Centroid

About: Centroid is a research topic. Over the lifetime, 4110 publications have been published within this topic receiving 53637 citations. The topic is also known as: barycenter (geometry) & geometric center of a plane figure.

Estimation of Skew Angle in Binary Document Images Using Hough Transform

Abstract: This paper includes two novel techniques for skew estimation of binary document images. These algorithms are based on connected component analysis and Hough transform. Both these methods focus on reducing the amount of input data provided to Hough transform. In the first method, referred as word centroid approach, the centroids of selected words are used for skew detection. In the second method, referred as dilate & thin approach, the selected characters are blocked and dilated to get word blocks and later thinning is applied. The final image fed to Hough transform has the thinned coordinates of word blocks in the image. The methods have been successful in reducing the computational complexity of Hough transform based skew estimation algorithms. Promising experimental results are also provided to prove the effectiveness of the proposed methods.

Maximum a posteriori estimation of wave-front slopes using a Shack–Hartmann wave-front sensor

Abstract: Current methods for estimating the wave-front slope at the pupil of a telescope by using a Shack–Hartmann wave-front sensor (SH–WFS) are based on a simple centroid calculation of the irradiance distributions (spots) recorded in each subaperture. The centroid calculation does not utilize knowledge concerning the correlation properties of the slopes over the subapertures or the amount of light collected by the SH–WFS. We present the derivation of a maximum a posteriori (MAP) estimation of the irradiance centroids by incorporating statistical knowledge of the wave-front tilts. Information concerning the light level in each subaperture and the relative spot size is also employed by the estimator. The MAP centroid estimator is found to be unbiased, and the mean square error performance is upper bounded by that exhibited by the classical centroid technique. This error performance is demonstrated by using Kolmogorov wave-front slope statistics for various light levels.

Harmonic cut and regularized centroid transform for localization of subcellular structures

Abstract: Two novel computational techniques, harmonic cut and regularized centroid transform, are developed for segmentation of cells and their corresponding substructures observed with an epi-fluorescence microscope. Harmonic cut detects small regions that correspond to subcellular structures. These regions also affect the accuracy of the overall segmentation. They are detected, removed, and interpolated to ensure continuity within each region. We show that interpolation within each region (subcellular compartment) is equivalent to solving the Laplace equation on a multi-connected domain with irregular boundaries. The second technique, referred to as the regularized centroid transform, aims to separate touching compartments. This is achieved by adopting a quadratic model for the shape of the object and relaxing it for final segmentation.