Recognition of overlapping objects is required in many applications in the field of computer vision. Examples include cell segmentation, bubble detection and bloodstain pattern analysis. This paper presents a method to identify overlapping objects by approximating them with ellipses. The method is intended to be applied to complex-shaped regions which are believed to be composed of one or more overlapping objects. The method has two primary steps. First, a pool of candidate ellipses are generated by applying the Euclidean distance transform on a compressed image and the pool is filtered by an overlaying method. Second, the concave points on the contour of the region of interest are extracted by polygon approximation to divide the contour into segments. Then, the optimal ellipses are selected from among the candidates by choosing a minimal subset that best fits the identified segments. We propose the use of the adjusted Rand index, commonly applied in clustering, to compare the fitting result with ground truth. Through a set of computational and optimization efficiencies, we are able to apply our approach in complex images comprised of a number of overlapped regions. Experimental results on a synthetic data set, two types of cell images and bloodstain patterns show superior accuracy and flexibility of our method in ellipse recognition, relative to other methods.
Volume visualization has been widely used to simulate and observe complex data in the fields of science, engineering, biomedicine, etc. One central topic of volume visualization is the transfer function (TF) of volume rendering. By setting TF, users design the mapping of voxels to optical properties of 3D datasets. However, the design of TF is usually a blind process. How to classify all volume data accurately and design a suitable TF fleetly is the key to improve the efficiency of volume rendering. In this paper, we propose a new TF design approach based on extreme gradient boosting (XGBoost) algorithm for fast visualization. First, the features are extracted from 3D volume data. Then we use the XGBoost model to classify the volume data and design TF. Finally, we assign the optical properties to the voxels to express and reveal the relevant features of dataset. This approach can help users to render the volume data efficiently. It has been tested and achieved satisfactory result.
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