Non-rigid point set registration is the process of transforming a shape represented as into matching another shape. In this paper, we propose an acceleration method for solving non-rigid problems. We accelerate by dividing it three steps: i) downsampling sets; ii) downsampled and iii) interpolation deformation vectors corresponding to points removed during downsampling. To register sets, use al...

in this work, we report synthesis of silver nanoplates by a simple reduction process of silver nitrate in the presence of polyvinyl alcohol (pva) and n,n'-dimethyl formamide (dmf). the characterization of the samples were carried out using x-ray diffraction (xrd), transmission-electron microscopy (tem) and uv-vis spectroscopy. absorption spectra of the nanoplates in comparison with that of...

Accurate medical diagnosis requires the segmentation of a large number of medical images. Although manual segmentation provides good results, it is a costly process in terms of both money and time. Automatic segmentation, on the other hand, remains a challenge due to low image contrast and ill-defined boundaries. In this report, we propose a fully automated medical image segmentation framework ...

Robust localization and segmentation of normal anatomical objects in medical images require (1) methods for creating descriptive object models that adequately capture object shape and expected shape variation across a population, (2) methods for combining such shape models with unclassified image data, and (3) means for localizing and extracting corresponding objects from the image data using t...

For object extraction, the target object in images often cannot be extracted completely and accurately using only low-level image features, especially from cluttered, occluded and noisy images. In practice, the shape of the target object is often known in advance, and edges can be extracted directly from image, which can contribute to the object extraction task. The authors introduce shape prio...

Accurate medical diagnosis requires a segmentation of a large number of medical images. Although the manual segmentation produces good results, it is a costly process (in the terms of money and time). On the other hand, the automatic segmentation is still challenging because of low image contrast and ill-defined boundaries. In this work, we propose a fully automated medical image segmentation f...

Robust localization and segmentation of normal anatomical objects in medical images require (1) methods for creating descriptive object models that adequately capture object shape and expected shape variation across a population, (2) methods for combining such shape models with unclassified image data, and (3) means for localizing and extracting corresponding objects from the image data using t...

We propose a technique for localization of complex shapes in images using a novel part–based deformable shape representation based on finite element vibration modes. Here, our method gives an extension for Finite Element Models to represent elastic co–variations of discrete variable shapes. It avoids misregistration by resolving several drawbacks inherent to standard shape–based approaches, whi...

In this paper, we propose a texture-constrained active shape model (TC-ASM) to localize a face in an image. TC-ASM effectively incorporates not only the shape prior and local appearance around each landmark, but also the global texture constraint over the shape. Therefore, it performs stable to initialization, accurate in shape localization and robust to illumination variation, with low computa...

In this paper, we propose a texture-constrained active shape model (TC-ASM) to localize a face in an image. TC-ASM effectively incorporates not only the shape prior and local appearance around each landmark, but also the global texture constraint over the shape. Therefore, it performs stable to initialization, accurate in shape localization and robust to illumination variation, with low computa...