Abstract
There have recently been advances in the area of fully automatic detection of clustered objects in color images. State of the art methods combine detection with segmentation. In this paper we show that these methods can be significantly improved by introducing a new iterative classification, statistical modeling, and segmentation procedure. The proposed method used a detect-and-merge algorithm, which iteratively finds and validates new objects and subsequently updates the statistical model, while converging in very few iterations.
Our new method does not require any a priori information or user input and works fully automatically on desktop computers and mobile devices, such as smartphones and tablets. We evaluate three different kinds of classifiers, which are used to substantially reduce the number of false positive matches, from which current state of the art methods suffer. Experiments are performed on a challenging database depicting wood log piles, with objects of inhomogeneous sizes and shapes. In all cases our method outperforms the current state of the art algorithms with a detection rate above 99 % and a false positive rate of less than 0.4 %.
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Herbon, C., Tönnies, K., Stock, B. (2014). Detection and Segmentation of Clustered Objects by Using Iterative Classification, Segmentation, and Gaussian Mixture Models and Application to Wood Log Detection. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham. https://doi.org/10.1007/978-3-319-11752-2_28
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DOI: https://doi.org/10.1007/978-3-319-11752-2_28
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