Il Dong Yun

Color image segmentation based on 3-D clustering: morphological approach

In this paper, a new segmentation algorithm for color images based on mathematical morphology is presented. Color image segmentation is essentially a clustering process in 3-D color space, but the characteristics of clusters vary severely, according to the type of images and color coordinates. Hence, the methodology employs the scheme of thresholding the difference of Gaussian smoothed 3-D histogram to get the initial seeds for clustering, and then uses a closing operation and adaptive dilation to extract the number of clusters and their representative values, and to include the suppressed bins during Gaussian smoothing, without a priori knowledge on the image. This procedure also implicitly takes into account the statistical properties, such as the shape, connectivity and distribution of clusters. Intensive computer simulation has been performed and the results are discussed in this paper. The results of the simulation show that the proposed segmentation algorithm is independent of the choice of color coordinates, the shape of clusters, and the type of images. The segmentation results using the k-means technique are also presented for comparison purposes.

Registration of multiple-range views using the reverse-calibration technique

Abstract In this paper we propose a new registration algorithm. The proposed algorithm consists of two steps. The first step is to estimate the transformation parameters among multiple range views, making use of the eigenvectors of the weighted covariance matrix of the 3-D coordinates of data points. The weighting factors are carefully selected to take into account the projection effect caused by different viewpoints. The next step is to register the views iteratively with the estimated transformation parameters as initial values. To solve the correspondence problem, the reverse calibration technique is used, which is adapted to the space-encoding range finder. The object function, defined by means of the reverse calibration technique, is minimized iteratively. Experimental results show that the proposed algorithm is very fast and efficient.

Color image retrieval using hybrid graph representation

Abstract In this paper, a robust color image retrieval algorithm is proposed based on the hybrid graph representation, i.e., a dual graph which consists of the Modified Color Adjacency Graph (MCAG) and Spatial Variance Graph (SVG). The MCAG, which is similar to the Color Adjacency Graph (CAG) [6] , is proposed to enhance the indexing ability and the database capacity, by increasing the feature dimension. In addition, the SVG is introduced, in order to utilize the geometric statistics of the chromatic segment in the spatial domain. In the matching process, we expand the histogram intersection [2] into the graph intersection, in which graph matching is performed using simple matrix operations. Intensive discussions and experimental results are provided to evaluate the performance of the proposed algorithm. Experiments are carried out on the Swains test images and the Virage images, demonstrating that the proposed algorithm yields high retrieval performance with tolerable computational complexity. It is also shown that the proposed algorithm works well, even if the query image is corrupted. e.g., a large part of pixels is missing.

Fast multiscale vessel enhancement filtering

This paper describes a fast multi-scale vessel enhancement filter in 3D medical images. For efficient review of the vascular information, clinicians need rendering the 3D vascular information as a 2D image. Generally, the maximum intensity projection (MIP) is a useful and widely used technique for producing a 2D image from the 3D vascular data. However, the MIP algorithm reduces the conspicuousness for small and faint vessels owing to the overlap of non-vascular structures. To overcome this invisibility, researchers have examined the multi-scale vessel enhancement filter based on a combination of the eigenvalues of the 3D Hessian matrix. This multi-scale vessel enhancement filter produces higher contrast. However, it is time-consuming and requires high cost computation due to large volume of data and complex 3D convolution. For fast vessel enhancement, we propose a novel multi-scale vessel enhancement filter using 3D integral images and 3D approximated Gaussian kernel. This approximated kernel looks like cube but it is not exact cube. Each layer of kernel is approximated 2D Gaussian second order derivative by dividing it into three rectangular regions whose sum is integer. 3D approximated kernel is a pile of these 2D box kernels which are normalized by Frobenius norm. Its size fits to vessel width in order to achieve better visualization of the small vessel. Proposed method is approximately five times faster and produces comparable results with previous multi-scale vessel enhancement filter.

Random tree walk toward instantaneous 3D human pose estimation

The availability of accurate depth cameras have made real-time human pose estimation possible; however, there are still demands for faster algorithms on low power processors. This paper introduces 1000 frames per second pose estimation method on a single core CPU. A large computation gain is achieved by random walk sub-sampling. Instead of training trees for pixel-wise classification, a regression tree is trained to estimate the probability distribution to the direction toward the particular joint, relative to the current position. At test time, the direction for the random walk is randomly chosen from a set of representative directions. The new position is found by a constant step toward the direction, and the distribution for next direction is found at the new position. The continual random walk through 3D space will eventually produce an expectation of step positions, which we estimate as the joint position. A regression tree is built separately for each joint. The number of random walk steps can be assigned for each joint so that the computation time is consistent regardless of the size of body segmentation. The experiments show that even with large computation gain, the accuracy is higher or comparable to the state-of-the-art pose estimation methods.

A new shape decomposition scheme for graph-based representation

Nowadays, the part-based representation of a given shape plays a significant role in shape-related applications, such as those involving content-based retrieval, object recognition, and so on. In this paper, to represent both 2-D and 3-D shapes as a relational structure, i.e. a graph, a new shape decomposition scheme, which recursively performs constrained morphological decomposition (CMD), is proposed. The CMD method adopts the use of the opening operation with the ball-shaped structuring element, and weighted convexity to select the optimal decomposition. For the sake of providing a compact representation, the merging criterion is applied using the weighted convexity difference. Therefore, the proposed scheme uses the split-and-merge approach. Finally, we present experimental results for various, modified 2-D shapes, as well as 3-D shapes represented by triangular meshes. Based on the experimental results, it is believed that the decomposition of a given shape coincides with that based on human insight for both 2-D and 3-D shapes, and also provides robustness to scaling, rotation, noise, shape deformation, and occlusion.

On the regular structure for the fast 2-D DCT algorithm

N. I. Cho and S. U. Lee (1991) proposed a fast algorithm for 2-D N*N DCT, where N=2/sup m/. It requires only half the number of multiplications of the conventional row-column approach. However, the signal flow graph for the postaddition stage seems very complicated and the order of the output index is seemingly irregular, because the postaddition stage was not based on the mathematical expressions. Consequently, derivation of the signal flow graph becomes complicated as the transform size increases. Systematic expressions for the postaddition stage of the algorithm that enable any N*N DCT to be implemented in a straightforward manner are provided here. The results show that the signal flow graph from input to output has a recursive structure in which the structure for smaller N reappears for larger N. However, the number of additions increases in the new signal flow graph at the expense of improving the regularity in the structure. >

Robust segmentation of cerebral arterial segments by a sequential Monte Carlo method: Particle filtering

In this paper a method to extract cerebral arterial segments from CT angiography (CTA) is proposed. The segmentation of cerebral arteries in CTA is a challenging task mainly due to bone contact and vein contamination. The proposed method considers a vessel segment as an ellipse travelling in three-dimensional (3D) space and segments it out by tracking the ellipse in spatial sequence. A particle filter is employed as the main framework for tracking and is equipped with adaptive properties to both bone contact and vein contamination. The proposed tracking method is evaluated by the experiments on both synthetic and actual data. A variety of vessels were synthesized to assess the sensitivity to the axis curvature change, obscure boundaries, and noise. The experimental results showed that the proposed method is also insensitive to parameter settings and requires less user intervention than the conventional vessel tracking methods, which proves its improved robustness.

Nonrigid Image Registration Using Dynamic Higher-Order MRF Model

In this paper, we propose a nonrigid registration method using the Markov Random Field (MRF) model with a higher-order spatial prior. The registration is designed as finding a set of discrete displacement vectors on a deformable mesh, using the energy model defined by label sets relating to these vectors. This work provides two main ideas to improve the reliability and accuracy of the registration. First, we propose a new energy model which adopts a higher-order spatial prior for the smoothness cost. This model improves limitations of pairwise spatial priors which cannot fully incorporate the natural smoothness of deformations. Next we introduce a dynamicenergy model to generate optimal displacements. This model works iteratively with optimal data cost while the spatial prior preserve the smoothness cost of previous iteration. For optimization, we convert the proposed model to pairwise MRF model to apply the tree-reweighted message passing (TRW). Concerning the complexity, we apply the decomposedscheme to reduce the label dimension of the proposed model and incorporate the linear constrained node (LCN) technique for efficient message passings. In experiments, we demonstrate the competitive performance of the proposed model compared with previous models, presenting both quantitative and qualitative analysis.

Constructing NURBS surface model from scattered and unorganized range data

We propose an algorithm to produce a 3D surface model from a set of range data, based on the Non-Uniform Rational B-Splines (NURBS) surface fitting technique. It is assumed that the range data is initially unorganized and scattered 3D points, while their connectivity is also unknown. The proposed algorithm is roughly made up of two stages: initial model approximation employing K-means clustering, and construction of NURBS patch network using hierarchical graph representation. The initial model is approximated by both a polyhedral and triangular model. Then, the initial model is represented by a hierarchical graph, which is efficiently used to construct the G/sup 1/ continuous NURBS patch network of the whole object. Experiments are carried out on synthetic and real range data to evaluate the performance of the proposed algorithm. It is shown that the initial model, as well as the NURBS patch network, are constructed automatically, while the modeling error is observed to be negligible.