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Segmentation of white blood cells (i.e. leukocytes) is a crucial step toward the development of haematological images analysis of peripheral blood smears. This step, however, is complicated by the complex nature of the different types of white blood cells and their large variations in shape, texture, color, and density. This study addresses this issue and presents a single fully automatic segmentation...
Impervious surface area (ISA) as the indicator of urbanization has a significance for urban ecological environment evaluation. Impervious surface extracting methods based on high-resolution remote sensing imagery can extract ISA in a fine-scale. However, a series of consequent problems cannot be ignored, such as shadows from tall building and canopies. In order to solve the shadow problem, high-resolution...
Accurate cell segmentation is an important and long-standing challenge in biomedical image analysis due to large variations in shape and boundary ambiguity. In this paper, we present a segmentation framework for partially overlapping cervical cells. The proposed method starts by cellular clump estimation with morphological reconstruction. Subsequently, the nuclei inside the cellular clumps are located...
Developing segmentation techniques for overlapping cells has become a major hurdle for automated analysis of cervical cells. In this paper, an automated three-stage segmentation approach to segment the nucleus and cytoplasm of each overlapping cell is described. First, superpixel clustering is conducted to segment the image into small coherent clusters that are used to generate a refined superpixel...
License plate recognition (LPR) is an important part of the vehicle detection system, which plays a significant role in traffic management and has a variety of applications. This paper presents a license plate recognition method based on pulse coupled neural network (PCNN) and template matching. One PCNN is implemented to segment the gray image containing the License plate, and another PCNN is applied...
Microscopic cellular image segmentation has become one of the most important routine procedures in modern biological applications. The segmentation task is non-trivial, however, mainly due to imaging artifacts causing highly inhomogeneous appearances of cell nuclei and background with large intensity variations within and across images. Such inconsistent appearance profiles would cause feature overlapping...
In this paper, we propose a novel method for object localization, generally applicable to medical images in which the objects can be distinguished from the background mainly based on feature differences. We design a new CRF model with additional contrast and interest-region potentials, which encode the higher-order contextual information between regions, on the global and structural levels. We also...
In this paper, we present an approach based on information fusion for road networks extraction from high-resolution multi-spectral satellite image data that builds upon pixel-based fuzzy logic segmentation approach. This road networks extraction system includes three different modules: geometrical features processing based on local context; segmentation based on fuzzy classification; angular texture...
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