectral color science. Drawing papers from researchers, scientists, and engineers worldwide, DGIV offered attendees a unique experience to share with colleagues in industry and academic, and on national and international standards committees. Held every year in Europe, DGIV papers were more academic in their focus and had high student participation rates.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual papers for details.

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